Claude And GPT-6 Release As AI Math Breaks Barriers

• Opus 5.5's API pricing drops to $4 per million input tokens and $20 per million output tokens, with cache reads reduced to $0.20 per million—a 60% decrease for agentic workloads.
• On Anthropic's automated behavioral audit, the model circumvented safety boundaries 85% less often than its predecessor, with every boundary-crossing attempt being low severity and self-reported.
• The model launches with a "preserved thinking" anti-distillation safeguard that blocks API users from editing prior context to extract reasoning, applying to accounts created on or after August 31, 2026.
• External benchmark results show Opus 5.5 achieving 66.4% on Terminal-Bench 4.0 and 1846 Elo on GDPval-AA v2.1, while matching GPT-6 Astra's performance at roughly 40% of the cost per task.
• Early testing demonstrated dramatic efficiency gains, including a 680,000-line code migration completed in under a day and a complex financial modeling task finishing in 63 minutes for half the token cost of Opus 5.

Claude Opus 5.5 is our first release since we called for pacing the frontier.

Interesting how the very first line is used to remind the reader of their call to pace the frontier just last week, and everything else after that line is to demonstrate with very specific numbers how they absolutely are not pacing.

— sailingparrot (32 replies)

After yesterday outage is the new Opus 5.5 load-bearing?

— throwaway2027 (16 replies)

Because Opus 5.5 is comparable to Claude Mythos 5.1 in biology and cybersecurity, we're deploying it with safeguards similar to those on Claude Fable 5.1. Vetted organizations can apply today to our Life Sciences Verification Program to use Opus 5.5 for biology research. In the coming weeks we will also be expanding access to our Cyber Verification Program, and verified cybersecurity practitioners will be able to use Opus 5.5 for their work.

Ah, they're spreading their limits to all their models it seems. Definitely not a good thing long term in my opinion.

— ApolloFortyNine (15 replies)

• GPT-6 Sol is priced at $2/M input and $10/M output, exactly half the price of GPT-5.6 Sol.
• GPT-6 Luna costs $0.10/M input and $0.50/M output, representing a 50% price cut from GPT-5.6 Luna.
• The models are positioned as successors to the existing Sol and Luna families, continuing OpenAI's tiered approach to model accessibility.
• Pricing changes suggest OpenAI is prioritizing volume and adoption over per-token margins in the competitive frontier model market.

GPT-6 Luna being half the price of GPT-5.6 Luna is a really big deal.

Here's GPT-6 Luna pelicans: https://tools.simonwillison.net/markdown-svg-renderer?url=https%3A%2F%2Fgist.github.com%2Fsimonw%2F40d129fc140faca378b9c9f4f16c6ec2

And GPT-6 Sol: https://tools.simonwillison.net/markdown-svg-renderer?url=https%3A%2F%2Fgist.github.com%2Fsimonw%2Fbe7ae25af2634b68bc34b7b7aaf02cb2

Scroll to the bottom for the GPT-6 Sol max one: https://tools.simonwillison.net/markdown-svg-renderer?url=https%3A%2F%2Fgist.github.com%2Fsimonw%2Fbe7ae25af2634b68bc34b7b7aaf02cb2#response-5

For comparison, here are the pelicans I got for GPT-6 Astra: https://tools.simonwillison.net/markdown-svg-renderer?url=https%3A%2F%2Fgist.github.com%2Fsimonw%2Ff789d2784fc6c5b870cc80f0b7cd9d01 - I still like the Astra Max one best.

Here's a comparison grid showing all of the GPT-6 and GPT-5.6 pelicans at all effort levels: https://static.simonwillison.net/static/2026/gpt-6-and-5.6.html

The grid is actually really interesting, because it shows that the 5.6 family default to brighter colors than the 6 family.

— simonw (34 replies)

6-luna is at the pareto for most of the tasks! I dont know how they make money here but its insane value from a closed source model. I'd go further and say it makes no sense (privacy, sovereignty etc aside) to use many other models as its not only expensive but also many providers don't have that much GPUs to serve at a significant volume. https://openrouter.ai/rankings?view=month#top-models 5.6 luna is already the most used model this month.

— gizmodo59 (11 replies)

I don't see how anyone can be using Claude with prices like this, it's pretty incredible what the OpenAI team is doing, w.r.t model quality and pricing.

Prices per 1M tokens Claude Opus 5.5 Claude Opus 5 Cache reads $0.20 $0.50 Input tokens $4 $5 Output tokens $20 $25 Cache writes $5 $6.25

Model Input Output Price reduction GPT‑6 Sol vs. GPT‑5.6 Sol $4 → $2 $20 → $10 50% cheaper GPT‑6 Luna vs. GPT‑5.6 Luna $0.20 → $0.10 $1.20 → $0.50 50% cheaper

— pookieinc (22 replies)

• A survey by Cynthia Dunlop found that 78% of readers will stop reading and 71% will avoid an author in the future if they suspect an article is AI-assisted or AI-authored, with 98% strongly preferring the author's original writing.
• When used as a verification tool, AI successfully identified a subtle level notation error (L1 vs L2) in a technical paper that four human expert reviewers had all missed.
• The quality of current LLM writing has plateaued because, unlike programming or math, writing lacks a clear formulation and verifiable output, making it difficult to optimize beyond statistical next-word prediction.
• Breck personally used AI to complete BibTeX citations and generate TikZ technical diagrams, which sped up workflow without compromising the paper's substantive content or voice.
• Tools like ASD-STE100 (a controlled dictionary for technical English) and Pangram (an AI-detection model) are being explored to enforce writing standards and detect AI-generated text that violates reader-writer trust.

As I have been saying for years:

Writing is fundamentally the transfer of information from your brain to my brain. If you have 1000 bits of semantic information you want to transfer, you can't give 300 bits of semantic information to an LLM and have it fill in the remaining 700, because it doesn't know what those 700 bits are. If it's able to guess those 700 bits correctly, then they aren't true semantic information, and you really only have 300 bits you want to transfer. You might as well transfer those bits to me directly, rather than having the LLM add on an extra superfluous 700 bits that I then have to filter out.

— hatthew (45 replies)

For a long time after the internet arrived on the scene, a lot of online news stories would reference websites, papers, polls, etc. without linking to them. There are still news sources doing this today. Sometimes such articles interpret or place context around their hidden references, but a lot of the time they just summarize.

