Laguna releases mixture-of-experts coding models
M.1 (225.8B parameters, 23.4B activated) and XS.2 (33.4B total, 3B activated) are MoE models trained end-to-end in a versioned Model Factory stack, competitive on SWE-bench and terminal coding tasks.
May 28, 2026
Summary
MoE architecture reduces inference cost per token while maintaining competitive performance on agentic software engineering benchmarks. XS.2's Apache 2.0 release gives builders a smaller, deployable baseline for terminal-based coding workflows.
Why it matters
MoE architecture reduces inference cost per token while maintaining competitive performance on agentic software engineering benchmarks. XS.2's Apache 2.0 release gives builders a smaller, deployable baseline for terminal-based coding workflows.
Implementation verdict
XS.2 weights are available now under Apache 2.0. Replaces closed agentic models for local deployment. Requires infrastructure to run 33.4B-parameter inference (3B activated per token is still substantial). Worth evaluating on your SWE-bench-like tasks before committing; M.1 data is technical report only, not yet open.
Sources
- 1.M.1 has $225.8$B total parameters ($23.4$B activated per token) and XS.2 has $33.4$B total ($3$B activated)
- 2.two Mixture-of-Experts foundation models built for long-horizon, agentic coding
- 3.On agentic software engineering and terminal benchmarks (SWE-bench Verified, SWE-bench Multilingual, SWE-Bench Pro, and Terminal-Bench 2.0) M.1 and XS.2 are competitive with state-of-the-art open models in their respective weight classes
- 4.Laguna XS.2 weights are released under Apache~2.0
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