Coding work
Code generation, repair, and software-engineering tasks
Claude Opus 5.5
Claude Opus 5.5 leads on the public coding lane, 87.1 to 65.6, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Claude Opus 5.5 vs GPT-6 Sol
Updated September 23, 2026. Rank says Claude Opus 5.5 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 5.5 has the higher public score estimate, 88.45 versus 82.17, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 7 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 7
- Claude Opus 5.5 only
- 40
- GPT-6 Sol only
- 3
- Like-for-like categories
- 3 / 8
Estimated: Claude Opus 5.5 and GPT-6 SolHow 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 5.5
Claude Opus 5.5 leads on the public agentic lane, 88.8 to 60.3, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: strongerLong documents
Prompts that approach the documented context limit
GPT-6 Sol
GPT-6 Sol has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
Chat turn cost
1K fresh input + 500 output tokens
GPT-6 Sol
GPT-6 Sol has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
GPT-6 Sol
GPT-6 Sol has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesRepository review cost
50K fresh input + 3K output tokens
GPT-6 Sol
GPT-6 Sol has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
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.
87.1Claude Opus 5.565.6GPT-6 Sol
Like-for-like · BenchAlign v5.6
Claude Opus 5.5 leads the like-for-like coding row.
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.
40 Claude Opus 5.5 only
7 Shared
3 GPT-6 Sol 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.
OSWorld 2.0Agentic
Normalized gap 11.8Claude Opus 5.5:48.7%GPT-6 Sol:60.5%AutomationBenchAgentic
Normalized gap 6.8Claude Opus 5.5:40.0%GPT-6 Sol:33.2%
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 v5.6 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 5.5
- 88.8
- Supported · #1/105
- GPT-6 Sol
- 60.3
- Supported · #15/105
- Basis
- BenchAlign v5.6 lane · 11 vs 4 public rows
- Reading
- Claude Opus 5.5 leads
Coding
Like-for-like- Claude Opus 5.5
- 87.1
- Supported · #1/135
- GPT-6 Sol
- 65.6
- Supported · #7/135
- Basis
- BenchAlign v5.6 lane · 9 vs 1 public rows
- Reading
- Claude Opus 5.5 leads
Knowledge
Like-for-like- Claude Opus 5.5
- 90.0
- Supported · #1/160
- GPT-6 Sol
- 79.6
- Supported · #7/160
- Basis
- BenchAlign v5.6 lane · 16 vs 5 public rows
- Reading
- Claude Opus 5.5 leads · intervals overlap
Reasoning
Directional only- Claude Opus 5.5
- 78.5
- #4/18
- GPT-6 Sol
- 78.5
- #5/18
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Multimodal
Not comparable- Claude Opus 5.5
- 88.8
- #3/50
- GPT-6 Sol
- 82.8
- Unranked · 1 rankable row
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Opus 5.5
- Not ranked
- GPT-6 Sol
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Opus 5.5
- Not ranked
- GPT-6 Sol
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Claude Opus 5.5
- Not ranked
- GPT-6 Sol
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Claude Opus 5.5 | GPT-6 Sol | Basis | Reading |
|---|---|---|---|---|
| Agentic | 88.8Supported · #1/105 | 60.3Supported · #15/105 | Like-for-likeBenchAlign v5.6 lane · 11 vs 4 public rows | Claude Opus 5.5 leads |
| Coding | 87.1Supported · #1/135 | 65.6Supported · #7/135 | Like-for-likeBenchAlign v5.6 lane · 9 vs 1 public rows | Claude Opus 5.5 leads |
| Knowledge | 90.0Supported · #1/160 | 79.6Supported · #7/160 | Like-for-likeBenchAlign v5.6 lane · 16 vs 5 public rows | Claude Opus 5.5 leads · intervals overlap |
| Reasoning | 78.5#4/18 | 78.5#5/18 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Multimodal | 88.8#3/50 | 82.8Unranked · 1 rankable row | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 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 v5.6) 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 5.5
- $0.014
- Fits in one request
- GPT-6 Sol
- $0.007
- Fits in one request
GPT-6 Sol has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Claude Opus 5.5
- $0.26
- Fits in one request
- GPT-6 Sol
- $0.13
- Fits in one request
GPT-6 Sol 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 5.5
- $0.32
- Fits in one request
- GPT-6 Sol
- $0.18
- Fits in one request
GPT-6 Sol has the lower modeled cost
Costs use the listed standard API rates.
