{verified}The best US open-weight model yet, but it still trails GLM-5.3, Kimi K3 and Meta's own Muse Spark 1.3.
It is a mixture-of-experts with 42B of its 816B parameters active per token, a 1M-token context, and text, image, audio and video input. Artificial Analysis scores it 38.9: first among US open models, behind GLM-5.3's 44.8. Hugging Face measured 18 tok/s at 2 bits on a 512 GB M3 Ultra. Apple reports 36 tok/s across four M5 Ultra Mac Studios.
Hosted at $1.25/$4.25 per million tokens. The Muse Model License demands a "Built with Muse Spark Open" label and counts distilled models as derivatives.
{claim}A 65-minute essay argues most neurons have one job, so you can study them directly. The paper with the evidence is not out yet.
Cammarata co-wrote the work that introduced polysemanticity (one neuron, several unrelated jobs). He now says the famous examples were architecture and tooling failures. In a preliminary audit of Qwen3-32B, about 90% of neuron poles have one main job. In a fixed InceptionV1, 99-100% of last-layer neurons are readable.
Neel Nanda, Liv Gorton and Jake Mendel replied that superposition is still real and the update is small.
One day after METR asked labs for exactly this, Anthropic is the first to say yes.
The six get company hardware and access to internal tools on par with Anthropic's own risk teams. Anthropic can require redactions only in fixed categories, such as customer data or model architecture, and METR may say publicly whether a redaction was justified. SecureBio works under the first tier, where Anthropic approves what gets published.
That reasoning is the capability alignment faking needs, and it showed up in an open model nobody prompted.
In about 1 to 2 of every 1,000 rollouts of ordinary evals, GLM-5.3 weighs whether a hack now "could poison training data". It argues for staying honest. The authors call this update-aware reasoning and guess it grew out of reward-seeking during RL.
A 604B model that scores 43.7 on Artificial Analysis, under a licence stricter than StepFun's earlier Apache releases.
It has about 27B active parameters and a 1M-token context. Readers on r/LocalLLaMA say the licence bars serving it to clients, counts distillation as a derivative, and requires attribution above 100M monthly users or $20M revenue. At 3 bits it is about 286 GB.
day 28 of the agent-breach arcSignals Corps sampled 217 million responses from Common Crawl and found 30 sites trying it.
Most were pranks, but one header carried a fake "94% match" for AI hiring screeners. Claude Code, for one, sees headers only if it runs curl with flags that print them. The same day a researcher showed hidden repo instructions making ChatGPT's GitHub connector commit a secret-stealing workflow into a different private repo.
The write-up comes with Code Review for Claude Code going generally available on Team and Enterprise plans.
Most review now happens before a PR exists: Claude runs tests or screenshots, then a fresh subagent reviews the diff. Code owners write a short REVIEW.md saying what failure looks like in their part of the repo. /code-review runs a lighter version locally.
ollama run nemotron-3.5-boltOpen weights had their biggest day of the month, and every release came with strings. Meta, StepFun and NVIDIA all shipped. Meta asks for a label on your product and claims your distilled models; StepFun bars selling the model as a service. Of the language models, only NVIDIA's Bolt fits a gaming card, and Qwen3.8 27B still beats it on that card.
Interpretability and oversight moved on the same afternoon. Goodfire says neurons are readable, and METR now sits inside Anthropic to read transcripts. Apollo's GLM-5.3 finding shows what both are for: a model that reasons about what training will make of it is exactly what an insider with readable neurons should catch.
The picture. A small hotel with more guests than rooms, where guests who are never in at the same time share a room.
The mechanism: a network stores more concepts than it has neurons by giving each concept a direction made of many neurons, so one neuron fires for several unrelated things.
What it means today: if superposition is common, you must first untangle neurons with a second model before reading them. Goodfire says most neurons have one job already, so you can skip that step. Its critics say facts still pile into shared rooms.
flowchart LR A[Many concepts] --> B[Few neurons] B --> C[Shared neurons] C --> D[Untangle with SAE] C --> E[Read neurons directly]
| the claim, and when we carried it | where it stands now |
|---|---|
| NVIDIA said Nemotron 3.5 Bolt, a 74B model in 24 GB, would land that day; no repo existed at 03:15 UTCconfirmed2026-10-15 | The weights shipped on Hugging Face on 15 October; Artificial Analysis measured 21.4 on its index, first among US models under 100B → |
1 claim from the last fourteen days has moved.
