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BenchLM
Model A
Claude 4 Sonnet

Anthropic

41.8/100

Supported · Public rank #185

90% interval 38.6–44.9

Claude 4 Sonnet vs Muse Spark

Updated August 21, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
Muse Spark

Meta

70.6/100

Supported · Public rank #17

90% interval 61.0–80.2

Decision reading

Muse Spark has the higher public score, 70.6 versus 41.8, and the 90% score intervals do not overlap.

1 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.

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

    Muse Spark

    Muse Spark 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

    The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.

    Confidence: limited

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    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

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Claude 4 Sonnet does not fit this workload in one request. Claude 4 Sonnet has no published cached-input rate, so cached tokens use its listed input rate. Muse Spark has no comparable published API token rate.

    Confidence: rate-fallback

  • 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.

Shared results
1
Claude 4 Sonnet only
2
Muse Spark only
23
Like-for-like categories
0 / 8

1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Coding

Directional only
Claude 4 Sonnet
72.7
Muse Spark
67.8
Weighted basis
1 vs 2 rows
Reading
Directional only

Agentic

Not comparable
Claude 4 Sonnet
Not measured
Muse Spark
59.0
Weighted basis
0 vs 1 rows
Reading
Not comparable

Reasoning

Not comparable
Claude 4 Sonnet
Not measured
Muse Spark
42.5
Weighted basis
0 vs 1 rows
Reading
Not comparable

Knowledge

Not comparable
Claude 4 Sonnet
Not measured
Muse Spark
50.4
Weighted basis
0 vs 1 rows
Reading
Not comparable

Math

Not comparable
Claude 4 Sonnet
Not measured
Muse Spark
32.9
Weighted basis
0 vs 2 rows
Reading
Not comparable

Multilingual

Not comparable
Claude 4 Sonnet
Not measured
Muse Spark
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Claude 4 Sonnet
Not measured
Muse Spark
82.5
Weighted basis
0 vs 2 rows
Reading
Not comparable

Instruction following

Not comparable
Claude 4 Sonnet
Not measured
Muse Spark
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

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.

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

Claude 4 Sonnet
$0.0105
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

Claude 4 Sonnet
$0.195
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

Claude 4 Sonnet
$0.81
Does not fit in one request
Cached input priced at the published list-input rate
Muse Spark
API rate not published
Fits in one request
Cached-input rate unavailable

Claude 4 Sonnet does not fit this workload in one request. Claude 4 Sonnet has no published cached-input rate, so cached tokens use its listed input rate. 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.

Context window

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

Claude 4 Sonnet

200K

Muse Spark

262K

API model ID

Claude 4 Sonnet

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.

Claude 4 Sonnet

Not published

Muse Spark

No comparable hosted API rate

Documented inputs

Claude 4 Sonnet

Not sourced

Muse Spark

Not sourced

Documented outputs

Claude 4 Sonnet

Not sourced

Muse Spark

Not sourced

Provider availability

Claude 4 Sonnet

Not sourced

Muse Spark

Not sourced

Reasoning profile

Claude 4 Sonnet

Non-Reasoning

Muse Spark

Reasoning

Weight access

Claude 4 Sonnet

Proprietary

Muse Spark

Proprietary

License

Claude 4 Sonnet

Proprietary

Muse Spark

Proprietary

Release date

Claude 4 Sonnet

2025-05-01

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
Muse Spark has the higher public score, 70.6 versus 41.8, and the 90% score intervals do not overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Muse Spark has the larger documented window (262K).

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 evidence26 rows

Agentic

  • Gert Labs

    Claude 4 Sonnet39.66%
    Source
    Muse Spark—

    Not directly comparable

  • JobBench

    Claude 4 Sonnet18.4%
    Source
    Muse Spark—

    Not directly comparable

  • Terminal-Bench 2.0

    Claude 4 Sonnet—
    Muse Spark59%
    Source

    Not directly comparable

  • τ²-bench results

    Claude 4 Sonnet—
    Muse Spark91.5%
    Source

    Not directly comparable

  • DeepSearchQA

    Claude 4 Sonnet—
    Muse Spark74.8%
    Source

    Not directly comparable

  • CyberGym

    Claude 4 Sonnet—
    Muse Spark43.5%
    Source

    Not directly comparable

  • Claw-Eval

    Claude 4 Sonnet—
    Muse Spark63.8%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Claude 4 Sonnet72.7%
    Source
    Muse Spark77.4%
    Source

    Muse Spark leads this result

  • SWE-bench Pro

    Claude 4 Sonnet—
    Muse Spark52.4%
    Source

    Not directly comparable

  • LiveCodeBench Pro

    Claude 4 Sonnet—
    Muse Spark80.0%
    Source

    Not directly comparable

  • Vibe Code Bench

    Claude 4 Sonnet—
    Muse Spark19.67%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    Claude 4 Sonnet—
    Muse Spark42.5%
    Source

    Not directly comparable

Knowledge

  • GPQA-D

    Claude 4 Sonnet—
    Muse Spark89.5%
    Source

    Not directly comparable

  • HLE

    Claude 4 Sonnet—
    Muse Spark50.4%
    Source

    Not directly comparable

  • HLE w/o tools

    Claude 4 Sonnet—
    Muse Spark42.8%
    Source

    Not directly comparable

  • HealthBench Hard

    Claude 4 Sonnet—
    Muse Spark42.8%
    Source

    Not directly comparable

  • MedXpertQA (Text)

    Claude 4 Sonnet—
    Muse Spark52.6%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Claude 4 Sonnet—
    Muse Spark39.000%
    Source

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Claude 4 Sonnet—
    Muse Spark14.600%
    Source

    Not directly comparable

Multimodal

  • CharXiv

    Claude 4 Sonnet—
    Muse Spark86.4%
    Source

    Not directly comparable

  • MMMU-Pro

    Claude 4 Sonnet—
    Muse Spark80.4%
    Source

    Not directly comparable

  • ERQA

    Claude 4 Sonnet—
    Muse Spark64.7%
    Source

    Not directly comparable

  • SimpleVQA

    Claude 4 Sonnet—
    Muse Spark71.3%
    Source

    Not directly comparable

  • ScreenSpot Pro

    Claude 4 Sonnet—
    Muse Spark84.1%
    Source

    Not directly comparable

  • ZeroBench

    Claude 4 Sonnet—
    Muse Spark33.0%
    Source

    Not directly comparable

  • MedXpertQA (MM)

    Claude 4 Sonnet—
    Muse Spark78.4%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Claude 4 Sonnet or Muse Spark?

Muse Spark has the higher public score, 70.6 versus 41.8, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.

Which is better for coding, Claude 4 Sonnet or Muse Spark?

The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, Claude 4 Sonnet or Muse Spark?

The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.

Which costs less, Claude 4 Sonnet 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, Claude 4 Sonnet or Muse Spark?

Muse Spark has the larger documented context window: 262K, compared with 200K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 21, 2026

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