Coding work
Code generation, repair, and software-engineering tasks
Claude Opus 4.7
Claude Opus 4.7 leads on the public coding lane, 58.1 to 53.3, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Claude Opus 4.7 vs Muse Spark
Updated September 29, 2026. Rank says Claude Opus 4.7 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
Share or export
Decision reading
Claude Opus 4.7 has the higher public score estimate, 66.27 versus 60.57, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 6 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 6
- Claude Opus 4.7 only
- 8
- Muse Spark only
- 21
- Like-for-like categories
- 2 / 8
Estimated: Claude Opus 4.7 · Supported: Muse SparkHow the comparison works
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
Claude Opus 4.7
Claude Opus 4.7 leads on the public agentic lane, 53.3 to 50, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limitedLong documents
Prompts that approach the documented context limit
Claude Opus 4.7
Claude Opus 4.7 has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
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-ratesCache-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-fallbackRepository 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
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.
58.1Claude Opus 4.753.3Muse Spark
Like-for-like · BenchAlign v5.7
Claude Opus 4.7 leads the like-for-like coding row, although the 90% intervals overlap.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
8 Claude Opus 4.7 only
6 Shared
21 Muse Spark only
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
FrontierMath v2 (Tier 4)Math
Normalized gap 8.3Claude Opus 4.7:22.917%Muse Spark:14.600%FrontierMath v2 (Tiers 1-3)Math
Normalized gap 4.8Claude Opus 4.7:43.793%Muse Spark:39.000%MMLU-Pro (Vals)Knowledge
Normalized gap 2.6Claude Opus 4.7:89.9%Muse Spark:87.3%
Bars run 0–100 on each benchmark’s normalized display scale
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.7 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
- 53.3
- Supported · #37/117
- Muse Spark
- 50.0
- Supported · #39/117
- Basis
- BenchAlign v5.7 lane · 5 vs 5 public rows
- Reading
- Claude Opus 4.7 leads · intervals overlap
Coding
Like-for-like- Claude Opus 4.7
- 58.1
- Supported · #22/143
- Muse Spark
- 53.3
- Supported · #35/143
- Basis
- BenchAlign v5.7 lane · 5 vs 5 public rows
- Reading
- Claude Opus 4.7 leads · intervals overlap
Knowledge
Directional only- Claude Opus 4.7
- 63.7
- Estimated · #32/169
- Muse Spark
- 60.5
- Supported · #40/169
- Basis
- BenchAlign v5.7 lane · 2 vs 7 public rows
- Reading
- Directional only
Reasoning
Not comparable- Claude Opus 4.7
- Not ranked
- Muse Spark
- 53.4
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Claude Opus 4.7
- Not ranked
- Muse Spark
- 78.5
- #14/50
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Opus 4.7
- Not ranked
- Muse Spark
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Opus 4.7
- Not ranked
- Muse Spark
- 91.9
- #8/124
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Claude Opus 4.7
- 60.6
- Unranked · 2 rankable rows
- Muse Spark
- 55.1
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 2 vs 2 weighted rows
- Reading
- Not comparable
| Category | Claude Opus 4.7 | Muse Spark | Basis | Reading |
|---|---|---|---|---|
| Agentic | 53.3Supported · #37/117 | 50.0Supported · #39/117 | Like-for-likeBenchAlign v5.7 lane · 5 vs 5 public rows | Claude Opus 4.7 leads · intervals overlap |
| Coding | 58.1Supported · #22/143 | 53.3Supported · #35/143 | Like-for-likeBenchAlign v5.7 lane · 5 vs 5 public rows | Claude Opus 4.7 leads · intervals overlap |
| Knowledge | 63.7Estimated · #32/169 | 60.5Supported · #40/169 | Directional onlyBenchAlign v5.7 lane · 2 vs 7 public rows | Directional only |
| Reasoning | Not ranked | 53.4Unranked · 3 rankable rows | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multimodal | Not ranked | 78.5#14/50 | 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 |
| Instruction following | Not ranked | 91.9#8/124 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 60.6Unranked · 2 rankable rows | 55.1Unranked · 2 rankable rows | Not comparableProvisional lane · 2 vs 2 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Supported evidence per lane · bars run 0–100Methodology
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
- $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 4.7
- $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 4.7
- $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 4.7 has no published cached-input rate, so cached tokens use its listed input rate. Muse Spark has no comparable published API token rate.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationClaude Opus 4.7Muse Spark
Context window
Maximum documented context; output-token limits may be lower.
Claude Opus 4.7
Muse Spark
262K
API model ID
Claude Opus 4.7
claude-opus-4-7
Anthropic model ID documentationMuse 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 4.7
Not published
Muse Spark
No comparable hosted API rate
Documented inputs
Claude Opus 4.7
text, image
Anthropic model overviewMuse Spark
Not sourced
Documented outputs
Claude Opus 4.7
Muse Spark
Not sourced
Provider availability
Claude Opus 4.7
Generally Available · Claude API
Anthropic model overviewMuse Spark
Not sourced
Reasoning profile
Claude Opus 4.7
Non-Reasoning
Muse Spark
Reasoning
Weight access
Claude Opus 4.7
Proprietary
Muse Spark
Proprietary
License
Claude Opus 4.7
Proprietary
Muse Spark
Proprietary
Release date
Claude Opus 4.7
2026-04-16
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
- Claude Opus 4.7 has the higher public score estimate, 66.27 versus 60.57, but the 90% score intervals overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Claude Opus 4.7 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Claude Opus 4.7 or Muse Spark?