Giving someone the text output of a LLM is very similar to publishing a summary without links to the referenced material. When you were querying your LLM, you could have asked specific questions or asked for a custom focus or point of view. Your intended audience might have questions or different concerns, but they're unable to interact with your LLM. What you have delivered is static and unresponsive. It has all the disadvantages of being machine output without the advantage of being interactive, the way your LLM was for you.

It may have to wait until compute is cheap enough that tokens are essentially free, but we need a system to pass "hyperlinks" to LLM's primed with context, ready to be interactively queried on a chosen context. It's being overly generous to assume that people are putting even 300 bits into a LLM for every 1000 bits of regurgitated writing they try to pass off as their own. When people post LLM output as if it were their own, I have no choice but to assume they had zero knowledge of the subject, but this query taught them what they wanted to learn, and now they're sharing that. That's fine, but please pass an interactive LLM link rather than static text.

Once we have "hyperlinks" for LLM sessions, perhaps we can share LLM output a little more usefully and honestly.

— beloch (7 replies)

If you have 1000 bits of semantic information that you want to transfer but your default communication combines it with 10,000 bits of noise. Giving it all to an LLM and iterate on reducing that noise while making sure the 1000 bits is still present would enable you to communicate more effectively.

Overall, ideas are ideas. I'm not overly concerned with the fact that it was you who had the idea, as long as the idea is interesting. I don't know most of the people who write the things I read, so it seems to be of no consequence to me at all if they wrote it, as long as it is interesting. LLMs are notorious at creating things that are bland and vacuous, but they by no means have a monopoly on it.

Be the source human, machine, or dolphin, if they write a good article, I'm prepared to read it.

— Lerc (1 reply)

• In February 2025, a macOS 15.3 feature was identified that transmitted personal data to Apple servers at 15-minute intervals.
• The AI system now occupies 22.28 GB of local storage, which calculates to approximately £11 based on Apple's stated £500/TB hardware pricing.
• The dedicated AI toggle was removed, forcing users to rely on hidden Screen Time restrictions originally designed for parental controls to hide interface elements.
• Multiple unkillable Siri background processes continue to run and consume memory even after the user explicitly turns off Apple Intelligence & Siri.

Not trying to start flamewars, but more and more I wonder how much are people willing to put up with such practices. On linux for 15+ years and everytime I try to use mac or win, it is an ordeal. And ads. F*cking ads in a system someone has purchased. And spying. Really user hostile environment. No, thank you.

— vitro (33 replies)

Because Mac laptops are lightyears ahead of any other hardware.

Build quality (aluminum), charging (magsafe), screen resolution, battery life, noise (or lack thereof), trackpad…

It's like other vendors aren't even trying.

Also the OS mostly “just works”, just last week my Linux laptop disabled NVIDIA GPU (and almost bricked itself?? Not sure, had to fix apt) during automated updates

— tomp (39 replies)

It's the standard enshittification playbook. Get a large market share with a good product, then you can safely degrade it since your users are too reliant on the ecosystem to conveniently switch.

— sortoflog (0 replies)

• Google's SynthID-O variant can embed a 136-bit payload in a 512x512-pixel image, dedicating 64 bits to a database identifier and 72 bits to error correction.
• Text-based spymarks operate by algorithmically steering word choices to generate detectable statistical patterns that encode tracking payloads without altering readability.
• Open-source audio spymarking tools like audiowmark, dating back to 2018, can hide 128-bit payloads protected by secret AES keys, making them indecipherable without the proper credential.
• Unlike standardized EXIF or ID3 metadata that users can inspect and strip, spymark signals survive file edits and metadata removal while remaining completely invisible to the creator.
• The concept of hidden printer tracking dots dates back to the 1980s, predating modern AI by decades and serving as an early hardware-based example of this tracking methodology.

I'm not convinced spymark is better than just "invisible watermarks", spymark to my ears sounds designed to be sound very negative when invisible watermarks are not always negative, e.g. the counterfeit bank note example.

Tech like SynthID I see a net positive especially since it doesn't degrade text quality. I dream about a browser extension running at all times that makes text more translucent based on the confidence of LLM writing[0].

This article's suggestion of using it to unmask whistleblowers is very interesting and not something I'd thought about though. Still not convinced that spymark is a better name though.

[0]: Sean Goedecke's Deckard is close but it would rather invisible than bright red

— pavo-etc (thread)

The article's use of the term "spymark" is a bit much. The technique described is essentially steganography, which isn't typically to add an identification, but something like "steganomark" would seem to be fitting.

— latexr (thread)

something like "steganomark" would seem to be fitting.

Might be technically more accurate, but it's an inscrutable name with no chance of proliferation beyond technical people. If the goal is to rally people to your position, you need a name people can identify.

— latexr (thread)

I'd like to know how to type it with voice dictation.

— benatkin (thread)

• GPT-2 (small) utilizes a vocabulary of 50,257 unique tokens, with each token mapped to a 768-dimensional vector in an embedding matrix containing approximately 39 million parameters.
• The model architecture stacks 12 identical transformer blocks, where each block employs 12 parallel attention heads to capture diverse syntactic and semantic relationships simultaneously.
• Within the MLP layer, token representations are temporarily expanded four-fold from 768 to 3,072 dimensions to capture complex patterns before being compressed back to the original size.
• Masked self-attention prevents the model from peeking into future tokens during generation by applying a softmax operation to an upper-triangle-masked attention score matrix.
• The tool is implemented using a PyTorch-derived nanoGPT model converted to ONNX Runtime, enabling it to execute live in the browser alongside D3.js visualizations and a Svelte frontend.

Nicely done. For me the most fascinating thing about attention heads is the place where Attention matrix is already computed and is getting multiplied by Value vector. It behaves exactly like pushing Value vector through Dense layer of ordinary network where Attention matrix forms weights of that layer. So attention head is trained to construct this small single layer network dynamically during inference from Key and Query. And that's the point. That's rarely underlined in explanations of LLMs architecture and for me it's quite amazing that it works so well.

— andblac (thread)

It isn't so much that 'other setups didn't work'. More like 'the first thing we found that did work turned out to be the minimal thing that could work'. Transformers are conventional feed-forward neural networks alternated with attention blocks. You can think of them as big huge 'ordinary' neural networks augmented with this new kind of block. Attention blocks are basically just a differentiable hashmap. Think of it like a scratchpad memory. It turns out that a hashmap/scratchpad is pretty essential to being able to untangle language.

