When Mistral put its flagship Mistral Large 4 (Le Chonk) into public preview on October 6, it promised open weights by the end of the month. On October 29 those weights landed on Hugging Face — and they are not the preview checkpoint: Mistral says the reinforcement-learning run kept going for three more weeks, and the released model is meaningfully stronger (official post).
Three more RL weeks, numbers up across the board
Terminal-Bench 4.0 is the headline: 28.3% at preview, 41% at release — "a 13-percentage-point improvement" in Mistral's words — while output tokens dropped 30%. In Mistral's comparison chart, GLM-5.3 sits at 40%, DeepSeek V4.1 Flash 1017 at 31%, Kimi K3 at 21%. On Cognition's private FrontierCode 2.0, Le Chonk leads with 19.7%, ahead of Kimi K3 (17.3%), GLM-5.3 (17.8%) and DeepSeek V4.1 Flash (19.3%).
On the Artificial Analysis Coding Agent Index the new model scores 48%, tied with GLM-5.3, while using 47% fewer tokens per task — the efficiency angle Mistral keeps pushing: match the score, cut the inference bill. Elsewhere: BrowseComp 84.5%; 73.3% on the finance benchmark Finch (second among open models, behind Kimi K3); and 97.1% attack resistance on the B3 agent-security benchmark. Reasoning effort now ships as low/medium/high, with high as default.
What a 1T model costs to run
The architecture matches the preview: about 1T total parameters, 49B active per token (52B including embeddings and output layers), 128 routed experts with 4 active per token plus one shared expert, MLA attention with a DeepSeek Sparse Attention indexer, a 1M context window, and a 1.6B Pixtral vision encoder. Official checkpoints ship in FP8 and NVFP4, with day-one GGUF quants from llama.cpp and Unsloth.
Hugging Face ran its own hardware tests: the 2-bit quants fit on a single Apple Silicon Ultra machine with 512GB of unified RAM — on an M3 Ultra, Q2_K prefills at about 90 tok/s and generates around 15 tok/s; vLLM with the NVFP4 checkpoint requires Blackwell GPUs. The license is a modified MIT ("Mistral Ecosystem Open-Weight License"): free to use, modify, fine-tune and commercialize, except that Model-as-a-Service businesses need a separate commercial license. API pricing is $0.70/M input, $0.07/M cached input and $2.10/M output.
Two caveats
First, "best open-weight model for coding" is Mistral's own framing: the Terminal-Bench and FrontierCode leads hold in its charts, but on the AA Intelligence Index v5 it scores 26 — the highest among Western open-weight models, yet only fifth among open weights overall, with Chinese models ahead on the aggregate. Second, the cybersecurity story needs unpacking: its 51% on the overall Cyber Index trails DeepSeek V4.1 Flash (57%) and MiMo-V2.6-Pro (56%). The real differentiator is "defense without refusals": legitimate reproduce-and-patch work does not trip safety filters, malicious cyber requests still get refused 96.6% of the time, and the release comes with endorsements from Booz Allen, Cloudflare and Orange Cyberdefense plus a European-sovereignty pitch.
The signal worth keeping: an open-weight flagship that keeps improving after its announcement, with the gains landing exactly where they hit your bill — token efficiency and deployment threshold. If you track inference costs, a model that ties GLM-5.3 at 48% while spending 47% fewer tokens deserves a run through your own evals.