Model A

Poolside

—

Evidence status unavailable

90% interval unavailable

Laguna S 2.1 vs Muse Spark

Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B

Muse Spark

Which one for your work

Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.

Long documents
Prompts that approach the documented context limit
Laguna S 2.1
Laguna S 2.1 has the larger documented context window.
Confidence: documented

Show secondary and unsupported calls

Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Laguna S 2.1 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Laguna S 2.1 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
Chat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Repository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

4 Laguna S 2.1 only

2 Shared

22 Muse Spark only

Shared results: 2
Laguna S 2.1 only: 4
Muse Spark only: 22
Like-for-like categories: 0 / 8

2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.

Agentic

Directional only

Laguna S 2.1: 48.2; Estimated · #77/151
Muse Spark: 58.8; Supported · #31/151
Basis: BenchAlign lane · 2 vs 5 public rows
Reading: Directional only

Coding

Directional only

Laguna S 2.1: 47.8; Estimated · #87/183
Muse Spark: 59.2; Supported · #28/183
Basis: BenchAlign lane · 4 vs 4 public rows
Reading: Directional only

Reasoning

Not comparable

Laguna S 2.1: Not ranked
Muse Spark: 45.9; Unranked · 3 rankable rows
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Knowledge

Not comparable

Laguna S 2.1: Not ranked
Muse Spark: 65.6; Supported · #23/181
Basis: BenchAlign lane · 0 vs 5 public rows
Reading: Not comparable

Math

Not comparable

Laguna S 2.1: Not ranked
Muse Spark: 55.3; Unranked · 2 rankable rows
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Laguna S 2.1: Not ranked
Muse Spark: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Laguna S 2.1: Not ranked
Muse Spark: 76.8; #14/48
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Laguna S 2.1: Not ranked
Muse Spark: 92.9; #8/120
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Laguna S 2.1 and Muse Spark
Category	Laguna S 2.1	Muse Spark	Basis	Reading
Agentic	48.2Estimated · #77/151	58.8Supported · #31/151	Directional onlyBenchAlign lane · 2 vs 5 public rows	Directional only
Coding	47.8Estimated · #87/183	59.2Supported · #28/183	Directional onlyBenchAlign lane · 4 vs 4 public rows	Directional only
Reasoning	Not ranked	45.9Unranked · 3 rankable rows	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Knowledge	Not ranked	65.6Supported · #23/181	Not comparableBenchAlign lane · 0 vs 5 public rows	Not comparable
Math	Not ranked	55.3Unranked · 2 rankable rows	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multimodal	Not ranked	76.8#14/48	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable
Instruction following	Not ranked	92.9#8/120	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Shape of the matched evidence

Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.

Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.

Terminal-Bench 2.0
Agentic
Laguna S 2.1: 70.2%Muse Spark: 59%Normalized gap 11.2Laguna S 2.1 source Muse Spark source
SWE-bench Pro
Coding
Laguna S 2.1: 59.4%Muse Spark: 52.4%Normalized gap 7.0Laguna S 2.1 source Muse Spark source

What each workload costs

Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.

Chat turn

1K fresh input + 500 output tokens

Laguna S 2.1: $0.0002; Fits in one request
Muse Spark: API rate not published; Fits in one request

Muse Spark has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Laguna S 2.1: $0.0056; Fits in one request
Muse Spark: API rate not published; Fits in one request

Muse Spark has no comparable published API token rate.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

Laguna S 2.1: $0.006; Fits in one request
Muse Spark: API rate not published; Fits in one request; Cached-input rate unavailable

Muse Spark has no comparable published API token rate.

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

SpecificationLaguna S 2.1Muse Spark

Context window

Maximum documented context; output-token limits may be lower.

Laguna S 2.1

Muse Spark

262K

API model ID

Laguna S 2.1

Not sourced

Muse Spark

Not sourced

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

Laguna S 2.1

$0.01 per 1M cached input tokens

Muse Spark

No comparable hosted API rate

Documented inputs

Laguna S 2.1

Not sourced

Muse Spark

Not sourced

Documented outputs

Laguna S 2.1

Not sourced

Muse Spark

Not sourced

Provider availability

Laguna S 2.1

Not sourced

Muse Spark

Not sourced

Reasoning profile

Laguna S 2.1

Reasoning

Muse Spark

Reasoning

Weight access

Laguna S 2.1

Open Weight

Muse Spark

Proprietary

License

Laguna S 2.1

Open Weight

Muse Spark

Proprietary

Release date

Laguna S 2.1

2026-07-21

Muse Spark

2026-04-08

If you already use one of these models

Deployment change: The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
Quality signal: At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: Laguna S 2.1 has the larger documented window (1M).

