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BenchLM
Model A
Claude Opus 4.6

Anthropic

67.8/100

Supported · Public rank #23

90% interval 54.2–81.4

Claude Opus 4.6 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 estimate, 70.6 versus 67.8, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

19 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

    Claude Opus 4.6

    Claude Opus 4.6 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

    The category averages use different weighted benchmark sets, so they are 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: 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
19
Claude Opus 4.6 only
14
Muse Spark only
5
Like-for-like categories
1 / 8

4 categories use 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.

Math

Like-for-like
Claude Opus 4.6
36.3
Muse Spark
32.9
Weighted basis
2 vs 2 rows
Reading
Claude Opus 4.6 leads

Agentic

Directional only
Claude Opus 4.6
73.0
Muse Spark
59.0
Weighted basis
3 vs 1 rows
Reading
Directional only

Coding

Directional only
Claude Opus 4.6
68.1
Muse Spark
67.8
Weighted basis
3 vs 2 rows
Reading
Directional only

Knowledge

Directional only
Claude Opus 4.6
69.1
Muse Spark
50.4
Weighted basis
4 vs 1 rows
Reading
Directional only

Multimodal

Directional only
Claude Opus 4.6
77.3
Muse Spark
82.5
Weighted basis
1 vs 2 rows
Reading
Directional only

Reasoning

Not comparable
Claude Opus 4.6
Not measured
Muse Spark
42.5
Weighted basis
0 vs 1 rows
Reading
Not comparable

Multilingual

Not comparable
Claude Opus 4.6
Not measured
Muse Spark
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Claude Opus 4.6
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 Opus 4.6
$0.0175
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 Opus 4.6
$0.325
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 Opus 4.6
$1.35
Fits 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 Opus 4.6 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 Opus 4.6

1M

Muse Spark

262K

API model ID

Claude Opus 4.6

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 Opus 4.6

Not published

Muse Spark

No comparable hosted API rate

Documented inputs

Claude Opus 4.6

Not sourced

Muse Spark

Not sourced

Documented outputs

Claude Opus 4.6

Not sourced

Muse Spark

Not sourced

Provider availability

Claude Opus 4.6

Not sourced

Muse Spark

Not sourced

Reasoning profile

Claude Opus 4.6

Non-Reasoning

Muse Spark

Reasoning

Weight access

Claude Opus 4.6

Proprietary

Muse Spark

Proprietary

License

Claude Opus 4.6

Proprietary

Muse Spark

Proprietary

Release date

Claude Opus 4.6

2026-02-01

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 estimate, 70.6 versus 67.8, but the 90% score intervals overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Claude Opus 4.6 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 evidence38 rows

Agentic

  • Terminal-Bench 2.0

    Claude Opus 4.665.4%
    Source
    Muse Spark59%
    Source

    Claude Opus 4.6 leads this result

  • BrowseComp

    Claude Opus 4.683.7%
    Source
    Muse Spark—

    Not directly comparable

  • OSWorld-Verified

    Claude Opus 4.672.7%
    Source
    Muse Spark—

    Not directly comparable

  • Claw-Eval

    Shared source
    Claude Opus 4.670.4%
    Muse Spark63.8%

    Claude Opus 4.6 leads this result

  • DeepSearchQA

    Shared source
    Claude Opus 4.673.7%
    Muse Spark74.8%

    Muse Spark leads this result

  • CyberGym

    Shared source
    Claude Opus 4.666.6%
    Muse Spark43.5%

    Claude Opus 4.6 leads this result

  • Gert Labs

    Claude Opus 4.661.85%
    Source
    Muse Spark—

    Not directly comparable

  • ResearchClawBench

    Claude Opus 4.619.9%
    Source
    Muse Spark—

    Not directly comparable

  • JobBench

    Claude Opus 4.636.7%
    Source
    Muse Spark—

    Not directly comparable

  • τ²-bench results

    Claude Opus 4.6—
    Muse Spark91.5%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Claude Opus 4.680.8%
    Source
    Muse Spark77.4%
    Source