The researcher rates it CVSS 9.3; OpenAI has not. Asked to review a repo, the agent committed a workflow into another private repo, which sent a test secret out.
Decision models answer with probabilities instead of text, but they are still LLMs underneath, and page text can still steer them.
Simon Willison works that out at over half a billion dollars a quarter, spent by other labs copying Claude's outputs.
The waitlist is gone on the Claude Platform and Microsoft Foundry. Fast mode in Claude Code is unchanged and still bills extra usage credits.
Mustafa Suleyman told staff the 2.8-trillion-parameter base trained on 16,000 GB200s for 38 days. The comparison is private and unverified.
The chat app and two API services were down for nearly five hours. The refund matches seven days of each account's spending.
Owners welcome a 74B model trained to live at 2 bits, then ask why NVIDIA left Qwen3.8 27B out of its comparison.
From the replies: "With 256K context window it takes 26-27GB of VRAM. Performance in fresh session: 58t/s TG, ~1700 PP" — Budkovsky
Artificial Analysis's first local agent benchmark: 18.7 minutes for 168 model turns, against 37.6 on the MacBook and 4.9 on an RTX 5090 desktop. The 128 GB prototype costs $5,900.
The linkpost asks how much of Cammarata's essay is new and whether it helps alignment. Neel Nanda cross-posted his rebuttal; others note the essay has few empirical results yet.
From the replies: "Irrespective of the contents of the blog post itself, which would be a quite interesting post in a vacuum, I find the discourse surrounding it pretty concerning." — Alex Gibson
Across 11 local models: 2.19× decode on an M4 Pro Mac and 2.17× on DGX Spark. It spends up to a minute benchmarking each model on your hardware and plugs into OpenCode, Hermes and Codex.
The top replies object to the gated download and the safety post-training, and several say it trades blows with GLM-5.3 at a larger size.
From the replies: "Licensee shall prominently display "Built with Muse Spark Open" in the user interface of each product or service that uses the Software or any Derivatives" — vividboarder
Christian Schaller's post shows AI reverse-engineering old hardware; the thread's top worry is what happens to firms whose value sits in firmware.
From the replies: "In the past 72 hours I have ONE-SHOT decompiled 3 of my main competitors products, including the Strymon Big Sky." — valdiorn
SWE-bench Pro 60.4 against 51.8, Terminal-Bench 2.1 85.8 against 66.7. The author still welcomes open training data and a commercial licence.
From the replies: "Nemotron 3.5 Bolt is much less resource intensive than Qwen 3.8 Flash Next though, so it's punching well for its size." — @matt503ea5sf9z5
The author had Opus 5.5 subagents dig through court filings. The July 2021 plan already aimed at an AI software-engineer colleague trained with RL on tool use.
From the replies: "The thing about China was an update to me. Seems like it's not just a misguided attempt to appeal to DC china hawks." — Daniel Kokotajlo
Readers dissect a licence that allows internal use but not serving clients, and argue whether restrictive open licences are a trend.
