Updated October 2, 2026. Rank says Claude Opus 4.7 (Adaptive) 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 4.7 (Adaptive) has the higher public point estimate, 69.65 versus 63.46. Their conditional score ranges overlap. These ranges do not establish rank confidence. 8 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Anthropic
69.65/100
Estimated · Public rank #20
Conditional range 55.3–84.0
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.
Tool use, computer use, and multi-step task completion
Claude Opus 4.7 (Adaptive)
Claude Opus 4.7 (Adaptive) has the higher public agentic point estimate, 60.9 to 39.3, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Claude Opus 4.7 (Adaptive) is scored on Estimated evidence for coding, 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 v5.8
Claude Opus 4.7 (Adaptive) 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.
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.
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.
HLEKnowledge
Normalized gap 13.3SWE-bench VerifiedCoding
Normalized gap 7.2SWE-bench ProCoding
Normalized gap 3.7GPQAKnowledge
Normalized gap 1.8Terminal-Bench 2.0Agentic
Normalized gap 0.3Each 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 4.7 (Adaptive) | Qwen3.7 Max | Basis | Reading |
|---|---|---|---|---|
| Agentic | 60.9Supported · #21/119 | 39.3Supported · #60/119 | Like-for-likeBenchAlign v5.8 lane · 7 vs 10 public rows | Claude Opus 4.7 (Adaptive) leads |
| Coding | 57.5Estimated · #26/144 | 45.4Supported · #54/144 | Directional onlyBenchAlign v5.8 lane · 3 vs 10 public rows | Directional only |
| Knowledge | 63.7Estimated · #33/171 | 59.8Supported · #41/171 | Directional onlyBenchAlign v5.8 lane · 4 vs 9 public rows | Directional only |
| Reasoning | 53.6Unranked · 3 rankable rows | 76.3Unranked · 3 rankable rows | Not comparableProvisional lane · 2 vs 1 weighted rows | Not comparable |
| Multimodal | 50.1#38/49 | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | 100.0#1/16 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Instruction following | Not ranked | 89.2#17/125 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Math | Not ranked | 81.9Unranked · 3 rankable rows | 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
Qwen3.7 Max has no comparable published API token rate.
50K fresh input + 3K output tokens
Qwen3.7 Max 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. Qwen3.7 Max 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 4.7 (Adaptive)
1M
Qwen3.7 Max
1M
Claude Opus 4.7 (Adaptive)
Not sourced
Qwen3.7 Max
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Opus 4.7 (Adaptive)
Not published
Qwen3.7 Max
No comparable hosted API rate
Claude Opus 4.7 (Adaptive)
Not sourced
Qwen3.7 Max
Not sourced
Claude Opus 4.7 (Adaptive)
Not sourced
Qwen3.7 Max
Not sourced
Claude Opus 4.7 (Adaptive)
Not sourced
Qwen3.7 Max
Not sourced
Claude Opus 4.7 (Adaptive)
Reasoning
Qwen3.7 Max
Reasoning
Claude Opus 4.7 (Adaptive)
Proprietary
Qwen3.7 Max
Proprietary
Claude Opus 4.7 (Adaptive)
Proprietary
Qwen3.7 Max
Proprietary
Claude Opus 4.7 (Adaptive)
2026-04-16
Qwen3.7 Max
2026-05-16
Claude Opus 4.7 (Adaptive) has the higher public point estimate, 69.65 versus 63.46. Their conditional score ranges overlap. These ranges do not establish rank confidence. 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, 57.5 to 45.4. Claude Opus 4.7 (Adaptive) is 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) has the higher public agentic tasks point estimate, 60.9 to 39.3, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
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
Qwen3.7 Max leads this result
BrowseComp
Not directly comparable
MCP Atlas
Claude Opus 4.7 (Adaptive) leads this result
OSWorld-Verified
Not directly comparable
CyberGym
Not directly comparable
OSWorld 2.0
Not directly comparable
JobBench
Not directly comparable
QwenClawBench
Not directly comparable
Claw-Eval
Not directly comparable
BFCL v4
Not directly comparable
VITA-Bench
Not directly comparable
HLE w/ tools
Not directly comparable
Gert Labs
Not directly comparable
ResearchClawBench
Not directly comparable
Terminal-Bench 2.1 (Vals)
Not directly comparable
SWE-bench Verified
Claude Opus 4.7 (Adaptive) leads this result
SWE-bench Pro
Claude Opus 4.7 (Adaptive) leads this result
Terminal-Bench 2.0
Qwen3.7 Max leads this result
SWE Multilingual
Not directly comparable
NL2Repo
Not directly comparable
SciCode
Not directly comparable
LiveCodeBench
Not directly comparable
OpenHarmony Bench
Not directly comparable
LiveCodeBench (Vals)
Not directly comparable
SWE-bench (Vals)
Not directly comparable
MRCR v2 128K-256K
Not directly comparable
ARC-AGI-2
Not directly comparable
ARC-AGI-3
Not directly comparable
MRCRv2
Not directly comparable
CritPt
Not directly comparable
GPQA
Claude Opus 4.7 (Adaptive) leads this result
GPQA-D
Claude Opus 4.7 (Adaptive) leads this result
HLE
Claude Opus 4.7 (Adaptive) leads this result
HLE w/o tools
Not directly comparable
MMLU-Pro
Not directly comparable
MMLU-Redux
Not directly comparable
SuperGPQA
Not directly comparable
MMMLU
Not directly comparable
GPQA Diamond (Vals)
Not directly comparable
MMLU-Pro (Vals)
Not directly comparable
MMLU-ProX
Not directly comparable
NOVA-63
Not directly comparable
INCLUDE
Not directly comparable
MAXIFE
Not directly comparable
PolyMath
Not directly comparable
FrontierMath (legacy)
Not directly comparable
HMMT Feb 2026
Not directly comparable
IMOAnswerBench
Not directly comparable
Apex
Not directly comparable
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Last updated October 2, 2026