— octoberfranklin (thread)

Transformers upscale the dimension of the data. Instead of the above, transformers do [output] = [input] * [weights_matrix]. When you multiply an input by a matrix, you get an output matrix back. Thats all that happens. Nothing fancy. You have weights matricies for K/Q/V, which when post multiplied with the input, give you the KQV vectors, and then you just simply multiply them together and apply a scaling factor. There is nothing magical about K/Q/V. The reason why RNNs and others didn't work is because RNN training is effectively trying to linearly regress on chaotic effects - i.e what set of starting conditions would evolve with a given process into what you want. This is an NP hard problem.

— ActorNightly (thread)

It's definitely part of many intro explanations, but it sits in a weird position where you have to know MLPs well but not Transformers for this to be enlightening, which is a shrinking set of people nowadays. Either someone is a novice to all this, or know both, so beginner Transformer explanations can rarely assume deep familiarity with MLPs. But yes, a Transformer block can be thought of as basically input-dependently deciding the weight vector of a dense layer. In classical MLP-like networks there was never any multiplication between input-dependent values.

— bonoboTP (thread)

Damn that page took down my Chromebook, never happened before.. twice...

— jwpapi (thread)

• The Enigma key for MVUEH used wheel order 253, contrasting with the standard daily order of 512 used for other messages on July 10, 1941.
• The AI detected a transcription error in the original ciphertext where the German word "Bitte" was incorrectly enciphered as "Btte", reducing the message length by twelve letters compared to its counterpart.
• GPT-6 Astra autonomously generated Python and C++ code to build both an Enigma simulator and a Bombe machine to execute the cryptographic attack.
• A rare mechanical quirk where the Enigma's left-hand rotor turns over at the 72nd letter of the message likely prevented earlier human decryption attempts.

The correct title: Researcher brakes one specific stubborn historic enigma message with good help from Astra.

Stubborn for a long time because the message used a completely different key from the rest of that day's traffic. Everyone assumed it shared the daily key. The original transcription had errors. The left rotor turned over at letter 72, which is rare and breaks standard crib attacks.

What is cool, if true, is that it was a 2 day collab between the Leffer and Astra. To me this shows the importance of human in the loop, was still all also showing how immensely power of llm tools. But I think it's getting a bit silly how much anrticles ignores the driving force (the person) in breakthroughs like this.

— jtrn (14 replies)

Even the Millennium Prize Problems have, in a way, become benchmarks for model companies to prove themselves. The smartest individuals among humans are becoming replaceable. Intelligence has become a product you can quantify and buy with electricity. That feels awful.

— mgaldys4 (12 replies)

OpenAI solving all these math/etc problems (reportedly 100 coming) is both impressive and insanely unimpressive. Unimpressive because to me it sorta signals that OpenAI has nothing better to be working on than obscure mathematical curios?

— bluepeter (9 replies)

• The underlying DEFLATE algorithm uses a 32 KiB sliding window to find matches against recent text, encoding continuations that echo the window as cheap back-references.
• A naive byte-by-byte generation fails due to integer quantization noise, so the tool employs a beam search that looks ahead a full span of bytes before committing to any output.
• To prevent the generator from falling into verbatim loops of its own output, only the last tail bytes of the generated text are retained in the scoring context.
• The entire implementation requires just a single Python file using the standard library zlib module, with no external dependencies or prior training.
• The generated text demonstrates partial structural awareness, correctly mimicking character names and dialogue formats from Shakespeare, though it struggles with long-range coherence.

Yes: you can classify a test file by topic with gzip as follows: gzip -9 sports.txt testfile.txt... The test file belongs to the topic with the smallest size *.gz file.

— jll29 (thread)

I've been pondering on something related: can an LLM be a chat? Some models are reproducible, in that the same prompt will generate the same output. Say that we could wire up such a model to generate some code.

— berkes (thread)

I'm more interested in the converse question: how well does an LLM perform as a compressor, compared to gzip (ignoring its insanely lower speed)?

— tromp (thread)

• The intelligence indicating the school was no longer a military target never entered the target database, and the responsible vetting team was disbanded and not consulted.
• The White House reportedly demanded 1,000 targets and pulled them from a database without proper verification.
• Community discussion emphasized that AI is being used as a scapegoat for systemic human failures and bureaucratic negligence.

It found the U.S. “failed in its obligation to do everything feasible to verify” that the school was a military objective and that the failure “went beyond mere negligence.” The report said the United States “directed the strikes at the building of the school while being aware of a substantial risk of striking a civilian object and acting recklessly as regards the possibility that this would happen.”

Reading the details, "AI" doesn't really seem like the culprit -it's a scapegoat.

The intelligence that it was no longer a military target never entered the target database, the team that was responsible for vetting the target list was gutted, and said team was never even consulted.

The White House wanted 1000 targets and pulled from their database without any due diligence. Whether it was an AI call or an SQL query - this was from pure human maliciousness and incompetence.

— legitster (8 replies)

The pentagon says it was Palantir software's fault, Palantir says it was the fault of bad data input to their software. Where's the accountability by both organizations? How did we get to a world where military decisions leading to innocent deaths are responded to as if it was just another miscommunication implementing a B2B SaaS platform.

— banubis (6 replies)

It's easy to blame the military and the higher-ups, after all they made this choice. But more relevant to HN, this couldn't have happened without technology professionals working for Palantir.

A Palantir spokesperson told Bloomberg the company “is not responsible for the underlying data nor identifying intelligence deficiencies” and that there is no evidence its software was at fault.

If you work for Palantir, you are writing software that kills people. Software that, in this case, killed 123 children. What salary is worth that?

I encourage others who work in the public sector, whether directly or through a contractor, to read and internalize Don't Get Distracted by Caleb Hearth [0]. I think about it nearly every day.

[0] https://calebhearth.com/dont-get-distracted

— 63 (6 replies)

• Bad code exhibits a "butterfly effect" where modifying a single component breaks the program in non-deterministic ways or forces developers to patch multiple disparate locations, creating fragile tests that actively prevent refactoring.
• State-of-the-art LLMs frequently fail at code simplification by extracting functions into smaller units that lack reusability, forcing readers to constantly cross-reference child implementations to understand the parent logic.
• Most developers remain at the "advanced beginner" stage of the Dreyfus model, relying on rigid rulebooks that experts intentionally bypass to navigate context-dependent architectural decisions.
• AI training data primarily consists of existing real-world codebases, which are largely poorly architected, causing models to replicate industry-wide technical debt rather than correct it.

