Updated October 6, 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
Claude Opus 5.5 has the higher public score estimate, 86.39 versus 84.9, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 20 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
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.
Code generation, repair, and software-engineering tasks
Claude Opus 5.5
Claude Opus 5.5 has the higher public coding point estimate, 83.5 to 76.6, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Tool use, computer use, and multi-step task completion
Claude Opus 5.5
Claude Opus 5.5 has the higher public agentic point estimate, 88.5 to 70.6, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
GPT-6 Astra
GPT-6 Astra has the larger documented context window.
1K fresh input + 500 output tokens
Claude Opus 5.5
Claude Opus 5.5 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Claude Opus 5.5
Claude Opus 5.5 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Claude Opus 5.5
Claude Opus 5.5 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
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.
Like-for-like · BenchAlign v5.8
Claude Opus 5.5 has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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 23.9ARC-AGI-2Reasoning
Normalized gap 3.3AutomationBenchAgentic
Normalized gap 1.4Each row shows the public-lane category score for both models: the BenchAlign v5.8 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.
| Category | Claude Opus 5.5 | GPT-6 Astra | Basis | Reading |
|---|---|---|---|---|
| Agentic | 88.5Supported · #1/120 | 70.6Supported · #7/120 | Like-for-likeBenchAlign v5.8 lane · 12 vs 11 public rows | Claude Opus 5.5 leads |
| Coding | 83.5Supported · #2/144 | 76.6Supported · #4/144 | Like-for-likeBenchAlign v5.8 lane · 10 vs 5 public rows | Claude Opus 5.5 leads · intervals overlap |
| Knowledge | 88.3Supported · #1/173 | 84.9Supported · #2/173 | Like-for-likeBenchAlign v5.8 lane · 16 vs 7 public rows | Claude Opus 5.5 leads · intervals overlap |
| Reasoning | 82.4#3/27 | 89.6#1/27 | Directional onlyProvisional lane · 1 vs 2 weighted rows | Directional only |
| Multimodal | 88.8#3/49 | 83.9Unranked · 3 rankable rows | 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 | 85.0Unranked · 1 rankable row | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) 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.
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.
1K fresh input + 500 output tokens
Claude Opus 5.5 has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Claude Opus 5.5 has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Claude Opus 5.5 has the lower modeled cost
Costs use the listed standard API rates.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
Claude Opus 5.5
GPT-6 Astra
Claude Opus 5.5
claude-opus-5-5
Anthropic Claude Opus 5.5 model documentationGPT-6 Astra
gpt-6-astra
OpenAI GPT-6 Astra model documentationA 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 Astra
$1 per 1M cached input tokens
OpenAI GPT-6 Astra model documentationClaude Opus 5.5
GPT-6 Astra
text, image
OpenAI GPT-6 Astra model documentationClaude Opus 5.5
GPT-6 Astra
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 Astra
Limited Availability · OpenAI Responses API, OpenAI Chat Completions API, ChatGPT Plus, Pro, Business, and Enterprise, Microsoft Azure, AWS Bedrock
OpenAI GPT-6 Astra model documentationClaude Opus 5.5
Reasoning
GPT-6 Astra
Reasoning
Claude Opus 5.5
Proprietary
GPT-6 Astra
Proprietary
Claude Opus 5.5
Proprietary
GPT-6 Astra
Proprietary
Claude Opus 5.5
2026-09-22
GPT-6 Astra
2026-09-03
Claude Opus 5.5 has the higher public score estimate, 86.39 versus 84.9, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Claude Opus 5.5 has the higher public coding point estimate, 83.5 to 76.6, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Claude Opus 5.5 has the higher public agentic tasks point estimate, 88.5 to 70.6, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
For the stated presets, chat costs $0.014 on Claude Opus 5.5 and $0.035 on GPT-6 Astra; repository review costs $0.26 and $0.65; the cache-heavy agent loop costs $0.32 and $0.9. Costs use the listed standard API rates.
GPT-6 Astra has the larger documented context window: 1.05M, compared with 1M.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 4.0
Claude Opus 5.5 leads this result
Terminal-Bench-Science 0.1
GPT-6 Astra leads this result
AutomationBench
GPT-6 Astra leads this result
HLE w/ tools
Claude Opus 5.5 leads this result
OSWorld 2.0
GPT-6 Astra leads this result
LAB all-pass (Harvey held-out)
Not directly comparable
LAB criterion-pass (Harvey held-out)
Not directly comparable
Toolathlon-Verified
Not directly comparable
Toolathlon Verified Pass@3
Not directly comparable
Toolathlon Verified Pass³
Not directly comparable
Toolathlon Verified avg. turns
Not directly comparable
CWE-bench v1
Shared sourceGPT-6 Astra leads this result
BrowseComp
Not directly comparable
ExploitGym
Not directly comparable
Agents' Last Exam
Not directly comparable
Terminal-Bench 2.1 (Vals)
Not directly comparable
ApprenticeBench
Not directly comparable
FrontierCode 1.1 Main
Claude Opus 5.5 leads this result
CursorBench 4.0
Not directly comparable
SWE-bench Pro
Not directly comparable
SWE Multilingual
Not directly comparable
SWE Multimodal
Not directly comparable
DeepSWE
Claude Opus 5.5 leads this result
FrontierCode 1.1 Extended
GPT-6 Astra leads this result
FrontierSWE v2
Shared sourceGPT-6 Astra leads this result
ProgramBench
Not directly comparable
PostTrainBench v1.1
Shared sourceClaude Opus 5.5 leads this result
ARC-AGI-1
GPT-6 Astra leads this result
ARC-AGI-2
GPT-6 Astra leads this result
GraphWalks BFS 256K–1M
Shared sourceGPT-6 Astra leads this result
ARC-AGI-3
Not directly comparable
GeneBench-Pro
Not directly comparable
MRCR v2 256K-512K
Not directly comparable
MRCR v2 512K-1M
Not directly comparable
Chartography (tools)
Not directly comparable
Chartography (no tools)
Not directly comparable
BenchCAD Vision2Code (no tools)
Not directly comparable
BenchCAD Vision2Code (tools)
Claude Opus 5.5 leads this result
Biomedical image analysis
Not directly comparable
OfficeQA
Not directly comparable
OfficeQA Pro
Not directly comparable
ScreenSpot Pro
Not directly comparable
HLE w/o tools
Not directly comparable
HealthBench (raw)
Claude Opus 5.5 leads this result
HealthBench (length-adjusted)
Claude Opus 5.5 leads this result
HealthBench Professional
Claude Opus 5.5 leads this result
HealthBench Professional (raw)
Claude Opus 5.5 leads this result
BioMysteryBench (human-solvable)
Not directly comparable
BioMysteryBench (human-difficult)
Not directly comparable
SpatialBench Verified
Not directly comparable
SingleCellBench
Not directly comparable
Morphology-to-molecule matching
Not directly comparable
Medicinal chemistry
Not directly comparable
Protein Design
Not directly comparable
Protein Design library ranking
Not directly comparable
De novo protein-binder design
Not directly comparable
Protocols (troubleshooting)
Not directly comparable
Protocols (understanding)
Not directly comparable
GPQA
Not directly comparable
GPQA-D
Not directly comparable
HealthBench Hard
Not directly comparable
Gray Swan IPI (15 attempts)
Shared sourceClaude Opus 5.5 leads this result
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Last updated October 6, 2026
