Agentic
Like-for-like- GPT-5.5
- 63.9
- Supported · #15/151
- Muse Spark
- 58.8
- Supported · #31/151
- Basis
- BenchAlign lane · 13 vs 5 public rows
- Reading
- GPT-5.5 leads · intervals overlap
GPT-5.5 vs Muse Spark
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
GPT-5.5 has the higher public score estimate, 73.27 versus 68.39, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
12 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
GPT-5.5
GPT-5.5 leads on the public coding lane, 67.7 to 59.2, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
GPT-5.5
GPT-5.5 leads on the public agentic lane, 63.9 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
GPT-5.5
GPT-5.5 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.
26 GPT-5.5 only
12 Shared
12 Muse Spark only
- Shared results
- 12
- GPT-5.5 only
- 26
- Muse Spark only
- 12
- Like-for-like categories
- 3 / 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.
Coding
Like-for-like- GPT-5.5
- 67.7
- Supported · #8/183
- Muse Spark
- 59.2
- Supported · #28/183
- Basis
- BenchAlign lane · 9 vs 4 public rows
- Reading
- GPT-5.5 leads · intervals overlap
Knowledge
Like-for-like- GPT-5.5
- 73.3
- Supported · #7/181
- Muse Spark
- 65.7
- Supported · #23/181
- Basis
- BenchAlign lane · 6 vs 5 public rows
- Reading
- GPT-5.5 leads · intervals overlap
Multimodal
Directional only- GPT-5.5
- 71.3
- #19/48
- Muse Spark
- 77.5
- #14/48
- Basis
- Provisional lane · 2 vs 2 weighted rows
- Reading
- Directional only
Instruction following
Directional only- GPT-5.5
- 92.9
- #7/120
- Muse Spark
- 92.9
- #8/120
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- GPT-5.5
- 63.5
- #15/22
- Muse Spark
- 45.9
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 2 vs 1 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.5
- 69.6
- Unranked · 3 rankable rows
- Muse Spark
- 55.3
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 2 vs 2 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.5
- Not ranked
- Muse Spark
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-5.5 | Muse Spark | Basis | Reading |
|---|---|---|---|---|
| Agentic | 63.9Supported · #15/151 | 58.8Supported · #31/151 | Like-for-likeBenchAlign lane · 13 vs 5 public rows | GPT-5.5 leads · intervals overlap |
| Coding | 67.7Supported · #8/183 | 59.2Supported · #28/183 | Like-for-likeBenchAlign lane · 9 vs 4 public rows | GPT-5.5 leads · intervals overlap |
| Knowledge | 73.3Supported · #7/181 | 65.7Supported · #23/181 | Like-for-likeBenchAlign lane · 6 vs 5 public rows | GPT-5.5 leads · intervals overlap |
| Multimodal | 71.3#19/48 | 77.5#14/48 | Directional onlyProvisional lane · 2 vs 2 weighted rows | Directional only |
| Instruction following | 92.9#7/120 | 92.9#8/120 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 63.5#15/22 | 45.9Unranked · 3 rankable rows | Not comparableProvisional lane · 2 vs 1 weighted rows | Not comparable |
| Math | 69.6Unranked · 3 rankable rows | 55.3Unranked · 2 rankable rows | Not comparableProvisional lane · 2 vs 2 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 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.
ARC-AGI-2
Reasoning
Terminal-Bench 2.0
Agentic
FrontierMath v2 (Tier 4)
Math
FrontierMath v2 (Tiers 1-3)
Math
SWE-bench Pro
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
- GPT-5.5
- $0.02
- 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
- GPT-5.5
- $0.34
- 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
- GPT-5.5
- $0.5
- 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.
