Mistral AI、Apache-2.0ライセンスのLean 4用コードエージェント「Leanstral 1.5」を公開しPutnamBenchで672問中587問を解決

Today, Mistral AI released Leanstral 1.5. It is a code agent model built for Lean 4. The release targets automated theorem proving and proof engineering. Weights are open under Apache 2.0. A free API endpoint, leanstral-1-5, is now live.

Leanstral 1.5 updates the earlier Leanstral-2603 model. It belongs to the Mistral Small 4 family.

What is Leanstral 1.5

Leanstral 1.5 is a code agent model for Lean 4, a proof assistant. A proof assistant checks every logical step mechanically. Lean 4 can express objects like perfectoid spaces and properties of Rust fragments.

The architecture is a mixture-of-experts, or MoE. An MoE routes each token to a few specialized sub-networks. This keeps compute low while total capacity stays large. Leanstral uses 128 experts, with 4 active per token.

Total size is 119B parameters, with 6.5B activated per token. Context length is 256k tokens. Input is multimodal, accepting text and image. Output is text only.

How Mistral Trained Leanstral 1.5

Training runs in three stages. These are mid-training, supervised fine-tuning, then reinforcement learning with CISPO. Two reinforcement-learning environments shaped the model’s agentic behavior.

In the multiturn environment, the model receives a theorem statement. It must prove or disprove it. It submits a proof, then reads Lean compiler feedback. It refines across attempts until it succeeds or exhausts its budget.

In the code agent environment, Leanstral works inside a raw filesystem. It edits files, runs bash commands, and uses the Lean language server. That server exposes goals, errors, and type information in real time.

This lets it complete partial proofs, build auxiliary lemmas, and persist through context compaction. Compaction compresses earlier context so long tasks still fit the window. Correctness is verified by Mistral’s fork of SafeVerify against target theorems.

Benchmarks and Performance

Mistral team reports that Leanstral 1.5 saturates miniF2F. It reaches 100% on both the validation and test sets. It solves 587 of 672 PutnamBench problems.

The model sets a new state-of-the-art on the FATE-H and FATE-X algebra benchmarks. Mistral lists 87% on FATE-H and 34% on FATE-X. On FLTEval, pass@1 rises from 21.9 to 28.9. Pass@8 rises from 31.9 to 43.2.

FLTEval is built from real pull requests to the Fermat’s Last Theorem repository. On it, Leanstral surpasses Opus 4.6’s 39.6 at one-seventh the cost. It also widens its lead over open-source models three to ten times larger. Pass@8 means eight attempts are allowed per problem.

BenchmarkLeanstral 1.5Detail

miniF2F (val + test)100%Saturated, per Mistral

PutnamBench587 / 672~$4 per problem

FATE-H87%New state-of-the-art

FATE-X34%New state-of-the-art

FLTEval pass@128.9Up from 21.9

FLTEval pass@843.2Beats Opus 4.6’s 39.6

On PutnamBench, Leanstral edges Seed-Prover 1.5 high by 7 problems. It does so at about $4 per problem. Mistral estimates Seed-Prover’s high setting near $300 or more per problem.

That setting runs a budget of 10 H20-days per problem. Mistral also compares against Goedel-Architect and AxProverBase. It notes Aleph Prover costs roughly $54 to $68 per problem.

Test-time scaling is the model’s defining behavior. Raising the token budget per attempt lifts PutnamBench Pass@8. Mistral team reports 44 solved at 50k, 244 at 200k, 493 at 1M, and 587 at 4M. The interactive explorer below lets you scrub across that same curve.

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Case Studies and Use Cases

Leanstral trained mainly on mathematics, but it also verifies code. Mistral team documents two case studies that matter for engineers.

First, Leanstral proved O(log n) time complexity for a real AVL tree implementation. AVL trees are self-balancing binary search trees. The proof used structural induction and monadic time tracking via the TimeM monad. It ran over 2.7 million tokens across 22 compactions. It established a bound near 48 steps per height unit, plus a constant.

Second, Leanstral found real bugs in open-source code. An automated pipeline used Aeneas to translate Rust into Lean. Leanstral inferred user intent and generated correctness properties. It attempted each property in four tries, then the negation in four more.

Across 57 repositories, it flagged 47 violated properties and 11 genuine bugs. Five were previously unreported on GitHub. One bug sat in the sign function for zigzag decoding in datrs/varinteger. On input Std.U64.MAX, the expression (value + 1) overflowed. That caused crashes in debug mode and silent corruption in release.

Practical use cases follow directly from these examples. Dev teams can complete partial proofs inside a repository. They can generate correctness properties for a function automatically. They can stress-test Rust code by proving or disproving inferred invariants.

Getting Started: Code and Deployment

The simplest path is Mistral Vibe, Mistral’s agent CLI. Leanstral runs on Mistral’s free plan. Enable ‘Labs models’ in your account, then create an API key.

Install Vibe, add the Lean agent, then launch it:

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1. Set up Mistral Vibe

uv tool install mistral-vibe

uv tool update mistral-vibe

vibe --setup

2. Inside vibe, install Leanstral, then leave vibe

/leanstall

exit

3. Launch the Lean agent

vibe --agent lean

For self-hosting, install vLLM 0.24.0 or newer, then serve the weights:

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Installs mistral_common >= 1.11.5 automatically

uv pip install -U vllm --torch-backend=auto

vllm serve mistralai/Leanstral-1.5-119B-A6B \

--max-model-len 200000 \

--tensor-parallel-size 4 \

--attention-backend FLASH_ATTN_MLA \

--tool-call-parser mistral \

--enable-auto-tool-choice \

--reasoning-parser mistral

Call the server through the OpenAI-compatible client. Set reasoning_effort to high for complex prompts, or none for speed:

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from openai import OpenAI

Point the OpenAI client at your vLLM server

client = OpenAI(api_key="EMPTY", base_url="<your-host-url>")

TEMP = 1.0

MAX_TOK = 32000

REASONING = "high" # switch to 'none' for faster answers

model = client.models.list().data[0].id

messages = [

{"role": "user", "content": [

{"type": "text", "text": "Define the transition rules as an inductive proposition in Lean 4."}

]},

]

response = client.chat.completions.create(

model=model,

messages=messages,

temperature=TEMP,

max_tokens=MAX_TOK,

reasoning_effort=REASONING,

)

print(response.choices[0].message.content)

print(response.choices[0].message.reasoning)

Leanstral also supports OpenAI-style tool calling. You can expose a function such as lean_run_code to compile snippets. Mistral further recommends the lean-lsp-mcp server for tighter Lean integration.

Key Takeaways

Leanstral 1.5 is a free, Apache-2.0 Lean 4 proof-engineering model.

It uses a 119B mixture-of-experts with 6.5B active parameters.

It saturates miniF2F and solves 587 of 672 PutnamBench problems.

It found 5 previously unreported bugs across open-source repositories.

Access it via Hugging Face weights, a free API, or local vLLM.

Check out the Mistral AI announcement, Leanstral 1.5 model card, and the Hugging Face.. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well.

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The post Mistral AI Releases Leanstral 1.5: An Apache-2.0 Lean 4 Code Agent Model Solving 587 of 672 PutnamBench Problems appeared first on MarkTechPost.