Agentic
Like-for-like- Claude Fable 5
- 74.8
- Supported · #3/151
- Muse Spark
- 58.8
- Supported · #31/151
- Basis
- BenchAlign lane · 4 vs 5 public rows
- Reading
- Claude Fable 5 leads · intervals overlap
Claude Fable 5 vs Muse Spark
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Claude Fable 5 has the higher public score, 80.9 versus 68.39, and the 90% score intervals do not overlap.
3 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Share or export
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.
Coding work
Code generation, repair, and software-engineering tasks
Claude Fable 5
Claude Fable 5 leads on the public coding lane, 76.9 to 59.2, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Agentic work
Tool use, computer use, and multi-step task completion
Claude Fable 5
Claude Fable 5 leads on the public agentic lane, 74.8 to 58.8, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Long documents
Prompts that approach the documented context limit
Claude Fable 5
Claude Fable 5 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-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.
15 Claude Fable 5 only
3 Shared
21 Muse Spark only
- Shared results
- 3
- Claude Fable 5 only
- 15
- Muse Spark only
- 21
- Like-for-like categories
- 3 / 8
1 category rests 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.
Coding
Like-for-like- Claude Fable 5
- 76.9
- Supported · #2/183
- Muse Spark
- 59.2
- Supported · #28/183
- Basis
- BenchAlign lane · 10 vs 4 public rows
- Reading
- Claude Fable 5 leads
Knowledge
Like-for-like- Claude Fable 5
- 83.5
- Supported · #2/181
- Muse Spark
- 65.7
- Supported · #23/181
- Basis
- BenchAlign lane · 2 vs 5 public rows
- Reading
- Claude Fable 5 leads · intervals overlap
Instruction following
Directional only- Claude Fable 5
- 78.3
- #54/120
- Muse Spark
- 92.9
- #8/120
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- Claude Fable 5
- 76.2
- #11/22
- Muse Spark
- 45.9
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Math
Not comparable- Claude Fable 5
- Not ranked
- Muse Spark
- 55.3
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Fable 5
- Not ranked
- Muse Spark
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Claude Fable 5
- 62.5
- Unranked · 2 rankable rows
- Muse Spark
- 77.5
- #14/48
- Basis
- Provisional lane · 1 vs 2 weighted rows
- Reading
- Not comparable
| Category | Claude Fable 5 | Muse Spark | Basis | Reading |
|---|---|---|---|---|
| Agentic | 74.8Supported · #3/151 | 58.8Supported · #31/151 | Like-for-likeBenchAlign lane · 4 vs 5 public rows | Claude Fable 5 leads · intervals overlap |
| Coding | 76.9Supported · #2/183 | 59.2Supported · #28/183 | Like-for-likeBenchAlign lane · 10 vs 4 public rows | Claude Fable 5 leads |
| Knowledge | 83.5Supported · #2/181 | 65.7Supported · #23/181 | Like-for-likeBenchAlign lane · 2 vs 5 public rows | Claude Fable 5 leads · intervals overlap |
| Instruction following | 78.3#54/120 | 92.9#8/120 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 76.2#11/22 | 45.9Unranked · 3 rankable rows | Not comparableProvisional lane · 0 vs 1 weighted 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 | 62.5Unranked · 2 rankable rows | 77.5#14/48 | Not comparableProvisional lane · 1 vs 2 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.
SWE-bench Pro
Coding
Terminal-Bench 2.0
Agentic
SWE-bench Verified
Coding
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 Fable 5
- $0.035
- 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 Fable 5
- $0.65
- 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 Fable 5
- $0.9
- 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.
SpecificationClaude Fable 5Muse Spark
Context window
Maximum documented context; output-token limits may be lower.