Claude Opus 4.7 has the higher public score estimate, 66.27 versus 60.57, 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 4.7 or Muse Spark?
Claude Opus 4.7 leads the public coding lane, 58.1 to 53.3, with Supported evidence for both models, although the 90% intervals overlap.
Which is better for agentic tasks, Claude Opus 4.7 or Muse Spark?
Claude Opus 4.7 leads the public agentic tasks lane, 53.3 to 50, with Supported evidence for both models, although the 90% intervals overlap.
Which costs less, Claude Opus 4.7 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 4.7 or Muse Spark?
Claude Opus 4.7 has the larger documented context window: 1M, compared with 262K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence35 rows
Agentic
Gert Labs
Claude Opus 4.765.59%SourceMuse Spark—Not directly comparable
ResearchClawBench
Claude Opus 4.720.7%SourceMuse Spark—Not directly comparable
OSWorld 2.0
Claude Opus 4.713.9%SourceMuse Spark—Not directly comparable
Terminal-Bench 2.1 (Vals)
Claude Opus 4.768.5%SourceMuse Spark—Not directly comparable
ApprenticeBench
Claude Opus 4.77%SourceMuse Spark—Not directly comparable
Terminal-Bench 2.0
Claude Opus 4.7—Muse Spark59%SourceNot directly comparable
τ²-bench results
Claude Opus 4.7—Muse Spark91.5%SourceNot directly comparable
DeepSearchQA
Claude Opus 4.7—Muse Spark74.8%SourceNot directly comparable
CyberGym
Claude Opus 4.7—Muse Spark43.5%SourceNot directly comparable
Claw-Eval
Claude Opus 4.7—Muse Spark63.8%SourceNot directly comparable
Coding
Vibe Code Bench
Shared sourceClaude Opus 4.771.00%Muse Spark19.67%Claude Opus 4.7 leads this result
React Native Evals
Claude Opus 4.782.8%SourceMuse Spark—Not directly comparable
FrontierCode 1.1 Main
Claude Opus 4.738.5%SourceMuse Spark—Not directly comparable
LiveCodeBench (Vals)
Claude Opus 4.785.1%SourceMuse Spark—Not directly comparable
SWE-bench (Vals)
Claude Opus 4.782.0%SourceMuse Spark74.4%SourceClaude Opus 4.7 leads this result
SWE-bench Verified
Claude Opus 4.7—Muse Spark77.4%SourceNot directly comparable
SWE-bench Pro
Claude Opus 4.7—Muse Spark52.4%SourceNot directly comparable
LiveCodeBench Pro
Claude Opus 4.7—Muse Spark80.0%SourceNot directly comparable
Reasoning
ARC-AGI-2
Claude Opus 4.7—Muse Spark42.5%SourceNot directly comparable
Multimodal
CharXiv
Claude Opus 4.7—Muse Spark86.4%SourceNot directly comparable
MMMU-Pro
Claude Opus 4.7—Muse Spark80.4%SourceNot directly comparable
ERQA
Claude Opus 4.7—Muse Spark64.7%SourceNot directly comparable
SimpleVQA
Claude Opus 4.7—Muse Spark71.3%SourceNot directly comparable
ScreenSpot Pro
Claude Opus 4.7—Muse Spark84.1%SourceNot directly comparable
ZeroBench
Claude Opus 4.7—Muse Spark33.0%SourceNot directly comparable
MedXpertQA (MM)
Claude Opus 4.7—Muse Spark78.4%SourceNot directly comparable
Knowledge
GPQA Diamond (Vals)
Claude Opus 4.790.2%SourceMuse Spark89.6%SourceClaude Opus 4.7 leads this result
MMLU-Pro (Vals)
Claude Opus 4.789.9%SourceMuse Spark87.3%SourceClaude Opus 4.7 leads this result
GPQA-D
Claude Opus 4.7—Muse Spark89.5%SourceNot directly comparable
HLE
Claude Opus 4.7—Muse Spark50.4%SourceNot directly comparable
HLE w/o tools
Claude Opus 4.7—Muse Spark42.8%SourceNot directly comparable
HealthBench Hard
Claude Opus 4.7—Muse Spark42.8%SourceNot directly comparable
MedXpertQA (Text)
Claude Opus 4.7—Muse Spark52.6%SourceNot directly comparable
Math
FrontierMath v2 (Tiers 1-3)
Shared sourceClaude Opus 4.743.793%Muse Spark39.000%Claude Opus 4.7 leads this result
FrontierMath v2 (Tier 4)
Shared sourceClaude Opus 4.722.917%Muse Spark14.600%Claude Opus 4.7 leads this result
35 public results · 6 shared
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Last updated September 29, 2026