Run the same representative tasks against both endpoints before changing production traffic.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence28 rows

Agentic

Terminal-Bench 2.0
Laguna S 2.170.2%
Source
Muse Spark59%
Source
Laguna S 2.1 leads this result
Toolathlon-Verified
Laguna S 2.149.7%
Source
Muse Spark—
Not directly comparable
τ²-bench results
Laguna S 2.1—
Muse Spark91.5%
Source
Not directly comparable
DeepSearchQA
Laguna S 2.1—
Muse Spark74.8%
Source
Not directly comparable
CyberGym
Laguna S 2.1—
Muse Spark43.5%
Source
Not directly comparable
Claw-Eval
Laguna S 2.1—
Muse Spark63.8%
Source
Not directly comparable

Coding

Terminal-Bench 2.0
Laguna S 2.170.2%
Source
Muse Spark—
Not directly comparable
SWE Multilingual
Laguna S 2.178.5%
Source
Muse Spark—
Not directly comparable
SWE-bench Pro
Laguna S 2.159.4%
Source
Muse Spark52.4%
Source
Laguna S 2.1 leads this result
deepSwe
Laguna S 2.140.4%
Source
Muse Spark—
Not directly comparable
SWE-bench Verified
Laguna S 2.1—
Muse Spark77.4%
Source
Not directly comparable
LiveCodeBench Pro
Laguna S 2.1—
Muse Spark80.0%
Source
Not directly comparable
Vibe Code Bench
Laguna S 2.1—
Muse Spark19.67%
Source
Not directly comparable

Reasoning

ARC-AGI-2
Laguna S 2.1—
Muse Spark42.5%
Source
Not directly comparable

Knowledge

GPQA-D
Laguna S 2.1—
Muse Spark89.5%
Source
Not directly comparable
HLE
Laguna S 2.1—
Muse Spark50.4%
Source
Not directly comparable
HLE w/o tools
Laguna S 2.1—
Muse Spark42.8%
Source
Not directly comparable
HealthBench Hard
Laguna S 2.1—
Muse Spark42.8%
Source
Not directly comparable
MedXpertQA (Text)
Laguna S 2.1—
Muse Spark52.6%
Source
Not directly comparable

Math

FrontierMath v2 (Tiers 1-3)
Laguna S 2.1—
Muse Spark39.000%
Source
Not directly comparable
FrontierMath v2 (Tier 4)
Laguna S 2.1—
Muse Spark14.600%
Source
Not directly comparable

Multimodal

CharXiv
Laguna S 2.1—
Muse Spark86.4%
Source
Not directly comparable
MMMU-Pro
Laguna S 2.1—
Muse Spark80.4%
Source
Not directly comparable
ERQA
Laguna S 2.1—
Muse Spark64.7%
Source
Not directly comparable
SimpleVQA
Laguna S 2.1—
Muse Spark71.3%
Source
Not directly comparable
ScreenSpot Pro
Laguna S 2.1—
Muse Spark84.1%
Source
Not directly comparable
ZeroBench
Laguna S 2.1—
Muse Spark33.0%
Source
Not directly comparable
MedXpertQA (MM)
Laguna S 2.1—
Muse Spark78.4%
Source
Not directly comparable

Frequently asked questions

Which is better, Laguna S 2.1 or Muse Spark?

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Laguna S 2.1 or Muse Spark?

Muse Spark scores higher for coding on the public lane, 59.2 to 47.8. Laguna S 2.1 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, Laguna S 2.1 or Muse Spark?

Muse Spark scores higher for agentic tasks on the public lane, 58.8 to 48.2. Laguna S 2.1 is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, Laguna S 2.1 or Muse Spark?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, Laguna S 2.1 or Muse Spark?

Laguna S 2.1 has the larger documented context window: 1M, compared with 262K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 4, 2026

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Laguna S 2.1 vs Muse Spark

Which one for your work

Long documents

Coding work

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

What is actually comparable

Category results, on a stated basis

Agentic

Coding

Reasoning

Knowledge

Math

Multilingual

Multimodal

Instruction following

Shape of the matched evidence

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

Benchmark evidence

Agentic

Coding

Reasoning

Knowledge

Math

Multimodal

Frequently asked questions

Which is better, Laguna S 2.1 or Muse Spark?

Which is better for coding, Laguna S 2.1 or Muse Spark?

Which is better for agentic tasks, Laguna S 2.1 or Muse Spark?

Which costs less, Laguna S 2.1 or Muse Spark?

Which has the larger context window, Laguna S 2.1 or Muse Spark?

Related comparisons

Watch Laguna S 2.1 vs Muse Spark