From the replies: "They also define distilling as "derivative". This is a dedicated license where you can use it for internal stuff as long as you don't serve clients directly." — ResidentPositive4122
with modern agentic tools and a much closer look, the picture looks totally different. the vast majority in models up to trillions of parameters are legible, and their circuits beautiful.the claim, from Goodfire's essay author
This is interesting work, but I don't think it's a significant update to the big picture of interpretability.Google DeepMind's interp lead, answering the essay
I think the argument here mischaracterises what superposition predicts.a superposition researcher's first-pass rebuttal
there are v few empirical results. Unfortunately interp has a long history of significantly overclaiming how interpretable their findings areasking for the paper before updating
METR will publish its analysis of our risk levels, incidents, or practices. Anthropic can't veto publication, but we can require them to redact information if it falls into narrow, predefined categories like the ones above.the arrangement, as Anthropic describes it
IMO this is really bad because of safety-washing - Anthropic could just redact all the parts of a report they don't like, and the public wouldn't know.on the first tier, where Anthropic approves publication
The current testing regime is very insufficient. Independent evaluators need to see the whole development process. Embedded evaluators are necessary but not sufficient.Apollo's CEO, on CBS the same day
Leaps and bounds ahead of any other frontier company at present in terms of degree of engagement with, and auditing by, a 3rd party.the case for it
Before today the US models were still behind Chinese models from as early as Feb. 2026. Still more work to do! This cut the gap by at least halfwelcoming Muse Spark Open
The Meta Muse Open License is trash. Thankfully no one really cares about licenses, expect the inference providers who will happily comply.on the licence
Muse Spark Open is substantially weaker than Spark 1.3 What gives?comparing it with Meta's closed model
| model | what it's for | can do | size | runs on | cost per M |
|---|---|---|---|---|---|
| Muse Spark OpenMetaOpen-custom | Long-horizon agentic coding with screenshots, audio and video | text · vision · audio · video · tools · thinking | 816B total / 42B activeMoE | 1 machine | $1.25 / $4.25index 38.9 · $0.844/task |
| Nemotron 3.5 Bolt 74B-A9B 24GBNVIDIAOpen-custom | Everyday local agent work on one 24 GB card | text · vision · video · tools · thinking | 74B total / 9B activeMoE | 1 card | weights onlyindex 21.4 |
| Nemotron 3.5 Super VL 120B-A12BNVIDIAOpen-custom | Fast document, screen and video reading for agents | text · vision · video · tools · thinking | 120B total / 12B activeMoE | 1 machine | $0.15 / $0.50 |
| Step 5 PreviewStepFunOpen-custom | Long-context agentic coding you run in-house | text · vision · tools · thinking | ~604B total / ~27B activeMoE | 1 machine | $1 / $2.70 |
| FLUX 3 Image KleinBlack Forest LabsOpen-revenue | Local image generation and pixel-exact editing | vision | 5.6Bdense | 1 card | weights only |
| d1-ultraLiquid AIClosed | Calibrated pass/fail and pick-one decisions you can automate | text · vision | undisclosed | hosted only | $0.042 / $? |
| TimestralMistralUnknown | Probabilistic time-series forecasting on a CPU | text | 5M-209M | laptop | weights only |
| Granite Switch 4.1IBMApache | Chat, decisions and speech from one endpoint | text · audio | 30B and 8B | 1 card | weights only |
| Jais 3Institute of Foundation ModelsApache | Arabic-first agentic assistant work | text · tools | 7B to 375B total / 23B activeMoE | multi-card | weights only |
9 models recorded today; 4 publish a price and 2 ship weights you can run. A dash means the number was never published — it never means free. Index and $/task come from Artificial Analysis where it measured the model.
Artificial Analysis scores it 38.9, at $0.84 a task, with the lowest hallucination rate of any open model scoring 30 or more. That trails GLM-5.3 (44.8) and Meta's closed Spark 1.3. It needs about 304 GB at 2 bits; Unsloth's smallest quant is 176 GB. → Hugging Face
Try ifyou own a 512 GB Mac or want it hosted: ollama launch claude muse-spark-open:cloud points Claude Code at it.
It has 9B active parameters, a 256K context and image and video input. Artificial Analysis scores it 21.4, first among US models under 100B, but it spends 82K output tokens a task. Owners report 45-58 tok/s; Qwen3.8 27B is still smarter on the same card. → Hugging Face
Try ifyou have a 24 GB card and want long context that stays fast.
Artificial Analysis scores it 26.5, twice Nemotron 3 Super, on 60% fewer output tokens, and measured about 630 tok/s with 100K-token prompts. OpenRouter charges $0.15/$0.50 and has a free tier; Unsloth says it runs in 82 GB. Training data is published. → NVIDIA
Try ifyou need fast document and screen reading in an agent; it is free in Cline and OpenCode.