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 5.5GPT-6 Sol
Context window
Maximum documented context; output-token limits may be lower.
Claude Opus 5.5
GPT-6 Sol
API model ID
Claude Opus 5.5
claude-opus-5-5
Anthropic Claude Opus 5.5 model documentationGPT-6 Sol
gpt-6-sol
OpenAI GPT-6 Sol model documentationCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Opus 5.5
$0.2 per 1M cached input tokens
Claude Opus 5.5 model documentationGPT-6 Sol
$0.2 per 1M cached input tokens
OpenAI GPT-6 Sol model documentationDocumented inputs
Claude Opus 5.5
GPT-6 Sol
text, image
OpenAI GPT-6 Sol model documentationDocumented outputs
Claude Opus 5.5
GPT-6 Sol
Provider availability
Claude Opus 5.5
Generally Available · Claude API, Amazon Bedrock, Google Cloud, Microsoft Foundry, Claude Platform on AWS
Anthropic Claude Opus 5.5 model documentationGPT-6 Sol
Generally Available · OpenAI Responses API, OpenAI Chat Completions API, ChatGPT Work, Codex
OpenAI GPT-6 Sol and Luna launchReasoning profile
Claude Opus 5.5
Reasoning
GPT-6 Sol
Reasoning
Weight access
Claude Opus 5.5
Proprietary
GPT-6 Sol
Proprietary
License
Claude Opus 5.5
Proprietary
GPT-6 Sol
Proprietary
Release date
Claude Opus 5.5
2026-09-22
GPT-6 Sol
2026-09-16
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 5.5 has the higher public score estimate, 88.45 versus 82.17, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.26 vs $0.13. Cache-heavy agent loop: $0.32 vs $0.18.
- Context tradeoff
- GPT-6 Sol has the larger documented window (1.05M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Claude Opus 5.5 or GPT-6 Sol?
Claude Opus 5.5 has the higher public score estimate, 88.45 versus 82.17, 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 5.5 or GPT-6 Sol?
Claude Opus 5.5 leads the public coding lane, 87.1 to 65.6, with Supported evidence for both models and non-overlapping 90% intervals.
Which is better for agentic tasks, Claude Opus 5.5 or GPT-6 Sol?
Claude Opus 5.5 leads the public agentic tasks lane, 88.8 to 60.3, with Supported evidence for both models and non-overlapping 90% intervals.
Which costs less, Claude Opus 5.5 or GPT-6 Sol?
For the stated presets, chat costs $0.014 on Claude Opus 5.5 and $0.007 on GPT-6 Sol; repository review costs $0.26 and $0.13; the cache-heavy agent loop costs $0.32 and $0.18. Costs use the listed standard API rates.
Which has the larger context window, Claude Opus 5.5 or GPT-6 Sol?
GPT-6 Sol has the larger documented context window: 1.05M, compared with 1M.
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 4.0
Claude Opus 5.566.40%SourceGPT-6 Sol—Not directly comparable
Terminal-Bench-Science 0.1
Claude Opus 5.558.7%SourceGPT-6 Sol—Not directly comparable
AutomationBench
Claude Opus 5.540.0%SourceGPT-6 Sol33.2%SourceClaude Opus 5.5 leads this result
HLE w/ tools
Claude Opus 5.567.7%SourceGPT-6 Sol—Not directly comparable
OSWorld 2.0
Claude Opus 5.548.7%SourceGPT-6 Sol60.5%SourceGPT-6 Sol leads this result
LAB all-pass (Harvey held-out)
Claude Opus 5.58.3%SourceGPT-6 Sol—Not directly comparable
LAB criterion-pass (Harvey held-out)
Claude Opus 5.591.2%SourceGPT-6 Sol—Not directly comparable
Toolathlon-Verified
Claude Opus 5.577.8%SourceGPT-6 Sol—Not directly comparable
Toolathlon Verified Pass@3
Claude Opus 5.582.4%SourceGPT-6 Sol—Not directly comparable
