Agentic
Directional only- GPT-5.5 Pro
- 60.6
- Estimated · #24/151
- Muse Spark
- 58.8
- Supported · #31/151
- Basis
- BenchAlign lane · 1 vs 5 public rows
- Reading
- Directional only
GPT-5.5 Pro vs Muse Spark
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Muse Spark has the higher public score estimate, 68.39 versus 63.07, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
4 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.
Long documents
Prompts that approach the documented context limit
GPT-5.5 Pro
GPT-5.5 Pro 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
GPT-5.5 Pro is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-5.5 Pro is scored on Estimated evidence for agentic, so the reading is 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.
2 GPT-5.5 Pro only
4 Shared
20 Muse Spark only
- Shared results
- 4
- GPT-5.5 Pro only
- 2
- Muse Spark only
- 20
- Like-for-like categories
- 0 / 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.
Knowledge
Directional only- GPT-5.5 Pro
- 61.1
- Estimated · #36/181
- Muse Spark
- 65.7
- Supported · #23/181
- Basis
- BenchAlign lane · 2 vs 5 public rows
- Reading
- Directional only
Coding
Not comparable- GPT-5.5 Pro
- Not ranked
- Muse Spark
- 59.2
- Supported · #28/183
- Basis
- BenchAlign lane · 0 vs 4 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-5.5 Pro
- Not ranked
- Muse Spark
- 45.9
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.5 Pro
- 70.2
- 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 Pro
- Not ranked
- Muse Spark
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.5 Pro
- Not ranked
- Muse Spark
- 77.5
- #14/48
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.5 Pro
- Not ranked
- Muse Spark
- 92.9
- #8/120
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-5.5 Pro | Muse Spark | Basis | Reading |
|---|---|---|---|---|
| Agentic | 60.6Estimated · #24/151 | 58.8Supported · #31/151 | Directional onlyBenchAlign lane · 1 vs 5 public rows | Directional only |
| Knowledge | 61.1Estimated · #36/181 | 65.7Supported · #23/181 | Directional onlyBenchAlign lane · 2 vs 5 public rows | Directional only |
| Coding | Not ranked | 59.2Supported · #28/183 | Not comparableBenchAlign lane · 0 vs 4 public rows | Not comparable |
| Reasoning | Not ranked | 45.9Unranked · 3 rankable rows | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Math | 70.2Unranked · 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 |
| Multimodal | Not ranked | 77.5#14/48 | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Instruction following | Not ranked | 92.9#8/120 | 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.
FrontierMath v2 (Tier 4)
Math
FrontierMath v2 (Tiers 1-3)
Math
HLE
Knowledge
HLE w/o tools
Knowledge
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 Pro
- $0.12
- 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 Pro
- $2.04
- 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 Pro
- $8.40
- 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
GPT-5.5 Pro 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.
SpecificationGPT-5.5 ProMuse Spark
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.5 Pro
Muse Spark
262K
API model ID
GPT-5.5 Pro
gpt-5.5-pro
OpenAI GPT-5.5 Pro model 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.
GPT-5.5 Pro
Not published
OpenAI pricingMuse Spark
No comparable hosted API rate
Documented inputs
GPT-5.5 Pro
text, image
OpenAI model catalogMuse Spark
Not sourced
Documented outputs
GPT-5.5 Pro
Muse Spark
Not sourced
Provider availability
GPT-5.5 Pro
Generally Available · OpenAI Responses API
OpenAI model catalogMuse Spark
Not sourced
Reasoning profile
GPT-5.5 Pro
Reasoning
Muse Spark
Reasoning
Weight access
GPT-5.5 Pro
Proprietary
Muse Spark
Proprietary
License
GPT-5.5 Pro
Proprietary
Muse Spark
Proprietary
Release date
GPT-5.5 Pro
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
- Muse Spark has the higher public score estimate, 68.39 versus 63.07, 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 Pro has the larger documented window (1.05M).
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 evidence26 rows
Agentic
BrowseComp
GPT-5.5 Pro90.1%SourceMuse Spark—Not directly comparable
Terminal-Bench 2.0
GPT-5.5 Pro—Muse Spark59%SourceNot directly comparable
τ²-bench results
GPT-5.5 Pro—Muse Spark91.5%SourceNot directly comparable
DeepSearchQA
GPT-5.5 Pro—Muse Spark74.8%SourceNot directly comparable
CyberGym
GPT-5.5 Pro—Muse Spark43.5%SourceNot directly comparable
Claw-Eval
GPT-5.5 Pro—Muse Spark63.8%SourceNot directly comparable
Coding
Reasoning
ARC-AGI-2
GPT-5.5 Pro—Muse Spark42.5%SourceNot directly comparable
Knowledge
HLE
GPT-5.5 Pro57.2%SourceMuse Spark50.4%SourceGPT-5.5 Pro leads this result
HLE w/o tools
GPT-5.5 Pro43.1%SourceMuse Spark42.8%SourceGPT-5.5 Pro leads this result
GPQA-D
GPT-5.5 Pro—Muse Spark89.5%SourceNot directly comparable
HealthBench Hard
GPT-5.5 Pro—Muse Spark42.8%SourceNot directly comparable
MedXpertQA (Text)
GPT-5.5 Pro—Muse Spark52.6%SourceNot directly comparable
Math
FrontierMath (legacy)
GPT-5.5 Pro52.4%SourceMuse Spark—Not directly comparable
FrontierMath v2 (Tiers 1-3)
Shared sourceGPT-5.5 Pro51.000%Muse Spark39.000%GPT-5.5 Pro leads this result
FrontierMath v2 (Tier 4)
Shared sourceGPT-5.5 Pro39.600%Muse Spark14.600%GPT-5.5 Pro leads this result
Multimodal
CharXiv
GPT-5.5 Pro—Muse Spark86.4%SourceNot directly comparable
MMMU-Pro
GPT-5.5 Pro—Muse Spark80.4%SourceNot directly comparable
ERQA
GPT-5.5 Pro—Muse Spark64.7%SourceNot directly comparable
SimpleVQA
GPT-5.5 Pro—Muse Spark71.3%SourceNot directly comparable
ScreenSpot Pro
GPT-5.5 Pro—Muse Spark84.1%SourceNot directly comparable
ZeroBench
GPT-5.5 Pro—Muse Spark33.0%SourceNot directly comparable
MedXpertQA (MM)
GPT-5.5 Pro—Muse Spark78.4%SourceNot directly comparable
Frequently asked questions
Which is better, GPT-5.5 Pro or Muse Spark?
Muse Spark has the higher public score estimate, 68.39 versus 63.07, 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 Pro or Muse Spark?
GPT-5.5 Pro is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, GPT-5.5 Pro or Muse Spark?
GPT-5.5 Pro scores higher for agentic tasks on the public lane, 60.6 to 58.8. GPT-5.5 Pro is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which costs less, GPT-5.5 Pro 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 Pro or Muse Spark?
GPT-5.5 Pro has the larger documented context window: 1.05M, compared with 262K.
Related comparisons
Last updated September 4, 2026
Watch GPT-5.5 Pro vs Muse Spark
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.