
Absolute Zero,ExpeL Research @MistralAI Ex. @Tsinghua_Uni @MSFTResearch,@ BIGAI Opinions are my own or gpt's
Mountain View, CA
Joined September 2020
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Andrew Zhao retweeted
Announcing Artificial Analysis Intelligence Index v5, featuring 2 new benchmarks and 3 upgrades. For the first time, the majority of the Index is built on private datasets
The Artificial Analysis Intelligence Index is our composite measure of model capabilities across tasks reflecting real-world usage today. v5 is our biggest upgrade since v4 launched in January. With v5, 60% of the Index is now based on evaluations with private test sets or held-out answers.
Alongside Intelligence Index v5, we’re launching the Artificial Analysis Progress Index v1, which tracks model capability over time on one continuous scale.
New to the Intelligence Index:
➤ FrontierCode 2.0: Cognition's newly-launched private benchmark of real-world coding tasks, graded on whether a repository maintainer would merge the agent's change
➤ Terminal-Bench-Science: 70 agentic scientific research tasks, run in a terminal environment
Upgraded:
➤ HLE-Diamond (replacing Humanity's Last Exam): a refined 1,000-question version of Humanity's Last Exam, split evenly between reasoning and knowledge questions
➤ AA-Omniscience v1.1: An updated dataset with saturated and public set questions removed, new questions added, and ambiguous or erroneous questions revised
➤ CritPt v2: a private, 40-question hard subset of CritPt v2, revised by the CritPt authors and run with a code-execution tool
Staying in the Index: AA-Briefcase, AutomationBench-AA, GDP.pdf and Terminal-Bench 4.0
Removed from the Index: GDPval-AA v2.1, SciCode, and AA-LCR v1.1
Key results:
➤ Claude Opus 5.5 (max) leads at 58, ahead of GPT-6 Astra (max, 57) and GPT-6.1 Sol (max, 55): Anthropic and OpenAI hold the top five models. Gemini 4 Argon (high) is next at 50
➤ The open weights gap widens from 3 points to 12: Kimi K3.1 Pro (weights not yet released) is the top open weights model at 46. Among released weights, DeepSeek V4.1 Flash 1017 (max) and GLM-5.3 (max) lead at 32 - 26 points behind the leader and below Claude Haiku 5.5 (max, 36)
➤ Cost per task varies by more than 13x among the leading models: of the 10 highest-scoring models shown, GPT-6.1 Terra (max) is the cheapest at $0.96 per Intelligence Index task and Claude Fable 5.1 (max) the most expensive at $13.04
Andrew Zhao retweeted
Introducing Fern, Mirendil’s first model: pretrained from scratch, with up to 10M tokens of context!
That’s ~0.5M lines of code (roughly Megatron-LM scale) or days of an agent’s work, all in context at once.
Its first version, Fern 0.1, had a short round of post-training 🚢
Andrew Zhao retweeted
Kimi K3.1 Pro launches at 4th on the Artificial Analysis Intelligence Index scoring 55, making large gains in agentic performance; it will be the leading open-weights model once its weights are published
@Kimi_Moonshot's new reasoning model gains 11 points over Kimi K3 (max, 44). It trails only Claude Opus 5.5 (max with fallback, 58), Claude Sonnet 5.5 (max with fallback, 56), and Muse Wonder Full (max, 56) on the Intelligence Index.
Once the weights for K3.1 Pro are released it will be the highest-scoring open weights model and the gap between the leading open weights and proprietary models on the Intelligence Index will narrow from 12 points to 3.
Key results:
➤ Large gains in agentic performance: Kimi K3.1 Pro scores 3rd on GDPval-AA with 1,846 Elo, up 312 Elo from Kimi K3, behind only Claude Opus 5.5 (1,866 Elo) and Muse Wonder Full (1,857 Elo). Kimi K3.1 Pro also achieves the highest score of any model on Terminal-Bench 4.0. K3.1 Pro scores 67%, up from 13% for Kimi K3 and ahead of Claude Sonnet 5.5 (64%). AutomationBench-AA, which tests agentic SaaS workflows, rises from 58% to 73%.
➤ Gains in other evals are more moderate: Humanity's Last Exam rises from 47% to 52%, CritPt from 23% to 25%, and SciCode from 59% to 61%. AA-Omniscience Index rises from 20 to 30, as the hallucination rate falls from 53% to 44% and accuracy rises from 48% to 51%.
➤ Cost per Task roughly equal to Kimi K3 at $1.98 per Task: K3.1 Pro uses 55K output tokens per Intelligence Index task, 14% more than Kimi K3 at 48K, while its first-party token prices are 33% lower.
Additional model details:
➤ Context window: 1M tokens.
➤ Size: 2.8T total, 104B active parameters
➤ Input modalities: Text and image
➤ Weights and license: Weights currently not published, Moonshot is expected to release the weights soon after launch
➤ Pricing: $2/$10/$0.20 per 1M input/output/cache hit tokens, 33% lower than Kimi K3 on all three rates.
Congratulations to the team at @Kimi_Moonshot on the release!
Andrew Zhao retweeted
Introducing Kimi K3.1 Pro: our most capable 3T-class open-weight model.
🔹 1M-token context, 2.8T total / 104B active params (MoE), Multimodal.
🔹 Full-project coding: games in Unreal, Unity & Godot.
🔹 Excels at parametric 3D modeling in Blender & CAD.
🔹 More reliable at finance work: Accurate modeling with hallucination down by 65%.
🔹 Matching the strongest proprietary models across coding, agentic and knowledge benchmarks.