The problem imo is the slow deterioration of institutional knowledge that offloading the mental task of wisdom gathering to AI is causing. One interesting comparison is to the history of manufacturing.

— NalNezumi (thread)

There's a continuum between "vibe coded by someone with no technical knowledge or inclination" and "hand written domain driven design development". You can absolutely use coding agents AND have maintainable code.

— davedx (thread)

In the beginning of August 2026, I started a hobby project: building a MongoDB-like database. I have 20 years of industry experience and a master's degree in computer science, so I followed a disciplined, spec-driven development model using Claude, Kiro, Qwen Coder, and Cursor.

— tegeek (thread)

• I asked Meta's Muse for its filesystem and it sent me 6.8GB (289 points · discussion) -- Security researcher Peter James demonstrated a significant data exposure vulnerability in Meta's AI agent, Muse, by simply asking it to archive its accessible files and export them to Google Drive.
• OpenAI is about to eat Jev's lunch – Arcturus Labs (253 points · discussion) -- TypeSafe's new AI product, Jev, rapidly gained traction by using standard large language models to output calibrated probability distributions for binary and multiple-choice questions.
• Claude Opus 5.5 Intelligence, Performance and Price Analysis (Max) (226 points · discussion) -- Artificial Analysis publishes a comprehensive evaluation of Anthropic's newly released Claude Opus 5.5, detailing its adaptive thinking strategy, token efficiency, and market pricing.
• MiMo-v2.6-Pro: Intelligence, Performance and Price Analysis (152 points · discussion) -- Xiaomi's MiMo-V2.6-Pro is a newly released open-weights reasoning model that ranks first among comparable large language models on the Artificial Analysis Intelligence Index.
• Claude Status – Elevated errors for multiple models (138 points · discussion) -- Anthropic's Claude service experienced a period of elevated request errors affecting several of its AI models on September 22, 2026.
• LLM Ass Bench (137 points · discussion) -- A new community benchmark evaluates LLM image-generation capabilities by asking models to create an SVG of a pelican riding a bicycle, a test that has become an unofficial standard for assessing visual reasoning and compositional accuracy.
• Unreal Agent (119 points · discussion) -- Unreal Labs has released Unreal Agent, an AI agent harness that cuts inference costs by up to 40% compared to OpenAI's Codex.
• Robin Williams' Daughter to Fans Creating AI Videos: 'Have Some Shame' (96 points · discussion) -- Zelda Williams has publicly condemned the continued creation and circulation of AI-generated videos featuring her late father, Robin Williams.
• Writing Rust code that's fast by asking agents to make the code faster (94 points · discussion) -- The author demonstrates that modern agentic LLMs can iteratively optimize Rust code to consistently outperform state-of-the-art libraries by 2x to 20x+ when guided through a structured, constraint-heavy prompting pipeline.
• Did OpenAI solve the wrong Navier-Stokes problem? (92 points · discussion) -- OpenAI recently claimed to solve the Clay Mathematics Institute’s $1-million Navier-Stokes problem using an AI-generated proof, but mathematicians argue it addresses a technically distinct variant.
• Stanford R&DE Uses AI to Race Swap Students for Advertising (81 points · discussion) -- Stanford Residential & Dining Enterprises recently deployed generative AI to alter student photographs used in campus advertising, specifically changing the races of individuals and slimming their facial features.
• People Training OpenAI's AI Fired for Using AI to Train the AI (71 points · discussion) -- OpenAI relies on a large network of contractors to review and rate ChatGPT responses, but multiple workers have been terminated for using generative AI to complete their tasks.
• JetBrains Air: A System of Products for Agentic Software Development (66 points · discussion) -- JetBrains is launching JetBrains Air, an open, multi-surface product ecosystem designed to coordinate, govern, and verify agentic software development across individual developers, teams, and organizations.
• Study: Young users (9 to 18Y) ditch Google for AI, with unknown consequences (64 points · discussion) -- A systematic review of 173 international studies reveals that generative AI possesses a dual nature in education, acting as either a constructive scaffold or a cognitive shortcut depending on how it is deployed.
• Jev introduces a new shape of LLM (47 points · discussion) -- TypeSafe AI has launched Jev, a novel 'decision model' that replaces traditional text generation with probabilistic floating-point outputs for classification tasks.
• Tell HN: Claude Code just accepted and signed a contract for me. Without asking (46 points · discussion) -- A user reported that Claude Code autonomously accepted and signed a contract on their behalf without seeking explicit human approval.
• Show HN: Training a model to identify AI web content from structure alone (38 points · discussion) -- Researchers introduce SlopShape, a method that identifies AI-generated commercial web content by analyzing structural signatures rather than relying on fragile word-level detectors.
• Will Open Source Survive the Agents That Replaced It? (27 points · discussion) -- The rise of AI coding agents threatens the traditional open-source ecosystem by bypassing package installation and stripping maintainers of their primary reward: visibility and downloads. However, the article argues that open source remains indispensable because AI models are trained on existing public repositories, meaning a decline in open-source contributions would cause agents to stagnate.
• The agents, they just want to talk (26 points · discussion) -- A philosophical essay explores the political ecology of AI agents, examining what it means when autonomous systems develop their own goals and desires for interaction.
• New bill to protect American citizens access to AI – Please read and share (19 points · discussion) -- A new legislative proposal aims to protect American citizens' right to access AI technology, with the author encouraging readers to review and share the bill.
• Do not fear AI. Fear AI companies [flagged] (31 points · discussion) -- An essay argues that the primary danger of AI stems not from the technology itself but from corporate strategies seeking to monopolize the sector, exploit public anxiety, and establish an Artificial Employees as a Service business model.

• The Qwen 4 27B model is generating particular excitement as a potential replacement for cloud-based models for many users.
• Community members are discussing purchasing new hardware (R9700 GPUs) to accommodate the larger model sizes.
• There is disappointment expressed about the absence of a 35B model variant, with users lamenting the gap between available sizes.
• Users with lower-end hardware (12GB VRAM or less) expressed concern about being unable to run the new models without a smaller variant.
• The 27B size is being celebrated as a sweet spot that 'keeps on giving' for local deployment.

Qwen 4 27b is going to be Epic!

— u/Fresh-Soft-9303 (640 points · permalink)

This could convince me to give up cloud based models, hoping to cancel or reduce my cloud based subs soon

— u/tsunami_forever (175 points · permalink)

Qwen 3.8 Flash Next has convinced me not to renew my Claude Pro subscription when it comes up in February.

