Agentic
Like-for-like- Claude Opus 4.7
- 55.8
- Supported · #36/152
- GPT-5.5
- 61.8
- Supported · #15/152
- Basis
- BenchAlign lane · 4 vs 13 public rows
- Reading
- GPT-5.5 leads · intervals overlap
Claude Opus 4.7 vs GPT-5.5
Updated September 8, 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, 72.01 versus 70.32, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
13 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 62.8, 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, 61.8 to 55.8, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Chat turn cost
1K fresh input + 500 output tokens
Claude Opus 4.7
Claude Opus 4.7 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Show secondary and unsupported calls
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
GPT-5.5
GPT-5.5 has the lower estimated token cost for this stated workload. Claude Opus 4.7 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Repository review cost
50K fresh input + 3K output tokens
Claude Opus 4.7
Claude Opus 4.7 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
13 Shared
25 GPT-5.5 only
- Shared results
- 13
- Claude Opus 4.7 only
- 0
- GPT-5.5 only
- 25
- Like-for-like categories
- 2 / 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 Opus 4.7
- 62.8
- Supported · #14/151
- GPT-5.5
- 67.7
- Supported · #8/151
- Basis
- BenchAlign lane · 5 vs 9 public rows
- Reading
- GPT-5.5 leads · intervals overlap
Knowledge
Directional only- Claude Opus 4.7
- 64.7
- Estimated · #26/182
- GPT-5.5
- 72.8
- Supported · #7/182
- Basis
- BenchAlign lane · 2 vs 6 public rows
- Reading
- Directional only
Reasoning
Not comparable- Claude Opus 4.7
- Not ranked
- GPT-5.5
- 63.8
- #12/18
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Math
Not comparable- Claude Opus 4.7
- 60.8
- Unranked · 2 rankable rows
- GPT-5.5
- 69.6
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 2 vs 2 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Opus 4.7
- Not ranked
- GPT-5.5
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Claude Opus 4.7
- Not ranked
- GPT-5.5
- 71.3
- #19/48
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Opus 4.7
- Not ranked
- GPT-5.5
- 93.2
- #7/121
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Claude Opus 4.7 | GPT-5.5 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 55.8Supported · #36/152 | 61.8Supported · #15/152 | Like-for-likeBenchAlign lane · 4 vs 13 public rows | GPT-5.5 leads · intervals overlap |
| Coding | 62.8Supported · #14/151 | 67.7Supported · #8/151 | Like-for-likeBenchAlign lane · 5 vs 9 public rows | GPT-5.5 leads · intervals overlap |
| Knowledge | 64.7Estimated · #26/182 | 72.8Supported · #7/182 | Directional onlyBenchAlign lane · 2 vs 6 public rows | Directional only |
| Reasoning | Not ranked | 63.8#12/18 | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Math | 60.8Unranked · 2 rankable rows | 69.6Unranked · 3 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 | 71.3#19/48 | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Instruction following | Not ranked | 93.2#7/121 | 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
MMLU-Pro (Vals)
Knowledge
OSWorld 2.0
Agentic
LiveCodeBench (Vals)
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 Opus 4.7
- $0.0175
- Fits in one request
- GPT-5.5
- $0.02
- Fits in one request
Claude Opus 4.7 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Claude Opus 4.7
- $0.325
- Fits in one request
- GPT-5.5
- $0.34
- Fits in one request
Claude Opus 4.7 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
- $1.35
- Fits in one request
- Cached input priced at the published list-input rate
- GPT-5.5
- $0.5
- Fits in one request
GPT-5.5 has the lower modeled cost
Claude Opus 4.7 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.
SpecificationClaude Opus 4.7GPT-5.5
Context window
Maximum documented context; output-token limits may be lower.
Claude Opus 4.7
GPT-5.5
API model ID
Claude Opus 4.7
claude-opus-4-7
Anthropic model ID documentationGPT-5.5
gpt-5.5
OpenAI pricingCached-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
GPT-5.5
$0.5 per 1M cached input tokens
OpenAI pricingDocumented inputs
Claude Opus 4.7
text, image
Anthropic model overviewGPT-5.5
Not sourced
Documented outputs
Claude Opus 4.7
GPT-5.5
Not sourced
Provider availability
Claude Opus 4.7
Generally Available · Claude API
Anthropic model overviewGPT-5.5
Not sourced
Reasoning profile
Claude Opus 4.7
Non-Reasoning
GPT-5.5
Reasoning
Weight access
Claude Opus 4.7
Proprietary
GPT-5.5
Proprietary
License
Claude Opus 4.7
Proprietary
GPT-5.5
Proprietary
Release date
Claude Opus 4.7
2026-04-16
GPT-5.5
2026-04-23
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, 72.01 versus 70.32, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.325 vs $0.34. Cache-heavy agent loop: $1.35 vs $0.5.