SpecificationGPT-5.5Muse Spark
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.5
Muse Spark
262K
API model ID
GPT-5.5
gpt-5.5
OpenAI pricingMuse Spark
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.5
$0.5 per 1M cached input tokens
OpenAI pricingMuse Spark
No comparable hosted API rate
Documented inputs
GPT-5.5
Not sourced
Muse Spark
Not sourced
Documented outputs
GPT-5.5
Not sourced
Muse Spark
Not sourced
Provider availability
GPT-5.5
Not sourced
Muse Spark
Not sourced
Reasoning profile
GPT-5.5
Reasoning
Muse Spark
Reasoning
Weight access
GPT-5.5
Proprietary
Muse Spark
Proprietary
License
GPT-5.5
Proprietary
Muse Spark
Proprietary
Release date
GPT-5.5
2026-04-23
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
- GPT-5.5 has the higher public score estimate, 73.27 versus 68.39, but the 90% score intervals overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- GPT-5.5 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 evidence50 rows
Agentic
Terminal-Bench 2.0
GPT-5.582%SourceMuse Spark59%SourceGPT-5.5 leads this result
CyberGym
GPT-5.581.8%SourceMuse Spark43.5%SourceGPT-5.5 leads this result
BrowseComp
GPT-5.584.4%SourceMuse Spark—Not directly comparable
OSWorld-Verified
GPT-5.578.7%SourceMuse Spark—Not directly comparable
MCP Atlas
GPT-5.575.3%SourceMuse Spark—Not directly comparable
Toolathlon
GPT-5.555.6%SourceMuse Spark—Not directly comparable
τ²-bench results
GPT-5.598%SourceMuse Spark91.5%SourceGPT-5.5 leads this result
Gert Labs
GPT-5.572.93%SourceMuse Spark—Not directly comparable
ResearchClawBench
GPT-5.517.0%SourceMuse Spark—Not directly comparable
OSWorld 2.0
GPT-5.513.0%SourceMuse Spark—Not directly comparable
JobBench
GPT-5.542.7%SourceMuse Spark—Not directly comparable
ExploitGym
GPT-5.513.4%SourceMuse Spark—Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.576.4%SourceMuse Spark—Not directly comparable
DeepSearchQA
GPT-5.5—Muse Spark74.8%SourceNot directly comparable
Claw-Eval
GPT-5.5—Muse Spark63.8%SourceNot directly comparable
Coding
SWE-bench Pro
GPT-5.558.6%SourceMuse Spark52.4%SourceGPT-5.5 leads this result
Terminal-Bench 2.0
GPT-5.582.0%SourceMuse Spark—Not directly comparable
Vibe Code Bench
Shared sourceGPT-5.569.85%Muse Spark19.67%GPT-5.5 leads this result
React Native Evals
GPT-5.584.7%SourceMuse Spark—Not directly comparable
cursorBench31
GPT-5.559.2%SourceMuse Spark—Not directly comparable
cursorBench32
GPT-5.558.4%SourceMuse Spark—Not directly comparable
FrontierCode 1.1 Main
GPT-5.543.0%SourceMuse Spark—Not directly comparable
LiveCodeBench (Vals)
GPT-5.585.3%SourceMuse Spark—Not directly comparable
SWE-bench (Vals)
GPT-5.582.6%SourceMuse Spark—Not directly comparable
SWE-bench Verified
GPT-5.5—Muse Spark77.4%SourceNot directly comparable
LiveCodeBench Pro
GPT-5.5—Muse Spark80.0%SourceNot directly comparable
Reasoning
Knowledge
GPQA
GPT-5.593.6%SourceMuse Spark—Not directly comparable
GPQA-D
GPT-5.593.6%SourceMuse Spark89.5%SourceGPT-5.5 leads this result
HLE
GPT-5.552.2%SourceMuse Spark50.4%SourceGPT-5.5 leads this result
HLE w/o tools
GPT-5.541.4%SourceMuse Spark42.8%SourceMuse Spark leads this result
GPQA Diamond (Vals)
GPT-5.593.2%SourceMuse Spark—Not directly comparable
MMLU-Pro (Vals)
GPT-5.588.1%SourceMuse Spark—Not directly comparable
HealthBench Hard
GPT-5.5—Muse Spark42.8%SourceNot directly comparable
MedXpertQA (Text)
GPT-5.5—Muse Spark52.6%SourceNot directly comparable
Math
FrontierMath (legacy)
GPT-5.551.7%SourceMuse Spark—Not directly comparable
FrontierMath v2 (Tiers 1-3)
Shared sourceGPT-5.551.700%Muse Spark39.000%GPT-5.5 leads this result
FrontierMath v2 (Tier 4)
Shared sourceGPT-5.535.400%Muse Spark14.600%GPT-5.5 leads this result
Multimodal
MMMU-Pro
GPT-5.581.2%SourceMuse Spark80.4%SourceGPT-5.5 leads this result
MMMU-Pro w/ Python
GPT-5.583.2%SourceMuse Spark—Not directly comparable
OfficeQA Pro
GPT-5.554.1%SourceMuse Spark—Not directly comparable
CharXiv
GPT-5.5—Muse Spark86.4%SourceNot directly comparable
ERQA
GPT-5.5—Muse Spark64.7%SourceNot directly comparable
SimpleVQA
GPT-5.5—Muse Spark71.3%SourceNot directly comparable
ScreenSpot Pro
GPT-5.5—Muse Spark84.1%SourceNot directly comparable
ZeroBench
GPT-5.5—Muse Spark33.0%SourceNot directly comparable
MedXpertQA (MM)
GPT-5.5—Muse Spark78.4%SourceNot directly comparable
Frequently asked questions
Which is better, GPT-5.5 or Muse Spark?
GPT-5.5 has the higher public score estimate, 73.27 versus 68.39, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, GPT-5.5 or Muse Spark?
GPT-5.5 leads the public coding lane, 67.7 to 59.2, with Supported evidence for both models, although the 90% intervals overlap.
Which is better for agentic tasks, GPT-5.5 or Muse Spark?
GPT-5.5 leads the public agentic tasks lane, 63.9 to 58.8, with Supported evidence for both models, although the 90% intervals overlap.
Which costs less, GPT-5.5 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, GPT-5.5 or Muse Spark?
GPT-5.5 has the larger documented context window: 1M, compared with 262K.
Related comparisons
Last updated September 4, 2026
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