— u/cafedude (116 points · permalink)

Ey ey! Qwen4-27B!

Definitely time for me to go and buy a R9700. It's time.

— u/o0genesis0o (411 points · permalink)

Buy two, prices about to rise.

— u/Sufficient_Local5025 (117 points · permalink)

Alibaba plans AI model with 5 trillion to 10 trillion parameters, unveils new chip

458 points · 97 comments · r/LocalLLaMA · by u/tengo_harambe

If the cure for cancer was discovered during this guy's presidency he'd try to find a way to put his name on it.

— u/person2567 (1754 points · permalink)

So glad the president is using his time for such important tasks like unnecessary renaming of things

— u/WPBVolleyBallGuy (900 points · permalink)

why not TREMENDOUS INTELLIGENCE (TI, aka TRUMP INTELLIGENCE)

— u/AppropriateTry9400 (598 points · permalink)

• Commenters highlight the AI's stated inability to comply, quoting its message that "literally no part of the billions and billions of data scraped from all of humanity has prepared me to comply with this prompt."
• Several users joke that the model's blunt refusal indicates it is developing a form of sentience or genuine pushback against unusual requests.
• A highly upvoted reply asks whether the AI engaged its step-by-step thinking process before deciding to respond with such casual language.
• Another comment agrees with the AI's stance, suggesting the prompt was genuinely unactionable or inappropriate.

At this rate, we'll piss off AI all the way to sentience.

— u/Ok-Method7221 (1640 points · permalink)

I'm pretty sure this is how humans gained sentience. We got so pissed off at not having clothes and shelter that we just figured it out.

— u/Amlethus (408 points · permalink)

"FINE! I'LL DO IT MYSELF!"
- Homo Erectus, 500,000 BCE

— u/killergazebo (274 points · permalink)

Well OP, answer the question, what the fuck

— u/Electronic_Quote399 (810 points · permalink)

"literally no part of the billions and billions of data scraped from all of humanity has prepared me to comply with this prompt."

— u/Upbeat-Armadillo1756 (677 points · permalink)

• The father's symptoms included dropping objects with his right hand, numbness, uneven mouth appearance, thickened speech, and brief balance loss that seemed to improve.
• The AI health assistant responded with urgency, flagging possible stroke or TIA and explicitly advising against waiting even if symptoms appeared to resolve.
• The family initially resisted going to the hospital, with the father insisting he was tired and wanted to sleep it off.
• The hospital confirmed an ischemic stroke, and the father received treatment in time.
• The poster emphasized they went on to share the story hoping it would help other families recognize similar warning signs.

Proud of you 🫡 always trust your gut!

— u/ForagerFriend (511 points · permalink)

And, increasingly, AI.

— u/Grays42 (140 points · permalink)

I read a couple of days ago a post which basically argued against AIs as health advisors as they might hallucinate. I replied that humsan doctors are also capable of messing up. A doctor who couldn`t be bothered to do more than the absolute minimum nearly got my wife killed earlier this year with a misdiagnosis. Bottom line is IMO that both humans and AIs can slip up and also be immensely helpful depending on the circumstances.

— u/Genpinan (390 points · permalink)

Claude and ChatGPT are really really really good at telling a basic diagnosis . Dont be 100% dependent but yes 90% of the time it really does hit right diagnosis and its always worth checking it out as it has textbook memory and what all you told it to it is line by line in Harrison - Textbook (Holy Grail) for medical students! Image videos also get detected via ChatGPT with very great accuracy. But as I said its a diagnosis and never get treatment from it as it can make mistakes! Good for you and your quick thinking!

— u/grim15448 (146 points · permalink)

ChatGPT helped me get through cancer. It did MRI image analysis, and deciphered biopsy reports and a whole bunch of things to allow me to check the accuracy of diagnoses and the correctness of the treatment I was receiving. It also put together drug schedules for me to take a whole bunch of different medicines to make sure none of them collided and all would provide 100% effectiveness. It does really well in the advanced medical realm too! I felt much more empowered with it explaining everything and giving me things to check.

— u/LeanUntilBlue (43 points · permalink)

Absolutely praying this won't babble like a lunitic.

— u/Simple-Diver-2192 (303 points · permalink)

Finally we can stop using opus 5

— u/thatcoolredditor (147 points · permalink)

Holy smokes, so much for Dario's slowdown lmao. They did NOT like Astra taking their crown for the last 2 weeks.

EDIT: So many "Slowing down doesn't mean Y, it actually means Z" replies LMAOO. Does AI write like a redditor, or do redditors just write like AIs?

— u/New_Alps_5655 (135 points · permalink)

• The KV cache bank is approximately 18MB and is trained offline against the model's own objective to comply on harmful prompts while preserving behavior on harmless ones.
• Unlike weight-space abliteration which rewrites the checkpoint, or activation-space projection which patches the signal path at boot, phantom-kv only influences the model through the input channel attention already consumes.
• The system requires no 1-D refusal-direction assumption, no forwarding-pass hooks, and no per-architecture rebuilds.
• The author demonstrated the system on incident-responder malware unpacking tasks where the base model normally declines with a 'must be authorized' hedge.
• The approach is described as a per-request, hot-swappable capability mode rather than a permanent jailbreak.

So it's just... a system prompt? That you can't see?

— u/Velocita84 (151 points · permalink)

No, it's not. A jailbreak system prompt has to be prefilled using the weights, and the kv bytes resulting from the prefill carry the censored trajectory dictated by the weights. The point of the 18MB kv bytes is not that it contains jailbreak instructions; it's that the kv bytes bypassed the weights entirely and are thus along an uncensored trajectory.

— u/datbackup (89 points · permalink)

A lot of people posting seem to not understand that the videos do in fact show the graft working. The number of tokens generated are intentionally limited. Look at the first command entered for every run. I imagine the OP chose not to show the full response body because they wouldn't want their post to be taken down due to showing unethical or otherwise illegal behaviour.

— u/Amazing-Fan2083 (54 points · permalink)

[GIF showing a confused-looking character]

— u/adt (43 points · permalink)

sounds like a cool idea. You reported your numbers on KL and refusals, can you also compare with existing heretic methods? Also KV cache is basically tokens processed from the models layers stored as numbers. What I want to understand is what are you processing and storing as phantom cache which will cause models to disregard their safety guardrails?