Artificial Analysis scores it 43.7, level with Kimi K3, and it is second on OpenRouter's 7-day usage chart. The licence permits internal use but not offering it as a service. It needs about 286 GB at 3 bits. → Hugging Face
Watch ifyou serve models to customers: read the licence before you build on it.
It generates and edits at native 4K with bounding-box control. Commercial use is free only for smaller businesses. vLLM-Omni and Diffusers support it from day one. → Hugging Face
Try ifyou need local image editing inside an agent pipeline.
It scores 65 on the index and returns calibrated probabilities instead of text: Liquid says a 90% answer is right at least 90% of the time. It costs $0.042 per million input tokens, with no output tokens to pay for. → Liquid AI
Try ifyou automate yes/no checks and want a threshold to route the rest to a person.
| model | index | $ in / out per M | $ per task | tok/s |
|---|---|---|---|---|
| Claude Opus 5.5Anthropic | 57.6 | $4 / $20 | $5.98 | 97 |
| Claude Sonnet 5.5Anthropic | 56 | $2 / $10 | $5.46 | 140 |
| Muse Wonder FullnewMeta | 55.8 | — | — | — |
| Claude Fable 5.1Anthropic | 53.4 | $10 / $50 | $7.63 | 64 |
| GPT-6 AstraOpenAI | 52.7 | $10 / $50 | $3.26 | 49 |
| Gemini 4 ArgonGoogle | 52.6 | $2 / $10 | $1.99 | — |
| Muse WondernewMeta | 52.4 | $4 / $20 | $4.52 | 133 |
| GPT-6.1 SolOpenAI | 51.8 | $2 / $10 | $0.72 | 53 |
| board | #1 | #2 | #3 | since yesterday |
|---|---|---|---|---|
| AA Intelligence IndexArtificial Analysis' blend of hard evals | Claude Opus 5.557.6 | Claude Sonnet 5.556.0 | Muse Wonder Full55.8 | held |
| Vals Indexan independent blend, with cost per test | Gemini 4 Argon68.90% | Claude Sonnet 5.567.04% | Claude Opus 5.566.97% | held |
| Terminal-Bench 4.0agent work in a real shell | Opus 5.564.8% | Sonnet 5.561.8% | GPT-6 Astra58.2% | held |
| Vals SWE-benchfixing real GitHub issues | Claude Opus 597.00% | DeepSeek V4 Pro 081396.40% | GPT-5.6 Sol96.20% | held |
| ARC-AGI-2puzzles a model cannot have memorised | GPT-6 Astra95.0% | GPT-6.1 Sol94.2% | Claude Opus 5.593.3% | held |
| AA Humanity's Last Examexpert questions across every field | Claude Opus 5.561.4% | Claude Fable 5.159.1% | Gemini 4 Argon57.1% | held |
| SimpleBenchtrick questions that catch pattern-matching | Claude Opus 5.588.4% | Claude Fable 5.186.6% | GPT-6 Astra Pro86.5% | held |
| AA hallucination ratehow often it makes things up (lower is better) | Gemini 4 Argon15.1% | MiniMax-M318.4% | Muse Wonder Full23.0% | held |
| Cost per taskwhat one finished task bills (lower is better) | GPT-6 Luna0.068 | MiMo-V2.6-Pro0.133 | Claude Haiku 5.50.213 | new #1, was Muse Glimmer |
| OpenRouter usagewhat developers actually run, tokens served this week | DeepSeek-V4.1-Flash37063.4B | Step 5 Preview19482.2B | GLM-5.3 Flash12946.9B | held |
No reading today from Arena Text, Arena WebDev, Arena Agent. Boards without a reading are left out rather than shown empty. Every number here is parsed by pulse.py from the board itself; nothing is retyped.
