Data as of October 25, 2026 · How the score is built
Released Apr 28, 2026256K context
Laguna M.1
Decision readingLaguna M.1 is tracked, but not publicly ranked yet. The profile exposes 10 sourced benchmark rows and leaves unsupported fields blank until a published record exists.
Released Apr 28, 2026 — see all recent releases
Decision snapshot
Each value carries a field reference instead of floating alone. Markers compare this model with the current ranked and priced catalog; they are not absolute quality thresholds.
Capability
Unranked
field median 50Not eligible for a public rank
Price
API rate not published
input median $0.75No comparable first-party hosted token rate
Speed
Not measured
field median 96 tok/sTime to first token not measured
Context
256Ktokens
field median 256,000Reported for this model; direct source link not stored
Strongest published evidence
Coding ranks #109. Particularly well-suited for software development and code generation tasks.
Validate before choosing
10 published rows leave some tracked benchmark slots empty. No comparable first-party API token rate is published.
Source-linked · 10 displayable benchmark rows
Category score record
Scores and ranks appear only where published evidence can be displayed. The table keeps the score, weight, cohort, and evidence state together.
| Category | Score | Rank | Percentile | Weight | Benchmarks | Evidence |
|---|---|---|---|---|---|---|
| AgenticRank Not rankedWeight 22%2 benchmarksVerified | 29.9 | 2 benchmarks | Verified | |||
| CodingRank #109 of 153Percentile 29thWeight 20%6 benchmarksVerified | 25.9 | 6 benchmarks | Verified | |||
| ReasoningWeight 17%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| MultimodalWeight 12%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| KnowledgeRank Not rankedWeight 12%2 benchmarksVerified | 28.0 | 2 benchmarks | Verified | |||
| MultilingualWeight 7%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| Inst. FollowingWeight 5%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| MathWeight 5%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured |
10 of 766 tracked benchmark slots have displayable evidence · bars run 0–100
Coverage detailsHow much of this is verified
Coverage is split by category so a strong number never hides a thin evidence base. Verified means the row is tied to a published source; provisional rows remain visible but separate.
- Agentic2/2 verified
- Coding6/6 verified
- ReasoningNot measured
- MultimodalNot measured
- Knowledge2/2 verified
- MultilingualNot measured
- Inst. FollowingNot measured
- MathNot measured
Benchmark ledger
Coding opens by default. The marker compares each value with the best source-verified result in the catalog; provisional leaders do not set the reference. Expand the remaining categories for every published row.
Coding6 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| SWE-bench Pro | Score49.2% | Versus best verified row Best verified: Claude Opus 5.5 · 89.9% | Gap40.7 behind | Weight26% ref. weight | Provider exact |
| LiveCodeBench (Vals)LiveCodeBench, Vals AI run | Score68.1% | Versus best verified row Best verified: Claude Fable 5.1 · 90.5% | Gap22.4 behind | Weight8% ref. weight | |
| SWE Multilingual | Score63.1% | Versus best verified row Best verified: Claude Opus 5.5 · 93.9% | Gap30.8 behind | Weight5% ref. weight | Provider exact |
| Terminal-Bench 2.0 | Score45.8% | Versus best verified row Best verified: GPT-5.5 · 82.0% | Gap36.2 behind | WeightScored in Agentic | Provider exact |
| SWE-bench VerifiedSoftware Engineering Benchmark Verified | Score74.6% | Versus best verified row Best verified: Claude Opus 5 · 96% | Gap21.4 behind | WeightDisplay only | Provider exact |
| SWE-bench (Vals)SWE-bench, Vals AI run | Score57.6% | Versus best verified row Best verified: Claude Opus 5 · 97.0% | Gap39.4 behind | WeightDisplay only | Verified |
Agentic2 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| Terminal-Bench 2.0 | Score45.8% | Versus best verified row Best verified: GPT-5.5 · 82% | Gap36.2 behind | Weight7% ref. weight | Provider exact |
| Terminal-Bench 2.1 (Vals)Terminal-Bench 2.1, Vals AI run | Score34.1% | Versus best verified row Best verified: GPT-6 Astra · 87.3% | Gap53.2 behind | Weight4% ref. weight |
Knowledge2 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| MMLU-Pro (Vals)MMLU-Pro, Vals AI run | Score68.8% | Versus best verified row Best verified: Claude Fable 5.1 · 92.4% | Gap23.6 behind | Weight6% ref. weight | Verified |
| GPQA Diamond (Vals)GPQA Diamond, Vals AI run | Score27.0% | Versus best verified row Best verified: Gemini 3.1 Pro · 95.5% | Gap68.5 behind | Weight2% ref. weight |
Bars run 0–100; the dark tick marks the best source-verified value
All 10 rowsLineage
The sequence follows explicit supersedes links. Each score is estimated for that model; a relative can inform a sparse estimate but never sets a floor, so a newer release can score below an earlier one. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.