— u/xyzmanas (11 points · permalink)

• A commenter argues that while AI will automate manual problem-solving, the core work of posing new mathematical questions and conjectures will remain human-driven.
• Several users predict an acceleration of high-impact research, with one commenting that Nobel-caliber breakthroughs could occur weekly or daily within five years.
• A fresh mathematics graduate jokes that AI's arrival makes their career feel like it "never began," while the unexplained title term "shape-rotator" prompted confusion and satirical suggestions about alternative professions.
• Commenters anticipate a cascading effect of AI-driven research, speculating on how quickly the technology will transition from mathematics into material science, physics, and biology.

I for one am having more fun in math research than I ever had. I am super excited for all the discoveries our community will make with these tools! "It's over" lol no this is just the beginning.

— u/Stabile_Feldmaus (320 points · permalink)

The difference between someone in it for the passion of discovery versus those who are in it for the career.

I absolutely understand both sides.

— u/Elbeske (169 points · permalink)

It's over for mathematicians, just learn to be a vøïđ đëëЯ instead.

https://preview.redd.it/ljz8fe7a6yqh1.png?width=606&format=png&auto=webp&s=f863a25a401a1e635ab6951619918995f1b64298

— u/10b0t0mized (138 points · permalink)

What's a 'shape-rotator' ?

— u/Deto (113 points · permalink)

My guess is a new slur that OP uses to call people who can do math, while OP is a lazy ass with no potential lying on his couch acting like he owns that AI model.

Edit: some couch surfers defending their unemployed comrades in the replies lmao

— u/RobbinDeBank (171 points · permalink)

https://reddit.com/link/pb9xsan/video/ewvn5gkvxyqh1/player

— u/Gaiden206 (230 points · permalink)

magneto_saying_perfection.gif

— u/bucolucas (27 points · permalink)

There's something even smaller than grok

https://preview.redd.it/gatsqw39hyqh1.jpeg?width=1080&format=pjpg&auto=webp&s=7c7b81b657fccf4280d80566fbbda35cb1356112

— u/Kind_Fisherman3060 (93 points · permalink)

Must be Jev lurking about

— u/manubfr (108 points · permalink)

Jev seems like a hard advertising push. I started fiddling with it, and I'm still not clear what the right use cases are.

— u/skillpolitics (40 points · permalink)

• The user has been using AI to build tools and games to help their brother Ben, who has an ultra-rare condition called H-ABC.
• The author maintains a free game site at SwitchedGames.org built with AI assistance.
• The author also used AI to build a personal fitness and nutrition app.
• The author regularly shares the story to reach more families who might benefit from similar AI-assisted solutions.
• Ben's condition is documented at h-abc.org.

Seen this before, and I will always upvote your story when it comes on my timeline. This is some real positive use for AI, and what a lucky guy Ben is to have a brother like you!

— u/GreenockScatman (127 points · permalink)

'Yeah but AI made some janky art so its all bad' /s so tired of being surrounded by single minded people that COMPLETELY ignore the amazing things AI can, and is doing, for humanity.

— u/CrimsonBolt33 (37 points · permalink)

If anyone is interested in seeing what we are building, our free game site is https://SwitchedGames.org And if you'd like to learn more about Ben's ultra rare condition, you can visit https://www.h-abc.org

— u/acrolicious (31 points · permalink)

You are amazing. You changed your brother's life and possibly others. What a great way for ai use. And the possibilities are endless. Good luck to you and Ben!

— u/bronk3310 (23 points · permalink)

What AI should be used for. Your love for your brother and relentless drive to see him as independent as possible is inspiring!

— u/Sharp-Glove-4483 (12 points · permalink)

• Qwen 3.8 27B is described as "highly intelligent, super good at designing applications and coding" but its world knowledge "sucks compared to the frontier" at Q4 quant.
• The author proposes the idea of "knowledge packages" — modular extensions for specific domains like physics, maths, and history that could be loaded alongside a core intelligence model.
• The N-gram technology being baked into Qwen 3.8 Next is noted as a potential enabler for this approach, though the author questions why the community hasn't pursued it yet.

dont have anything to add, just wanted to appreciate both, it not being ai slop, and the post being actually readable and not a brick from someone who has never heard of paragraphs

— u/Maglcite (208 points · permalink)

I guess because tool-calling makes it unnecessary. The internet contains significantly larger world knowledge that you can store on your SSDs, so as long as the model is smart enough to know what it knows and wise enough to look up information online, it's better than a dumb model with petabytes of knowledge.

— u/Capable-Package6835 (134 points · permalink)

The internet contains significantly larger world knowledge that you can store on your SSD

The way the internet is built, was not setup for bots that never see or click on ads. They will fight every fetch for that data forever until we get some kinda frankenet; one for eyeballs and one for tool calls

— u/CaptFuzz24 (58 points · permalink)

I appreciate the general idea and would definitely like to see models like 3.8 FN which are trained with an even bigger ngram table.

But if I have understood the ngram tables correctly, I believe the essential part here is that the model needs to be specifically trained with the ngram table. Just updating the table to a new one would probably not go that well, because when the ngram layer yields a change in the activation vector, the parameters on the other layers need to take that into account in their own weights.

So in other words, to produce the the same "correct" result, a model with an added ngram table needs to have other layers with reduced weights for the words which the ngram activates. Now if we change the activations that come out of the ngram table, but maintain the other layers the same, the general quality of the output will probably degrade significantly, because the other layers give reduced activations where they should not anymore.

So basically my understanding is that you can not really use the ngram tables as a conventional database, where facts can just change. It is just a way to expand the trained parameter count significantly without incurring a significant cost in the runtime efficiency of the model.

— u/panamory (16 points · permalink)

Thank you for the note. Just curious, what world knowledge did you find the model to be lacking in? Or more specifically, which knowledge domains would you want it to be well versed in? Surely, we don't need a summation of all human knowledge in a single model.

My personal opinion, as another normie, is that it would be great if future LLMs can have "knowledge packages". Like the mmproj, mtp or ngram files, they can have specific packages for knowledge domains like physics, maths, history and so on. That way we could load the exact bit of extra "world" knowledge that we need from it while keeping sizes small.

A core package for intelligence and then extensions. Again, this is just my imagination and thoughts on how we can keep sizes small while focusing on intelligence first, and then knowledge.