- 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 evidence38 rows
Agentic
Gert Labs
Shared sourceClaude Opus 4.765.59%GPT-5.572.93%GPT-5.5 leads this result
ResearchClawBench
Shared sourceClaude Opus 4.720.7%GPT-5.517.0%Claude Opus 4.7 leads this result
OSWorld 2.0
Shared sourceClaude Opus 4.713.9%GPT-5.513.0%Claude Opus 4.7 leads this result
Terminal-Bench 2.1 (Vals)
Claude Opus 4.768.5%SourceGPT-5.576.4%SourceGPT-5.5 leads this result
Terminal-Bench 2.0
Claude Opus 4.7—GPT-5.582%SourceNot directly comparable
CyberGym
Claude Opus 4.7—GPT-5.581.8%SourceNot directly comparable
BrowseComp
Claude Opus 4.7—GPT-5.584.4%SourceNot directly comparable
OSWorld-Verified
Claude Opus 4.7—GPT-5.578.7%SourceNot directly comparable
MCP Atlas
Claude Opus 4.7—GPT-5.575.3%SourceNot directly comparable
Toolathlon
Claude Opus 4.7—GPT-5.555.6%SourceNot directly comparable
τ²-bench results
Claude Opus 4.7—GPT-5.598%SourceNot directly comparable
JobBench
Claude Opus 4.7—GPT-5.542.7%SourceNot directly comparable
ExploitGym
Claude Opus 4.7—GPT-5.513.4%SourceNot directly comparable
Coding
Vibe Code Bench
Shared sourceClaude Opus 4.771.00%GPT-5.569.85%Claude Opus 4.7 leads this result
React Native Evals
Shared sourceClaude Opus 4.782.8%GPT-5.584.7%GPT-5.5 leads this result
FrontierCode 1.1 Main
Shared sourceClaude Opus 4.738.5%GPT-5.543.0%GPT-5.5 leads this result
LiveCodeBench (Vals)
Claude Opus 4.785.1%SourceGPT-5.585.3%SourceGPT-5.5 leads this result
SWE-bench (Vals)
Claude Opus 4.782.0%SourceGPT-5.582.6%SourceGPT-5.5 leads this result
SWE-bench Pro
Claude Opus 4.7—GPT-5.558.6%SourceNot directly comparable
Terminal-Bench 2.0
Claude Opus 4.7—GPT-5.582.0%SourceNot directly comparable
cursorBench31
Claude Opus 4.7—GPT-5.559.2%SourceNot directly comparable
cursorBench32
Claude Opus 4.7—GPT-5.558.4%SourceNot directly comparable
Reasoning
Knowledge
GPQA Diamond (Vals)
Claude Opus 4.790.2%SourceGPT-5.593.2%SourceGPT-5.5 leads this result
MMLU-Pro (Vals)
Claude Opus 4.789.9%SourceGPT-5.588.1%SourceClaude Opus 4.7 leads this result
GPQA
Claude Opus 4.7—GPT-5.593.6%SourceNot directly comparable
GPQA-D
Claude Opus 4.7—GPT-5.593.6%SourceNot directly comparable
HLE
Claude Opus 4.7—GPT-5.552.2%SourceNot directly comparable
HLE w/o tools
Claude Opus 4.7—GPT-5.541.4%SourceNot directly comparable
Math
FrontierMath v2 (Tiers 1-3)
Shared sourceClaude Opus 4.743.793%GPT-5.551.700%GPT-5.5 leads this result
FrontierMath v2 (Tier 4)
Shared sourceClaude Opus 4.722.917%GPT-5.535.400%GPT-5.5 leads this result
FrontierMath (legacy)
Claude Opus 4.7—GPT-5.551.7%SourceNot directly comparable
Multimodal
Frequently asked questions
Which is better, Claude Opus 4.7 or GPT-5.5?
GPT-5.5 has the higher public score estimate, 72.01 versus 70.32, 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 GPT-5.5?
GPT-5.5 leads the public coding lane, 67.7 to 62.8, with Supported evidence for both models, although the 90% intervals overlap.
Which is better for agentic tasks, Claude Opus 4.7 or GPT-5.5?
GPT-5.5 leads the public agentic tasks lane, 61.8 to 55.8, with Supported evidence for both models, although the 90% intervals overlap.
Which costs less, Claude Opus 4.7 or GPT-5.5?
For the stated presets, chat costs $0.0175 on Claude Opus 4.7 and $0.02 on GPT-5.5; repository review costs $0.325 and $0.34; the cache-heavy agent loop costs $1.35 and $0.5. Claude Opus 4.7 has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Claude Opus 4.7 or GPT-5.5?
Both models list the same context window, 1M.
Related comparisons
Last updated September 8, 2026
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