Spec sheet
Each documented value carries its source. Missing fields stay visible as not sourced or not published, rather than disappearing from the page.
- API model ID
- Not published
- Context window
- 256K
- Maximum output
- Not sourced yet
- Knowledge cutoff
- Not sourced yet
- Input modalities
- Not sourced yet
- Output modalities
- Not sourced yet
- Parameters
- Not disclosed by the provider
- Availability
- Available through Poolside API and OpenRouter. Poolside describes Laguna M.1 as a closed-weight flagship coding-agent model.
- Cloud regions
- Not tracked yet
- Lifecycle
- Superseded
- API capabilities
- Tool calling, structured outputs, and batch support are not tracked yet
- Prompt caching
- Not documented in the pricing record
- Self-host
- Weights are not published
- Rate limits
- Not tracked yet
How to read this profile
The visual layer above carries the decisions. These notes preserve the model, ranking, coverage, and family context behind the numbers.
We track Laguna M.1, but the public leaderboard excludes this profile until enough non-generated benchmark coverage is available. Only published rows appear above.
Laguna M.1 is a proprietary model with a 256K context window. It uses an explicit reasoning mode, which can improve complex problem solving while adding latency and token use.
Available through Poolside API and OpenRouter. Poolside describes Laguna M.1 as a closed-weight flagship coding-agent model.
Official exact-value snapshot from Poolside's May 26, 2026 Laguna M.1/XS.2 technical report. BenchLM maps SWE-bench Verified, SWE-Bench Pro, and Terminal-Bench 2.0 into the weighted schema; SWE-bench Multilingual is stored as a provider display row because it is not currently a weighted core benchmark.
Laguna M.1 sits in the Laguna family with Laguna S 2.1, Laguna XS.2, Laguna XS 2.1. 10 of 766 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.
Its strongest eligible category is Coding at #109. particularly well-suited for software development and code generation tasks.
Last updated October 25, 2026. Runtime fields remain blank until a sourced snapshot exists.
Questions
How does Laguna M.1 perform overall in AI benchmarks?
Laguna M.1 has 10 source-displayable benchmark rows, but it does not qualify for a public overall rank. The available rows remain visible by category without being converted into a site-wide score. Missing evidence stays blank instead of being estimated from an earlier model.
Is Laguna M.1 good for knowledge and understanding?
Laguna M.1 has source-displayable benchmark coverage for knowledge and understanding, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.
Is Laguna M.1 good for coding and programming?
Laguna M.1 ranks #109 out of 153 eligible models for coding and programming, with a public category score of 25.9/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
Is Laguna M.1 good for agentic tool use and computer tasks?
Laguna M.1 has source-displayable benchmark coverage for agentic tool use and computer tasks, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.
Which sibling models are related to Laguna M.1?
Laguna M.1 belongs to the Laguna family. Related tracked variants include Laguna S 2.1, Laguna XS.2, Laguna XS 2.1. A sibling link indicates shared lineage or a documented configuration relationship; it does not mean the variants have identical pricing, context limits, benchmark evidence, or deployment behavior. Compare before switching.
Does Laguna M.1 have full benchmark coverage on BenchLM?
No. Laguna M.1 currently has 10 source-displayable rows. The profile exposes published, non-generated evidence and leaves missing categories blank until an exact evaluation is available. Coverage describes how much was measured; it is not a penalty added to an individual benchmark result.
What is the context window size of Laguna M.1?
Laguna M.1 has a reported context window of 256K in the exact-model catalog record. The value stays visible, but the profile marks its source link as unavailable instead of presenting it as directly documented. Maximum output length remains separate because providers often publish a different limit.