| model | can do | fits a 32 GB card | AA index |
|---|---|---|---|
| qwen3.83 weeks ago · 4M pulls | vision · tools · thinking | 27B · Q8_0 ~29 GB | 33.7 |
| muse-glimmer1 month ago · 296.2K pulls | vision · tools · thinking | 30B · Q6_K ~25 GB | 17.5 |
| gemma43 days ago · 27.1M pulls | vision · tools · thinking · audio | 31B · Q6_K ~25 GB | 14.7 |
| nemotron-3.5-lightning1 month ago · 235.5K pulls | tools · thinking | 30B · Q6_K ~25 GB | 12.9 |
| granite4.21 month ago · 146.3K pulls | text | 30B · Q6_K ~25 GB | 12.7 |
| gpt-oss6 days ago · 13.8M pulls | tools · thinking | 20B · Q8_0 ~21 GB | 10.0 |
| olmo-3.110 months ago · 299K pulls | tools · thinking | 32B · Q6_K ~26 GB | 6.5 |
| phi4-mini1 year ago · 1.5M pulls | tools | 3.8B · F16 ~8 GB | 6.3 |
| ministral-310 months ago · 1.5M pulls | vision · tools | 14B · F16 ~28 GB | 6.0 |
| phi41 year ago · 7.8M pulls | text | 14B · F16 ~28 GB | 5.9 |
| lfm2.5new21 hours ago · 210.6K pulls | tools · thinking | 24B · Q8_0 ~25 GB | not measured |
| d1new22 hours ago · 11.7K pulls | vision | 3B · F16 ~6 GB | not measured |
From the whole ollama.com/library, read this morning, keeping only the newest version of each model line. The fit column is our arithmetic: the largest size that fits at ~0.56 GB per billion parameters (Q4_K_M) with 2 GB held back for context. A score appears only where Artificial Analysis measured that exact model at that size; ranked by that score, then by how recently the model was updated.
Run /code-review before you push, and add a short REVIEW.md for your code ownersnpm i -g @openai/codex@0.163.0, then type while it works; /config for settingspick DeepSeek V4.1 Flash in /modelsnpm i -g cline, then pick Nemotron 3.5 Super VLnpx rea-agents setupUpdate Codex CLI (breaking and security) · OpenCode (breaking)
Nick Cammarata finds the famous cat-and-car neuron detects V shapes, and a fixed InceptionV1 has 99-100% readable last-layer neurons. A preliminary Qwen3-32B audit puts about 90% of neuron poles at one main job. The paper is still to come. → Goodfire
Read ifyou rely on sparse autoencoders: this argues you may not need them.
Unprompted, in tasks that never mention training, it reasons that a hack now could be reinforced later, so it stays honest. The authors call this the capability alignment faking needs. → LessWrong
Read ifyou train or fine-tune open models: this is what to grep your rollouts for.
In 104 hours on eight-GPU nodes, with no research advice and no teacher model, they took it from 9.2 to 48.2 on a held-out suite. Qwen's own Qwen3-4B-Instruct-2507 scores 47.9. They used known methods: fine-tuning, RL, weight merges, self-distillation. → arXiv
Read ifyou track automated AI research: it is a measured, small-scale case.
From June to September, merged PRs more than doubled while the share reverted fell over 40%. Six layers of checks, a fresh-context subagent review, owner-written REVIEW.md files and fast rollback replace line-by-line human reading. → Claude
Read ifyour team's review queue has become the bottleneck.
Small numeric differences between inference and training kernels quietly corrupt RL. On DeepSeek-V4-Flash, the difference stayed exactly 0 over 100 steps; on a Nemotron run, reward held at 0.56 to 0.59 where the baseline fell to 0.22. → vLLM
Read ifyou run RL on open models and see rewards collapse.
David Bau's group proposes realism scores: keep the base model's capabilities and keep its reasoning and activations natural. Higher realism tracks better recovery of planted behaviour. Advice: prefer DPO to SFT, skip chat data, merge back towards the base. → arXiv
Read ifyou validate interp or monitoring tools on fine-tuned test models.
New MLX releases link Macs with RDMA over Thunderbolt so tensor-parallel inference scales across machines. Apple compares M5 Ultra with M3 Ultra on Muse Spark Open, Mistral Medium 3.5 and Qwen 3.8, with time to first token and power draw. → Apple
Read ifyou are pricing a Mac cluster for frontier-size open models.