Laguna-M.1

Laguna M.1 is a 225B total parameter Mixture-of-Experts model with 23B activated parameters per token designed for agentic coding and long-horizon work.

[!NOTE] For more details on how we trained this model, including our Model Factory approach, post-training recipe, async off-policy agent RL, and evaluations, check out our release blog post and technical report.

Highlights

• Large sparse MoE for agentic coding: Laguna M.1 is a 70-layer MoE transformer with 225B total parameters and 23B activated parameters per token
• High-capacity expert routing: After 3 dense SwiGLU layers, Laguna M.1 uses 67 sparse MoE layers with 256 experts, top-k=16 routing and auxiliary-loss-free load balancing
• Global attention architecture: Laguna M.1 uses global attention across all layers with 64 Q-heads, 8 KV-heads and softplus attention output gating
• Native reasoning support: Interleaved thinking between tool calls with support for enabling and disabling thinking per-request
• Strong agentic benchmark performance: Laguna M.1 is competitive with state-of-the-art open-weight and frontier models on SWE-bench Verified, SWE-bench Multilingual, SWE-Bench Pro and Terminal-Bench 2.0
• Apache 2.0 license: Use and modify freely for commercial and non-commercial purposes

Model overview

• Training: pre-training, post-training and reinforcement learning stages
• Number of parameters: 225B total with 23B activated per token
• Optimizer: Muon
• Layers: 70 layers with global attention
• Experts: 256 experts with 1 shared expert; top-k=16 routing
• Dense layers: first 3 layers are dense SwiGLU; remaining 67 layers are sparse MoE
• Attention: 64 Q-heads, 8 KV-heads, head dimension 128, with softplus attention output gating
• Positional encoding: RoPE with YaRN
• Modality: text-to-text
• Context window: 262,144 tokens
• Reasoning support: interleaved thinking with preserved thinking

Benchmark results

We used the highest publicly-referenced scores for all comparison models across each benchmark. In almost all cases these were official scores published in release blog posts or equivalent, with Claude Sonnet 4.6 shown as a frontier proprietary reference of comparable model size. “-” indicates a score not reported by the model provider.

[!NOTE] All benchmarking for Laguna M.1 was completed using our pool agent harness, with a maximum of 500 steps and sandboxed execution. The same sampling parameters were used for all Laguna M.1 benchmarking: temperature=1.0 and top_k=20, with thinking mode enabled and a context length of 256K tokens. All tasks were run in their own sandbox using 8 GB RAM/2 CPUs, with the exception of Terminal-Bench 2.0, which used 48 GB RAM/32 CPUs.

Some base task images and verifiers were patched to fix infrastructure reliability issues inherent in task setup, such as rate limits on third-party dependencies in external registries used by the verifier. All four agentic benchmarks were run with patched images. We also ran a reward-hack judge post-hoc on Laguna M.1 evaluation runs and did not find significant reward hacking after joint judge review and manual review.

• SWE-bench Verified: mean pass@1 averaged over 4 runs
• SWE-bench Multilingual: mean pass@1 averaged over 4 runs
• SWE-Bench Pro: mean pass@1 averaged over 4 runs
• Terminal-Bench 2.0: mean pass@1 averaged over 4 runs; 48 GB RAM/32 CPUs

Usage

Laguna M.1 has upstream support in vLLM, SGLang, and Transformers, and TRT-LLM thanks to the support of the team at NVIDIA.

pool

pool is a lightweight terminal-based coding agent and a dual Agent Client Protocol client-server.

Download and install for macOS and Linux:

curl -fsSL https://downloads.poolside.ai/pool/install.sh | bash

Launch and Log in with Poolside to get a free API key.

pool

Use in any ACP client. Configure Zed and JetBrains automatically:

pool acp setup --editor zed|jetbrains

Feedback and issues

Submit feedback with /feedback and read the full documentation on GitHub.

Deployment

vLLM

Serve Laguna M.1 locally with vLLM and query it from any OpenAI-compatible client (see Controlling reasoning for tool calls, streaming, and reasoning extraction):

[!NOTE] Laguna support landed in vLLM via vllm-project/vllm#41129 (shared with [Laguna XS.2](https://huggingface.co/poolside/Lag