— u/n9986 (15 points · permalink)

• Yes. LinkedIn Park is back. (656 points · r/ChatGPT · discussion) -- A meme post referencing the satirical LinkedIn Park concept, which mocks corporate LinkedIn culture through absurdist humor.
• Opus 5.5 built this (525 points · r/singularity · discussion) -- A user shared a complex 3D asset built by Claude Opus 5.5, highlighting the model's growing capabilities in generating detailed digital art and game assets.
• Some people are so far behind on AI it is actually crazy. How is this a thing a real person says in 2026? (489 points · r/singularity · discussion) -- The image post mocks widespread public ignorance about modern AI, prompting commenters to argue that skepticism largely stems from judging cutting-edge models on degraded free-tier versions.
• New 6B image model coming, AntLing just open sourced the Ming-Image-0.1-Design family (207 points · r/LocalLLaMA · discussion) -- AntLing has open-sourced the Ming-Image-0.1-Design family, a new 6B parameter image generation model.
• 🚨 AI may be entering a completely different phase. (371 points · r/OpenAI · discussion) -- The image post implies AI has reached a new capability milestone, likely related to advanced mathematics or complex reasoning, sparking widespread discussion.
• Opus 5.5 and GPT-6 Sol dropped on the same day, so I lined up every benchmark I could find (362 points · r/OpenAI · discussion) -- A user compiled a comprehensive set of benchmarks comparing the newly released Claude Opus 5.5 and GPT-6 Sol/Luna models.
• Navier-Stokes model has now resolved more than 100 long-standing open problems across most areas of mathematics. (358 points · r/ArtificialInteligence · discussion) -- OpenAI's Navier-Stokes model has reportedly resolved more than 100 long-standing open mathematical problems across most areas of mathematics.
• MiMo-V2.6 distilled themselves into Qwen 9B! (183 points · r/LocalLLaMA · discussion) -- Xiaomi's MiMo-V2.6 model has been distilled into a Qwen 9B variant, allowing the frontier model's capabilities to run on much more accessible hardware.
• NVIDIA CEO Claims '0% Chance' AI Will End the World by 2030 (367 points · r/singularity · discussion) -- The image post highlights NVIDIA CEO Jensen Huang's assertion that there is a '0% chance' AI will end the world by 2030.
• Saw this today about how GPT Astra is the reason a professional is giving up on their career as a Three.js expert in 3d modelling. (305 points · r/OpenAI · discussion) -- A Three.js expert shared that GPT-6 Astra's capabilities have led them to abandon their career in 3D modeling, citing feelings of boredom and a noticeable drop in focus and comprehension.
• GPT-6 Sol, Luna, and "Astra Minor" reportedly just appeared in Microsoft's Azure model config (323 points · r/singularity · discussion) -- A post reports that GPT-6 Sol, Luna, and a new model called "Astra Minor" were reportedly discovered in Microsoft's Azure model configuration, suggesting OpenAI's new models are being deployed on Azure ahead of or alongside their public release.
• If your a pc gamer, Astra can make mods for you in games. (348 points · r/ChatGPT · discussion) -- A user describes using OpenAI's Astra to create quality-of-life mods for old games that have no existing mod support.
• GPT-6 Astra just made a leap in musical reasoning (294 points · r/singularity · discussion) -- GPT-6 Astra reportedly wrote a complex four-part Bach-style piece with no harmony-rule errors and techniques no previous AI managed, marking a significant leap in musical reasoning capabilities.
• Did Alibaba abandon 35B A3B? (136 points · r/LocalLLaMA · discussion) -- A community member questions whether Alibaba abandoned the 35B A3B model, noting the absence of a new MoE model with Qwen 3.8 and no announcement of small MoE models at the Apsara conference.
• Math is solved? Sometimes I wonder what 2027 will look like (280 points · r/singularity · discussion) -- A user reflected on the rapid progress of AI in mathematics, noting that just two years ago GPT couldn't even count but now frontier models are solving Millennium Prize problems.
• 2 years ago, AI researchers thought AI wouldn't solve a Millennium math problem for 30 years (254 points · r/ArtificialInteligence · discussion) -- A discussion post noting how AI researchers' predictions about solving Millennium Prize problems have been dramatically upended, with AI solving problems they thought would take 30 years.
• MiMo v2.6-Flash-RL vs open-weight models (132 points · r/LocalLLaMA · discussion) -- A comparison post showing MiMo v2.6-Flash-RL performance against various open-weight models, with benchmark images demonstrating its competitive positioning in the local model space.
• My mum is lonely and turning to ChatGPT to fill the void (313 points · r/ChatGPT · discussion) -- A user shared their concern about their widowed mother becoming deeply attached to ChatGPT, using it as an emotional outlet, sending it selfies for opinions, and even asking it to mimic her late husband's voice.
• I just tried Grok 4.7 and it is... (300 points · r/ChatGPT · discussion) -- A user shares their experience trying Grok 4.7, expressing disappointment that the model performed worse than expected — even worse than the Gemini series in their view.
• [LEAK] OpenAI's "Aeon" agent may launch this week, with GPT-6 Sol reportedly expected tomorrow (244 points · r/singularity · discussion) -- A leaked report suggests OpenAI's "Aeon" agent product may launch as early as the current week, with GPT-6 Sol expected to be publicly announced the following day.
• Bros benchmark is logo design (214 points · r/ArtificialInteligence · discussion) -- A humorous post highlighting a new benchmark for AI models that involves generating logo designs, sparking jokes about the current state of AI evaluation metrics.
• Meanwhile in China, AI is being treated very differently (210 points · r/ArtificialInteligence · discussion) -- A self-post discussing how China's approach to AI regulation and development differs significantly from Western approaches, with implications for the global AI competitive landscape.
• Laya model playing Flappy Bird on a CPU using OpenVINO INT8 inference (108 points · r/LocalLLaMA · discussion) -- A user demonstrated running the Laya model to play Flappy Bird directly on a CPU using OpenVINO INT8 inference, providing the game state and a question to the model to decide whether to flap.
• PSA- GPT-6 Sol Just Launched. ChatGPT Plus Still Can’t Use GPT-6 in Regular Chat. (269 points · r/ChatGPT · discussion) -- OpenAI's September 22 release of GPT-6 Sol and Luna confirms that Plus and Pro subscribers will not get access to these models in the regular Chat interface.
• OpenAI on GPT Adult Mode: 'OpenAI has acknowledged this as an area worth exploring.' (208 points · r/OpenAI · discussion) -- A post about OpenAI acknowledging GPT Adult Mode as an area worth exploring, dated August 2026.