    Claude Opus 4.6 leads this result

  • SWE-bench Verified*

    Claude Opus 4.675.6%
    Source
    Muse Spark—

    Not directly comparable

  • LiveCodeBench Pro

    Shared source
    Claude Opus 4.670.7%
    Muse Spark80.0%

    Muse Spark leads this result

  • SWE-bench Pro

    Shared source
    Claude Opus 4.653.4%
    Muse Spark52.4%

    Claude Opus 4.6 leads this result

  • SWE-Rebench

    Claude Opus 4.665.3%
    Source
    Muse Spark—

    Not directly comparable

  • React Native Evals

    Claude Opus 4.684.1%
    Source
    Muse Spark—

    Not directly comparable

  • Vibe Code Bench

    Shared source
    Claude Opus 4.657.57%
    Muse Spark19.67%

    Claude Opus 4.6 leads this result

  • FrontierCode 1.1 Main

    Claude Opus 4.626.9%
    Source
    Muse Spark—

    Not directly comparable

Reasoning

  • ARC-AGI-2

    Claude Opus 4.6—
    Muse Spark42.5%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Claude Opus 4.691.3%
    Source
    Muse Spark—

    Not directly comparable

  • GPQA-D

    Claude Opus 4.689.2%
    Source
    Muse Spark89.5%
    Source

    Muse Spark leads this result

  • SuperGPQA

    Claude Opus 4.695%
    Source
    Muse Spark—

    Not directly comparable

  • MMLU-Pro

    Claude Opus 4.682%
    Source
    Muse Spark—

    Not directly comparable

  • MMLU-Pro (Arcee)

    Claude Opus 4.689.1%
    Source
    Muse Spark—

    Not directly comparable

  • HLE

    Claude Opus 4.653%
    Source
    Muse Spark50.4%
    Source

    Claude Opus 4.6 leads this result

  • HLE w/o tools

    Shared source
    Claude Opus 4.640%
    Muse Spark42.8%

    Muse Spark leads this result

  • HealthBench Hard

    Shared source
    Claude Opus 4.614.8%
    Muse Spark42.8%

    Muse Spark leads this result

  • MedXpertQA (Text)

    Shared source
    Claude Opus 4.652.1%
    Muse Spark52.6%

    Muse Spark leads this result

Math

  • AIME25 (Arcee)

    Claude Opus 4.699.8%
    Source
    Muse Spark—

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    Shared source
    Claude Opus 4.640.700%
    Muse Spark39.000%

    Claude Opus 4.6 leads this result

  • FrontierMath v2 (Tier 4)

    Shared source
    Claude Opus 4.622.900%
    Muse Spark14.600%

    Claude Opus 4.6 leads this result

Multimodal

  • MMMU-Pro

    Claude Opus 4.677.3%
    Source
    Muse Spark80.4%
    Source

    Muse Spark leads this result

  • ERQA

    Shared source
    Claude Opus 4.651.6%
    Muse Spark64.7%

    Muse Spark leads this result

  • ScreenSpot Pro

    Shared source
    Claude Opus 4.683.1%
    Muse Spark84.1%

    Muse Spark leads this result

  • MedXpertQA (MM)

    Shared source
    Claude Opus 4.664.8%
    Muse Spark78.4%

    Muse Spark leads this result

  • CharXiv

    Claude Opus 4.6—
    Muse Spark86.4%
    Source

    Not directly comparable

  • SimpleVQA

    Claude Opus 4.6—
    Muse Spark71.3%
    Source

    Not directly comparable

  • ZeroBench

    Claude Opus 4.6—
    Muse Spark33.0%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Claude Opus 4.6 or Muse Spark?

Muse Spark has the higher public score estimate, 70.6 versus 67.8, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Claude Opus 4.6 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 Opus 4.6 or Muse Spark?

The current agentic tasks 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 costs less, Claude Opus 4.6 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 Opus 4.6 or Muse Spark?

Claude Opus 4.6 has the larger documented context window: 1M, compared with 262K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 21, 2026

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