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BenchLM

Claude Opus 4.7 (Adaptive) vs Muse Spark 1.2

Decision reading

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.

0 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Anthropic logo
Model A
Claude Opus 4.7 (Adaptive)

Anthropic

69.31/100

Estimated · Public rank #21

90% interval 50.3–80.8

Meta logo
Model B
Muse Spark 1.2

Meta

70.28/100

Estimated · Public rank #16

90% interval 58.9–76.0

Updated September 18, 2026. Rank says Muse Spark 1.2 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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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.

  • Agentic work

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

    Muse Spark 1.2

    Muse Spark 1.2 leads on the public agentic lane, 61 to 59.8, with Supported evidence for both models, although the 90% intervals overlap.

    Confidence: limited

  • Chat turn cost

    1K fresh input + 500 output tokens

    Muse Spark 1.2

    Muse Spark 1.2 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

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

    Muse Spark 1.2

    Muse Spark 1.2 has the lower estimated token cost for this stated workload. Claude Opus 4.7 (Adaptive) has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

Show secondary and unsupported calls

  • Repository review cost

    50K fresh input + 3K output tokens

    Muse Spark 1.2

    Muse Spark 1.2 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    Claude Opus 4.7 (Adaptive) is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    No clear pick

    The documented context windows are equal.

    Confidence: documented

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

57.9Claude Opus 4.7 (Adaptive)60.0Muse Spark 1.2

Directional only · BenchAlign

Muse Spark 1.2 scores higher, but at least one score rests on Estimated evidence or a different benchmark set. Directional only, no winner.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboardShortlist models for this job

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
0
Claude Opus 4.7 (Adaptive) only
21
Muse Spark 1.2 only
8
Like-for-like categories
1 / 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

Like-for-like
Claude Opus 4.7 (Adaptive)
59.8
Supported · #22/154
Muse Spark 1.2
61.0
Supported · #16/154
Basis
BenchAlign lane · 7 vs 2 public rows
Reading
Muse Spark 1.2 leads · intervals overlap

Coding

Directional only
Claude Opus 4.7 (Adaptive)
57.9
Estimated · #31/154
Muse Spark 1.2
60.0
Supported · #21/154
Basis
BenchAlign lane · 3 vs 5 public rows
Reading
Directional only

Knowledge

Directional only
Claude Opus 4.7 (Adaptive)
61.2
Estimated · #33/184
Muse Spark 1.2
70.7
Estimated · #10/184
Basis
BenchAlign lane · 4 vs 1 public rows
Reading
Directional only

Reasoning

Not comparable
Claude Opus 4.7 (Adaptive)
49.7
Unranked · 3 rankable rows
Muse Spark 1.2
75.3
Unranked · 2 rankable rows
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Claude Opus 4.7 (Adaptive)
Not ranked
Muse Spark 1.2
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Claude Opus 4.7 (Adaptive)
Not ranked
Muse Spark 1.2
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Claude Opus 4.7 (Adaptive)
50.1
#36/48
Muse Spark 1.2
Not ranked
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Claude Opus 4.7 (Adaptive)
Not ranked
Muse Spark 1.2
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
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.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

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 4.7 (Adaptive)
$0.0175
Fits in one request
Muse Spark 1.2
$0.00338
Fits in one request

Muse Spark 1.2 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Claude Opus 4.7 (Adaptive)
$0.325
Fits in one request
Muse Spark 1.2
$0.07525
Fits in one request

Muse Spark 1.2 has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

Claude Opus 4.7 (Adaptive)
$1.35
Fits in one request
Cached input priced at the published list-input rate
Muse Spark 1.2
$0.0975
Fits in one request

Muse Spark 1.2 has the lower modeled cost

Claude Opus 4.7 (Adaptive) has no published cached-input rate, so cached tokens use its listed input rate.

Specification differences

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

Cached-input rate

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

Claude Opus 4.7 (Adaptive)

Not published

Muse Spark 1.2

$0.15 per 1M cached input tokens

Meta: Muse Spark 1.2 model page

Documented inputs

Claude Opus 4.7 (Adaptive)

Not sourced

Muse Spark 1.2

Not sourced

Documented outputs

Claude Opus 4.7 (Adaptive)

Not sourced

Muse Spark 1.2

Not sourced

Provider availability

Claude Opus 4.7 (Adaptive)

Not sourced

Muse Spark 1.2

Not sourced

Reasoning profile

Claude Opus 4.7 (Adaptive)

Reasoning

Muse Spark 1.2

Reasoning

Weight access

Claude Opus 4.7 (Adaptive)

