Updated September 29, 2026. We do not rank this pair: at least one has no public score. 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
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead. 0 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Anthropic
68.46/100
Estimated · Public rank #20
90% interval 57.0–80.0
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.
No workload recommendation clears the current evidence threshold.
Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Claude Opus 4.6 (Adaptive) and Claude Opus 4.7 (Adaptive) are scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Claude Opus 4.6 (Adaptive) is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
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.
Directional only · BenchAlign v5.7
Claude Opus 4.7 (Adaptive) scores higher, but at least one score rests on Estimated evidence or a different benchmark set. Directional only, no winner.
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.
2 categories rest 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.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
Each row shows the public-lane category score for both models: the BenchAlign v5.7 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 4.6 (Adaptive) | Claude Opus 4.7 (Adaptive) | Basis | Reading |
|---|---|---|---|---|
| Agentic | 46.9Estimated · #42/117 | 59.1Supported · #22/117 | Directional onlyBenchAlign v5.7 lane · 0 vs 7 public rows | Directional only |
| Coding | 54.2Estimated · #33/143 | 57.9Estimated · #24/143 | Directional onlyBenchAlign v5.7 lane · 1 vs 3 public rows | Directional only |
| Reasoning | Not ranked | 53.6Unranked · 3 rankable rows | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Multimodal | Not ranked | 50.1#39/50 | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Knowledge | Not ranked | 63.9Estimated · #29/169 | Not comparableBenchAlign v5.7 lane · 0 vs 4 public 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 v5.7) 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 4.6 (Adaptive) has no comparable published API token rate.
50K fresh input + 3K output tokens
Claude Opus 4.6 (Adaptive) has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
Claude Opus 4.7 (Adaptive) has no published cached-input rate, so cached tokens use its listed input rate. Claude Opus 4.6 (Adaptive) has no comparable published API token rate.
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 4.6 (Adaptive)
Claude Opus 4.7 (Adaptive)
1M
Claude Opus 4.6 (Adaptive)
claude-opus-4-6
Anthropic model ID documentationClaude Opus 4.7 (Adaptive)
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Opus 4.6 (Adaptive)
No comparable hosted API rate
Claude Opus 4.7 (Adaptive)
Not published
Claude Opus 4.6 (Adaptive)
text, image
Anthropic model overviewClaude Opus 4.7 (Adaptive)
Not sourced
Claude Opus 4.6 (Adaptive)
Claude Opus 4.7 (Adaptive)
Not sourced
Claude Opus 4.6 (Adaptive)
Generally Available · Claude API
Anthropic model overviewClaude Opus 4.7 (Adaptive)
Not sourced
Claude Opus 4.6 (Adaptive)
Reasoning
Claude Opus 4.7 (Adaptive)
Reasoning
Claude Opus 4.6 (Adaptive)
Proprietary
Claude Opus 4.7 (Adaptive)
Proprietary
Claude Opus 4.6 (Adaptive)
Proprietary
Claude Opus 4.7 (Adaptive)
Proprietary
Claude Opus 4.6 (Adaptive)
2026-02-01
Claude Opus 4.7 (Adaptive)
2026-04-16
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.
Claude Opus 4.7 (Adaptive) scores higher for coding on the public lane, 57.9 to 54.2. Claude Opus 4.6 (Adaptive) and Claude Opus 4.7 (Adaptive) are scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Claude Opus 4.7 (Adaptive) scores higher for agentic tasks on the public lane, 59.1 to 46.9. Claude Opus 4.6 (Adaptive) 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.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Both models list the same context window, 1M.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.0
Not directly comparable
BrowseComp
Not directly comparable
MCP Atlas
Not directly comparable
OSWorld-Verified
Not directly comparable
CyberGym
Not directly comparable
OSWorld 2.0
Not directly comparable
JobBench
Not directly comparable
Vibe Code Bench
Not directly comparable
SWE-bench Verified
Not directly comparable
SWE-bench Pro
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
GPQA
Not directly comparable
GPQA-D
Not directly comparable
HLE
Not directly comparable
HLE w/o tools
Not directly comparable
FrontierMath (legacy)
Not directly comparable
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Last updated September 29, 2026