Claude Fable 5
Muse Spark
262K
API model ID
Claude Fable 5
claude-fable-5
Anthropic model overviewMuse 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 Fable 5
$1 per 1M cached input tokens
Claude API pricingMuse Spark
No comparable hosted API rate
Documented inputs
Claude Fable 5
text, image
Anthropic model overviewMuse Spark
Not sourced
Documented outputs
Claude Fable 5
Muse Spark
Not sourced
Provider availability
Claude Fable 5
Generally Available · Claude API
Anthropic model overviewMuse Spark
Not sourced
Reasoning profile
Claude Fable 5
Reasoning
Muse Spark
Reasoning
Weight access
Claude Fable 5
Proprietary
Muse Spark
Proprietary
License
Claude Fable 5
Proprietary
Muse Spark
Proprietary
Release date
Claude Fable 5
2026-06-09
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 Fable 5 has the higher public score, 80.9 versus 68.39, and the 90% score intervals do not overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Claude Fable 5 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 evidence39 rows
Agentic
Terminal-Bench 3.0
Claude Fable 534.0%SourceMuse Spark—Not directly comparable
Terminal-Bench 2.0
Claude Fable 584.3%SourceMuse Spark59%SourceClaude Fable 5 leads this result
OSWorld-Verified
Claude Fable 585%SourceMuse Spark—Not directly comparable
Terminal-Bench 2.1 (Vals)
Claude Fable 580.5%SourceMuse Spark—Not directly comparable
τ²-bench results
Claude Fable 5—Muse Spark91.5%SourceNot directly comparable
DeepSearchQA
Claude Fable 5—Muse Spark74.8%SourceNot directly comparable
CyberGym
Claude Fable 5—Muse Spark43.5%SourceNot directly comparable
Claw-Eval
Claude Fable 5—Muse Spark63.8%SourceNot directly comparable
Coding
SWE-bench Verified
Claude Fable 595%SourceMuse Spark77.4%SourceClaude Fable 5 leads this result
SWE-bench Pro
Claude Fable 580%SourceMuse Spark52.4%SourceClaude Fable 5 leads this result
FrontierSWE v2
Claude Fable 548.0%SourceMuse Spark—Not directly comparable
FrontierCode 1.1 Main
Claude Fable 553.5%SourceMuse Spark—Not directly comparable
Terminal-Bench 2.0
Claude Fable 584.3%SourceMuse Spark—Not directly comparable
cursorBench31
Claude Fable 570.6%SourceMuse Spark—Not directly comparable
cursorBench32
Claude Fable 570.5%SourceMuse Spark—Not directly comparable
VulcanBench v3
Claude Fable 589.5%SourceMuse Spark—Not directly comparable
LiveCodeBench (Vals)
Claude Fable 589.8%SourceMuse Spark—Not directly comparable
SWE-bench (Vals)
Claude Fable 595.0%SourceMuse Spark—Not directly comparable
LiveCodeBench Pro
Claude Fable 5—Muse Spark80.0%SourceNot directly comparable
Vibe Code Bench
Claude Fable 5—Muse Spark19.67%SourceNot directly comparable
Reasoning
ARC-AGI-2
Claude Fable 5—Muse Spark42.5%SourceNot directly comparable
Knowledge
GPQA Diamond (Vals)
Claude Fable 593.2%SourceMuse Spark—Not directly comparable
MMLU-Pro (Vals)
Claude Fable 591.5%SourceMuse Spark—Not directly comparable
GPQA-D
Claude Fable 5—Muse Spark89.5%SourceNot directly comparable
HLE
Claude Fable 5—Muse Spark50.4%SourceNot directly comparable
HLE w/o tools
Claude Fable 5—Muse Spark42.8%SourceNot directly comparable
HealthBench Hard
Claude Fable 5—Muse Spark42.8%SourceNot directly comparable
MedXpertQA (Text)
Claude Fable 5—Muse Spark52.6%SourceNot directly comparable
Math
Multimodal
Blueprint-Bench 2
Claude Fable 538.6%SourceMuse Spark—Not directly comparable
OfficeQA Pro
Claude Fable 557.9%SourceMuse Spark—Not directly comparable
CharXiv
Claude Fable 5—Muse Spark86.4%SourceNot directly comparable
MMMU-Pro
Claude Fable 5—Muse Spark80.4%SourceNot directly comparable
ERQA
Claude Fable 5—Muse Spark64.7%SourceNot directly comparable
SimpleVQA
Claude Fable 5—Muse Spark71.3%SourceNot directly comparable
ScreenSpot Pro
Claude Fable 5—Muse Spark84.1%SourceNot directly comparable
ZeroBench
Claude Fable 5—Muse Spark33.0%SourceNot directly comparable
MedXpertQA (MM)
Claude Fable 5—Muse Spark78.4%SourceNot directly comparable
Frequently asked questions
Which is better, Claude Fable 5 or Muse Spark?
Claude Fable 5 has the higher public score, 80.9 versus 68.39, 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 Fable 5 or Muse Spark?
Claude Fable 5 leads the public coding lane, 76.9 to 59.2, with Supported evidence for both models and non-overlapping 90% intervals.
Which is better for agentic tasks, Claude Fable 5 or Muse Spark?
Claude Fable 5 leads the public agentic tasks lane, 74.8 to 58.8, with Supported evidence for both models, although the 90% intervals overlap.
Which costs less, Claude Fable 5 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 Fable 5 or Muse Spark?
Claude Fable 5 has the larger documented context window: 1M, compared with 262K.
Related comparisons
Last updated September 4, 2026
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