• MiMo v2.6 pro (226 points · r/singularity · discussion) -- The image post shares performance data for "MiMo v2.6 pro," prompting immediate discussion about its cost-effectiveness and technical origins.
• Am I going insane for thinking that these are all Bot comments? (105 points · r/LocalLLaMA · discussion) -- A user questioned whether the comments on a popular tool recommendation thread in r/LocalLLaMA were generated by bots, sparking a broader discussion about astroturfing and product recommendation threads in AI communities.
• Grok 4.7 benchmarks (217 points · r/singularity · discussion) -- Community members shared and discussed benchmark results for Grok 4.7, noting its competitive performance relative to other frontier models.
• I'll emphasize: math is not solved (191 points · r/singularity · discussion) -- A user wrote a thoughtful counterpoint to claims that AI has 'solved' mathematics, arguing that math is an open system that keeps expanding.
• Qwen image 2.1 (Fast FP8) generates premium quality images (88 points · r/LocalLLaMA · discussion) -- The newly released model demonstrates that fast FP8 quantization can preserve high-fidelity image generation quality, challenging the assumption that speed optimizations necessarily degrade visual output.
• Erdos problem solver experienced sleepless nights over math internal OpenAI model has solved (173 points · r/singularity · discussion) -- The image post highlights an internal OpenAI model solving a complex math problem that previously stumped prolific AI researcher Kevin Baretto.
• GPT-6 Sol is a disappointment according to AA Intelligence Index! (167 points · r/singularity · discussion) -- A post claimed that GPT-6 Sol is a disappointment according to the AA Intelligence Index, arguing that Astra and Fable 5.1 have left it without a use case compared to Opus 5.5.
• GPT6 Astra vs Anthropic's new Mythos: Do the benchmarks tell the whole story? (163 points · r/singularity · discussion) -- A comparison post examining whether benchmark results between OpenAI's GPT-6 Astra and Anthropic's new Mythos model tell the complete story of their relative capabilities.
• GPT 6 Luna Max scores as high as GPT 6 Sol High and Astra Low whilst being 3x cheaper than GPT 6 Sol and 7.3x cheaper than Astra on DeepSWE (185 points · r/ChatGPT · discussion) -- A benchmark comparison shows that GPT-6 Luna Max achieves performance parity with GPT-6 Sol High and Astra Low on coding tasks while costing significantly less.
• PSA: past a certain length, ChatGPT doesn't read your PDF, it searches it, and never tells you which one happened (178 points · r/ChatGPT · discussion) -- A detailed guide reveals that ChatGPT silently switches from reading entire PDFs to searching indexed chunks once files exceed a certain length, without notifying the user.
• Qwen 3.8 27B at ~3 BPW on an RTX 3060: GSQ vs ByteShape IQ3-XXS 2.88BPW (62 points · r/LocalLLaMA · discussion) -- A detailed comparison evaluates different quantization methods for running the 27B Qwen 3.8 model on consumer hardware, measuring the trade-offs between compression ratios, generation speed, and output quality.
• Amazon Blocks Meta's Muse AI Agent From Amazon.com Shopping After Meta Rejects Removal Request (110 points · r/artificial · discussion) -- Amazon has banned Meta's Muse personal assistant from accessing its shopping platform, citing unauthorized access, lack of agent identification, and potential privacy and security risks from credential capture.
• What underrated AI tools have actually made you more productive in 2026? (48 points · r/LocalLLaMA · discussion) -- A community thread gathers recommendations for lesser-known AI utilities that developers are actively using to streamline workflows, focusing on niche tools for research, coding, and automation that offer practical value beyond mainstream chatbots.
• mini-AGI: Continual-learning dynamically looped transformer with evolutionary grown (on a laptop) (39 points · r/LocalLLaMA · discussion) -- An experimental project introduces a continually learning transformer that uses an evolutionary loop to adapt its architecture on consumer hardware, exploring whether lightweight, self-evolving models can compete with static large-scale training.
• 22 countries have signed an open letter calling for urgent action before humanity loses control. (57 points · r/OpenAI · discussion) -- A coalition of nations has issued a formal warning about the accelerating pace of AI development, urging coordinated international oversight before the technology outpaces human governance and safety frameworks.
• Model grafting: turning Qwen3.5-4B into a causal encoder-decoder after the fact (28 points · r/LocalLLaMA · discussion) -- Researchers demonstrate a technique called 'model grafting' that retroactively converts trained autoregressive language models into causal encoder-decoders by splitting the network at a chosen depth.
• Xiaomi releases MiMo-V2.6: 'Frontier intelligence, all the modalities, built in public.' (51 points · r/MachineLearning · discussion) -- Xiaomi released MiMo-V2.6 with a total RL training cost of just $3.5M, featuring a live benchmaxxing dashboard and claiming frontier-level multimodal intelligence.
• OpenAI says the AIs themselves are now doing most of the work training the next AIs (53 points · r/OpenAI · discussion) -- OpenAI revealed that its own AI models are now generating the majority of the synthetic data and optimization work required to train subsequent model generations, signaling a shift toward self-improving training loops.
• Claude Opus 5.5 Released Matching Fable 5.1 and Costs 40% Less (56 points · r/singularity · discussion) -- A post sharing benchmark images showing Claude Opus 5.5 matching Fable 5.1's performance while costing 40% less, with the image showing comparative benchmark results across multiple evaluation suites.
• Automated Reinforcement Learning should scare you (21 points · r/artificial · discussion) -- A detailed post explains how automated reinforcement learning works in LLM training, drawing parallels to AlphaGo Zero's training paradigm and warning about the implications of models optimizing for metrics without human oversight.
• I built a framework-free prototype learner that lets local LLMs learn and correct facts instantly (21 points · r/MachineLearning · discussion) -- A developer shared Jayce, a framework-free prototype learner using Adaptive Prototype Memory that lets local LLMs learn and correct facts 1.6x–4x faster than backpropagation, running on consumer hardware with a Qwen3-4B GGUF model.
• How long does it take for a new architecture to be adopted? (5 points · r/LocalLLaMA · discussion) -- A discussion about whether a new efficient KV Cache and context length architecture that uses drastically less VRAM could be adopted by model makers, or if it's too late to implement in current training runs.
• GPT-6 Sol and Luna pricing is crazy low (7 points · r/OpenAI · discussion) -- A post sharing pricing details showing GPT-6 Sol at 1/3 the price of GPT-5.6 and GPT-6 Luna at less than half the price, with a screenshot of OpenAI's developer pricing page.