What are the best alternatives to Leanstral?

The best alternatives to Leanstral include GitHub Copilot, Claude Opus 4.8, Devstral 2. Each offers unique features for different use cases.

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Leanstral

Mistral's open-source Lean 4 proof agent — formal verification at low cost

freemiumDR 87Free API endpoint at launch. Available in Mistral Vibe (zero-setup). Open weights under Apache 2.0 for self-hosting.View full pricing →

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About Leanstral

Leanstral is Mistral AI's open-source code agent purpose-built for Lean 4, the proof assistant used for formal verification of mathematics and mission-critical software. Released March 16, 2026, it's a 120B-parameter sparse MoE model with 6B active parameters — designed to operate in realistic formal repositories, not just isolated math competition problems. Apache 2.0 license, free API endpoint, and integrated into Mistral Vibe for zero-setup use.

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Key Features

✓120B total parameters, 6B active (sparse MoE) — efficient inference at scale

✓Apache 2.0 open weights — full self-hosting permitted

✓Free API endpoint via Mistral La Plateforme

✓Zero-setup integration in Mistral Vibe via /leanstall command

✓MCP support: trained to maximize performance with lean-lsp-mcp

✓FLTEval benchmark: 26.3 at pass@2 — beats Claude Sonnet 4.6 (23.7) at 1/15th the cost

✓At pass@16, reaches 31.9 — beats Sonnet by 8 points and Haiku by 8.9 points

✓Outperforms Qwen3.5-397B-A17B and Kimi-K2.5-1T-A32B despite far fewer active parameters

✓New FLTEval evaluation suite for real proof engineering (FLT project PRs), not just competition math

Leanstral Pros & Cons

✅ Pros

+Best open-source model for Lean 4 formal verification — first purpose-built agent for the task
+Extreme cost efficiency: pass@2 beats Claude Sonnet at $36 vs $549 (15× cheaper)
+Apache 2.0 license — no usage restrictions for commercial or research use
+Sparse MoE architecture keeps inference costs low despite 120B total parameter count
+Works in realistic proof engineering repos (FLT project), not just toy math problems
+MCP-native: trained for lean-lsp-mcp, pluggable with arbitrary MCPs via Mistral Vibe

⚠️ Cons

−Highly specialized: only useful for Lean 4 formal verification — not a general coding model
−Claude Opus 4.6 still leads on quality (39.6 vs 31.9) when cost is not a constraint
−Lean 4 ecosystem is small — most developers will never have a use case for this model
−FLTEval is a new benchmark without broad community adoption yet