Proprietary

Muse Spark 1.2

Proprietary

License

Claude Opus 4.7 (Adaptive)

Proprietary

Muse Spark 1.2

Proprietary

Release date

Claude Opus 4.7 (Adaptive)

2026-04-16

Muse Spark 1.2

2026-08-05

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
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost
Repository review: $0.325 vs $0.07525. Cache-heavy agent loop: $1.35 vs $0.0975.
Context tradeoff
Both models list 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 evidence29 rows

Agentic

  • Terminal-Bench 2.0

    Claude Opus 4.7 (Adaptive)69.4%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • BrowseComp

    Claude Opus 4.7 (Adaptive)79.3%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • MCP Atlas

    Claude Opus 4.7 (Adaptive)77.3%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • OSWorld-Verified

    Claude Opus 4.7 (Adaptive)78%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • CyberGym

    Claude Opus 4.7 (Adaptive)73.1%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • OSWorld 2.0

    Claude Opus 4.7 (Adaptive)18.2%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • JobBench

    Claude Opus 4.7 (Adaptive)45.9%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • Terminal-Bench 2.1

    Claude Opus 4.7 (Adaptive)—
    Muse Spark 1.282.9%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Claude Opus 4.7 (Adaptive)—
    Muse Spark 1.269.7%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Claude Opus 4.7 (Adaptive)87.6%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • SWE-bench Pro

    Claude Opus 4.7 (Adaptive)64.3%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • Terminal-Bench 2.0

    Claude Opus 4.7 (Adaptive)69.4%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • Terminal-Bench 2.1

    Claude Opus 4.7 (Adaptive)—
    Muse Spark 1.282.9%
    Source

    Not directly comparable

  • DeepSWE

    Claude Opus 4.7 (Adaptive)—
    Muse Spark 1.259.3%
    Source

    Not directly comparable

  • VulcanBench v3

    Claude Opus 4.7 (Adaptive)—
    Muse Spark 1.287.0%
    Source

    Not directly comparable

  • FrontierSWE v2

    Claude Opus 4.7 (Adaptive)—
    Muse Spark 1.212.0%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    Claude Opus 4.7 (Adaptive)—
    Muse Spark 1.286.6%
    Source

    Not directly comparable

Reasoning

  • MRCR v2 128K-256K

    Claude Opus 4.7 (Adaptive)59.2%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • ARC-AGI-2

    Claude Opus 4.7 (Adaptive)75.8%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • ARC-AGI-3

    Claude Opus 4.7 (Adaptive)0.2%
    Source
    Muse Spark 1.2—

    Not directly comparable

Knowledge

  • GPQA

    Claude Opus 4.7 (Adaptive)94.2%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • GPQA-D

    Claude Opus 4.7 (Adaptive)94.2%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • HLE

    Claude Opus 4.7 (Adaptive)54.7%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • HLE w/o tools

    Claude Opus 4.7 (Adaptive)46.9%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • MMLU-Pro (Vals)

    Claude Opus 4.7 (Adaptive)—
    Muse Spark 1.288.3%
    Source

    Not directly comparable

Math

  • FrontierMath (legacy)

    Claude Opus 4.7 (Adaptive)43.8%
    Source
    Muse Spark 1.2—

    Not directly comparable

Multimodal

  • OfficeQA Pro

    Claude Opus 4.7 (Adaptive)43.6%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • CharXiv

    Claude Opus 4.7 (Adaptive)91%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • CharXiv w/o tools

    Claude Opus 4.7 (Adaptive)82.1%
    Source
    Muse Spark 1.2—

    Not directly comparable

Questions

Which is better, Claude Opus 4.7 (Adaptive) or Muse Spark 1.2?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Claude Opus 4.7 (Adaptive) or Muse Spark 1.2?

Muse Spark 1.2 scores higher for coding on the public lane, 60 to 57.9. Claude Opus 4.7 (Adaptive) 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, Claude Opus 4.7 (Adaptive) or Muse Spark 1.2?

Muse Spark 1.2 leads the public agentic tasks lane, 61 to 59.8, with Supported evidence for both models, although the 90% intervals overlap.

Which costs less, Claude Opus 4.7 (Adaptive) or Muse Spark 1.2?

For the stated presets, chat costs $0.0175 on Claude Opus 4.7 (Adaptive) and $0.00337 on Muse Spark 1.2; repository review costs $0.325 and $0.07525; the cache-heavy agent loop costs $1.35 and $0.0975. Claude Opus 4.7 (Adaptive) has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Claude Opus 4.7 (Adaptive) or Muse Spark 1.2?

Both models list the same context window, 1M.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 18, 2026

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