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Data

Data as of October 25, 2026 · How the score is built

Superseded.Poolside has newer models in this line:Laguna XS 2.1
SupersededOpen WeightReasoning

Released Apr 28, 2026256K context

Laguna XS.2

Decision readingLaguna XS.2 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

Self-hosted; infrastructure cost varies

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 #119. 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 scores, ranks, weighting, benchmark coverage, and evidence status
CategoryScore
AgenticRank Not rankedWeight 22%2 benchmarksVerified
20.3
CodingRank #119 of 153Percentile 22ndWeight 20%6 benchmarksVerified
22.0
ReasoningWeight 17%0 benchmarksNot measured
Not measured
MultimodalWeight 12%0 benchmarksNot measured
Not measured
KnowledgeRank Not rankedWeight 12%2 benchmarksVerified
28.0
MultilingualWeight 7%0 benchmarksNot measured
Not measured
Inst. FollowingWeight 5%0 benchmarksNot measured
Not measured
MathWeight 5%0 benchmarksNot measured
Not measured

10 of 766 tracked benchmark slots have displayable evidence · bars run 0–100

Coverage details

How 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.

  1. Agentic2/2 verified
  2. Coding6/6 verified
  3. ReasoningNot measured
  4. MultimodalNot measured
  5. Knowledge2/2 verified
  6. MultilingualNot measured
  7. Inst. FollowingNot measured
  8. MathNot measured
Verified sourceProvisionalNot 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
Coding benchmark values, best verified comparison, weight, and source status
SWE-bench ProScore46.3%Versus best verified row

Best verified: Claude Opus 5.5 · 89.9%

Gap43.6 behindWeight26% ref. weight
LiveCodeBench (Vals)LiveCodeBench, Vals AI runScore67.8%Versus best verified row

Best verified: Claude Fable 5.1 · 90.5%

Gap22.7 behindWeight8% ref. weight
SWE MultilingualScore57.7%Versus best verified row

Best verified: Claude Opus 5.5 · 93.9%

Gap36.2 behindWeight5% ref. weight
Terminal-Bench 2.0Score35.7%Versus best verified row

Best verified: GPT-5.5 · 82.0%

Gap46.3 behindWeightScored in Agentic
SWE-bench VerifiedSoftware Engineering Benchmark VerifiedScore69.9%Versus best verified row

Best verified: Claude Opus 5 · 96%

Gap26.1 behindWeightDisplay only
SWE-bench (Vals)SWE-bench, Vals AI runScore55.2%Versus best verified row

Best verified: Claude Opus 5 · 97.0%

Gap41.8 behindWeightDisplay only
Agentic2 rows
Agentic benchmark values, best verified comparison, weight, and source status
Terminal-Bench 2.0Score35.7%Versus best verified row

Best verified: GPT-5.5 · 82%

Gap46.3 behindWeight7% ref. weight
Terminal-Bench 2.1 (Vals)Terminal-Bench 2.1, Vals AI runScore25.8%Versus best verified row

Best verified: GPT-6 Astra · 87.3%

Gap61.5 behindWeight4% ref. weight
Knowledge2 rows
Knowledge benchmark values, best verified comparison, weight, and source status
MMLU-Pro (Vals)MMLU-Pro, Vals AI runScore69.1%Versus best verified row

Best verified: Claude Fable 5.1 · 92.4%

Gap23.3 behindWeight6% ref. weight
GPQA Diamond (Vals)GPQA Diamond, Vals AI runScore55.1%Versus best verified row

Best verified: Gemini 3.1 Pro · 95.5%

Gap40.4 behindWeight2% ref. weight

Bars run 0–100; the dark tick marks the best source-verified value

All 10 rows

Lineage

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.

  1. Apr 28, 2026 · you are here

    Laguna XS.2

    Not publicly ranked · Price not listed

  2. Jul 2, 2026

    Laguna XS 2.1

    Not publicly ranked · $0.1 / $0.2

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 sourced yet
Availability
Open weights are available on Hugging Face under Apache 2.0, and hosted inference is available through Poolside API and OpenRouter.
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
Open weights available; hardware estimate not sourced
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 XS.2, but the public leaderboard excludes this profile until enough non-generated benchmark coverage is available. Only published rows appear above.

Laguna XS.2 is a open weight model with a 256K context window. It uses an explicit reasoning mode, which can improve complex problem solving while adding latency and token use.

Open weights are available on Hugging Face under Apache 2.0, and hosted inference is available through Poolside API and OpenRouter.

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. The Hugging Face Laguna XS.2 collection lists the Apache 2.0 open-weight release and quantized variants.

Laguna XS.2 sits in the Laguna family with Laguna S 2.1, Laguna M.1, 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 #119. particularly well-suited for software development and code generation tasks.

Last updated October 25, 2026. Runtime fields remain blank until a sourced snapshot exists.

Deployment options

Self-host and provider-specific paths stay separate from benchmark evidence so operating constraints are visible before a score becomes the whole decision.

Published weights are available, but BenchLM does not yet have a sourced parameter and VRAM profile for this exact model. Hardware cost estimates stay unavailable until that sizing record is complete.

Estimate VRAM from known parameters

Questions

How does Laguna XS.2 perform overall in AI benchmarks?

Laguna XS.2 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 XS.2 good for knowledge and understanding?

Laguna XS.2 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 XS.2 good for coding and programming?

Laguna XS.2 ranks #119 out of 153 eligible models for coding and programming, with a public category score of 22/100. 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 XS.2 good for agentic tool use and computer tasks?

Laguna XS.2 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.

Is Laguna XS.2 open source?

Laguna XS.2 is an open-weight model from Poolside. Its weights can be downloaded for local or hosted deployment, subject to the published license. Open weight does not automatically mean open source: training data and training code may remain private, and commercial restrictions can still apply.

Which sibling models are related to Laguna XS.2?

Laguna XS.2 belongs to the Laguna family. Related tracked variants include Laguna S 2.1, Laguna M.1, 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 XS.2 have full benchmark coverage on BenchLM?

No. Laguna XS.2 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 XS.2?

Laguna XS.2 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.

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