Toolathlon Verified Pass³
Claude Opus 5.572.2%SourceGPT-6 Sol—Not directly comparable
Toolathlon Verified avg. turns
Claude Opus 5.526.9 turnsSourceGPT-6 Sol—Not directly comparable
Agents' Last Exam
Claude Opus 5.5—GPT-6 Sol56.4%SourceNot directly comparable
ExploitGym
Claude Opus 5.5—GPT-6 Sol22.1%SourceNot directly comparable
Coding
FrontierCode 1.1 Main
Claude Opus 5.554.4%SourceGPT-6 Sol—Not directly comparable
cursorBench40
Claude Opus 5.557.8%SourceGPT-6 Sol—Not directly comparable
SWE-bench Pro
Claude Opus 5.589.9%SourceGPT-6 Sol—Not directly comparable
SWE Multilingual
Claude Opus 5.593.9%SourceGPT-6 Sol—Not directly comparable
SWE Multimodal
Claude Opus 5.561.4%SourceGPT-6 Sol—Not directly comparable
DeepSWE
Claude Opus 5.574.2%SourceGPT-6 Sol68.8%SourceClaude Opus 5.5 leads this result
FrontierCode 1.1 Extended
Claude Opus 5.563.6%SourceGPT-6 Sol—Not directly comparable
FrontierSWE v2
Claude Opus 5.562.3%SourceGPT-6 Sol—Not directly comparable
ProgramBench
Claude Opus 5.591.2%SourceGPT-6 Sol—Not directly comparable
Multimodal
Chartography (tools)
Claude Opus 5.589.0%SourceGPT-6 Sol—Not directly comparable
Chartography (no tools)
Claude Opus 5.564.4%SourceGPT-6 Sol—Not directly comparable
BenchCAD Vision2Code (no tools)
Claude Opus 5.50.730SourceGPT-6 Sol—Not directly comparable
BenchCAD Vision2Code (tools)
Claude Opus 5.50.962SourceGPT-6 Sol—Not directly comparable
Biomedical image analysis
Claude Opus 5.571.4%SourceGPT-6 Sol—Not directly comparable
OfficeQA
Claude Opus 5.578.9%SourceGPT-6 Sol—Not directly comparable
OfficeQA Pro
Claude Opus 5.567.7%SourceGPT-6 Sol—Not directly comparable
Knowledge
HLE w/o tools
Claude Opus 5.564.4%SourceGPT-6 Sol—Not directly comparable
HealthBench (raw)
Claude Opus 5.568.1%SourceGPT-6 Sol47.1%SourceClaude Opus 5.5 leads this result
HealthBench (length-adjusted)
Claude Opus 5.560.6%SourceGPT-6 Sol53.2%SourceClaude Opus 5.5 leads this result
HealthBench Professional
Claude Opus 5.565.6%SourceGPT-6 Sol60.8%SourceClaude Opus 5.5 leads this result
HealthBench Professional (raw)
Claude Opus 5.577.1%SourceGPT-6 Sol59.5%SourceClaude Opus 5.5 leads this result
BioMysteryBench (human-solvable)
Claude Opus 5.589.3%SourceGPT-6 Sol—Not directly comparable
BioMysteryBench (human-difficult)
Claude Opus 5.550.0%SourceGPT-6 Sol—Not directly comparable
SpatialBench Verified
Claude Opus 5.572.0%SourceGPT-6 Sol—Not directly comparable
SingleCellBench
Claude Opus 5.561.2%SourceGPT-6 Sol—Not directly comparable
Morphology-to-molecule matching
Claude Opus 5.534.0%SourceGPT-6 Sol—Not directly comparable
Medicinal chemistry
Claude Opus 5.563.5%SourceGPT-6 Sol—Not directly comparable
Protein Design
Claude Opus 5.560.2%SourceGPT-6 Sol—Not directly comparable
Protein Design library ranking
Claude Opus 5.556.0%SourceGPT-6 Sol—Not directly comparable
De novo protein-binder design
Claude Opus 5.582.6%SourceGPT-6 Sol—Not directly comparable
Protocols (troubleshooting)
Claude Opus 5.573.7%SourceGPT-6 Sol—Not directly comparable
Protocols (understanding)
Claude Opus 5.569.0%SourceGPT-6 Sol—Not directly comparable
HealthBench Hard
Claude Opus 5.5—GPT-6 Sol30.1%SourceNot directly comparable
Multilingual
Math
50 public results · 7 shared
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Last updated September 23, 2026