Kimi K3.1 Pro and K3.1 Pro Highspeed (2–3× faster) are now live on Kimi.ai, Kimi Work, Kimi Code, and the Kimi API.
Open weights by Nov 4, 2026.
🔗 API: platform.kimi.ai
🔗 Tech blog: kimi.ai/blog/kimi-k3-1-pro
Andrew Zhao retweeted
Recursive self-improvement needs agents that can build their own training environments. But researchers still part of the loop, making it hard to scale.
We introduce AutoEnvScaling, which turns Data Flywheel into a terminal task: a proposer agent builds complete Harbor tasks and sandbox. It builds just like human researchers. This enables self-play RL for terminal agents: one model improves as both builder and solver, without human-in-the-loop.
Andrew Zhao retweeted
pretty interesting document.
Anthropic released their 2021 research roadmap earlier this year (because of copyright litigation); I think some people might find it interesting: i.sm4.ca/concise-research-ro…
Andrew Zhao retweeted
🇫🇷 Le Chonk joins the sudo L7 leaderboard 🇫🇷
We just added @MistralAI's Mistral Large 4 to sudo L7, our benchmark for staff-level software engineering.
Mistral Large 4 scores 13.3%, ahead of Muse Spark 1.3 (12.2%) and Grok 4.7 (10.6%).
sudo L7 tests whether coding agents can go beyond writing correct code to exercise the judgment of experienced engineers: navigating messy systems, spotting hidden risks, and knowing when technically correct code is still the wrong thing to ship.
Allez, le Chonk ! 🥖
Leaderboard ↓
surgehq.ai/benchmarks/sudo-l…
Andrew Zhao retweeted
Mistral large 4 btw 🤷♂️
Andrew Zhao retweeted
L’annonce importante de Mistral hier c’est moins leur nouveau modèle Large 4 en tant que tel que le fait qu’ils vont accélérer la sortie de nouveaux modèles avec désormais une nouvelle architecture technique qui leur permet de faire mieux, plus vite, sur ses propres serveurs.
le chonk wasnt trained on these kind of animations, cant believe its generalizing this well
Le Chonk enters the Frontier race 🐈
We put @MistralAI Large 4, MiMo V2.6 Pro, Opus 5.5 and GPT-6.1 Sol side by side with voxel, ASCII, Blender and SVG remakes of the launch clip
Run AI models locally and via API -> atomic.chat
Andrew Zhao retweeted
huge congrats, this is a really nice model, on par with frontier oss models on benchmarks, and leading with very high score on cyber
full benchmark results:
Meet Mistral Large 4, aka Le Chonk.
• 1T parameters, natively multimodal. 49B active.
It is the best open weights model from US or Europe on aggregated benchmarks.
• State-of-the-art on critical workloads, including cyber defense, manufacturing and finance and it surpasses closed frontier models on visual grounding.
• Forged in Europe end-to-end and is deployable from Europe via our own Mistral Cloud infrastructure.
• Available to all via API today. Working with cybersecurity partners privately.
Open weights release end of October.
Andrew Zhao retweeted
When everyone said Le Chaton fat was just a joke but @MistralAI makes him real.
Andrew Zhao retweeted
hosting the Self-Improving Agents social at @COLM_conf tomorrow
- 4 talks: @sethkarten @yoonholeee @qinyuan_ye and Noah Provenzano
- panel: @HuaxiuYaoML @ZhiruoW @jasonwu0731
my 4th one after ICLR, ICML and NeurIPS, and I still love hosting these.
1-2:30pm, Franciscan D. come by if you're at COLM
luma.com/m8v4fq76
Andrew Zhao retweeted
Mistral AI Releases Mistral Large 4 (Le Chonk): A 1.05T Parameter Multimodal MoE Model
Mistral Large 4, nicknamed Le Chonk, went into public preview today. 1.05T total parameters, 49B active per token, 1M context, native image input. Trained from scratch on 3,800 NVIDIA Grace Blackwell GPUs in Mistral's own European datacenters.
Here is what stood out to me after reading the release and the docs:
→ Only about 4.7% of the weights fire per token
→ That is how a 1T class model serves at $1.36 per 1M input and $4.18 per 1M output tokens
→ The full 1.05T still has to sit in memory, so plan hardware around total, not active
→ 93% on Cybench, 82% on CyberGym-E2E, top 5 on the Artificial Analysis Cyber Index
→ 61.7% DeepSWE v1.1, 28.3% Terminal-Bench 4.0, 49.8% on the Coding Agent Index
→ API is live today as mistral-large-4
I broke down the architecture, benchmarks and a full comparison against DeepSeek V4 Pro, Kimi K3 and GLM-5.3, with an interactive explainer.
Here is the full analysis: marktechpost.com/2026/10/06/…
Technical details: mistral.ai/news/mistral-larg…
@MistralAI @MistralDevs
Andrew Zhao retweeted
Le Chonk enters the Frontier race 🐈
We put @MistralAI Large 4, MiMo V2.6 Pro, Opus 5.5 and GPT-6.1 Sol side by side with voxel, ASCII, Blender and SVG remakes of the launch clip
Run AI models locally and via API -> atomic.chat
Meet Mistral Large 4, aka Le Chonk.
• 1T parameters, natively multimodal. 49B active.
It is the best open weights model from US or Europe on aggregated benchmarks.
• State-of-the-art on critical workloads, including cyber defense, manufacturing and finance and it surpasses closed frontier models on visual grounding.
• Forged in Europe end-to-end and is deployable from Europe via our own Mistral Cloud infrastructure.
• Available to all via API today. Working with cybersecurity partners privately.
Open weights release end of October.
































