Claude Opus 4.7 vs Qwen3.7 Max

Updated October 2, 2026. Rank says Claude Opus 4.7 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

Claude Opus 4.7 has the higher public point estimate, 65.82 versus 63.46. Their conditional score ranges overlap. These ranges do not establish rank confidence. 7 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

Claude Opus 4.7

Anthropic

65.82/100

Estimated · Public rank #30

Conditional range 56.1–75.5

Model B

Qwen3.7 Max

Alibaba

63.46/100

Supported · Public rank #43

90% interval 54.0–72.9

Shared results: 7
Claude Opus 4.7 only: 8
Qwen3.7 Max only: 34
Like-for-like categories: 2 / 8

Estimated: Claude Opus 4.7 · Supported: Qwen3.7 Max. Conditional ranges do not establish rank confidence.How 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.

Coding work
Code generation, repair, and software-engineering tasks
Claude Opus 4.7
Claude Opus 4.7 has the higher public coding point estimate, 57.9 to 45.4, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Claude Opus 4.7
Claude Opus 4.7 has the higher public agentic point estimate, 53.2 to 39.3, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Confidence: limited

Show secondary and unsupported calls

Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: rate-fallback
Repository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
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.

Pick the job you would use them for

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

57.9Claude Opus 4.745.4Qwen3.7 Max

Like-for-like · BenchAlign v5.8

Claude Opus 4.7 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.

Open the coding leaderboard Shortlist models for this job

Same basis rules as the category table below

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.

LiveCodeBench (Vals)Coding
Normalized gap 2.0
Claude Opus 4.7:85.1%
Qwen3.7 Max:87.1%
Claude Opus 4.7 source Qwen3.7 Max source
MMLU-Pro (Vals)Knowledge
Normalized gap 0.6
Claude Opus 4.7:89.9%
Qwen3.7 Max:89.3%
Claude Opus 4.7 source Qwen3.7 Max source

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 v5.8 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.

Agentic

Like-for-like

Claude Opus 4.7: 53.2; Supported · #38/119
Qwen3.7 Max: 39.3; Supported · #60/119
Basis: BenchAlign v5.8 lane · 5 vs 10 public rows
Reading: Claude Opus 4.7 leads · intervals overlap

Coding

Like-for-like

Claude Opus 4.7: 57.9; Supported · #25/144
Qwen3.7 Max: 45.4; Supported · #54/144
Basis: BenchAlign v5.8 lane · 6 vs 10 public rows
Reading: Claude Opus 4.7 leads · intervals overlap

Knowledge

Directional only

Claude Opus 4.7: 63.5; Estimated · #35/171
Qwen3.7 Max: 59.8; Supported · #41/171
Basis: BenchAlign v5.8 lane · 2 vs 9 public rows
Reading: Directional only

Reasoning

Not comparable

Claude Opus 4.7: Not ranked
Qwen3.7 Max: 76.3; Unranked · 3 rankable rows
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Claude Opus 4.7: Not ranked
Qwen3.7 Max: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Claude Opus 4.7: Not ranked
Qwen3.7 Max: 100.0; #1/16
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Claude Opus 4.7: Not ranked
Qwen3.7 Max: 89.2; #17/125
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Math

Not comparable

Claude Opus 4.7: 60.6; Unranked · 2 rankable rows
Qwen3.7 Max: 81.9; Unranked · 3 rankable rows
Basis: Provisional lane · 2 vs 1 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Claude Opus 4.7 and Qwen3.7 Max
Category	Claude Opus 4.7	Qwen3.7 Max	Basis	Reading
Agentic	53.2Supported · #38/119	39.3Supported · #60/119	Like-for-likeBenchAlign v5.8 lane · 5 vs 10 public rows	Claude Opus 4.7 leads · intervals overlap
Coding	57.9Supported · #25/144	45.4Supported · #54/144	Like-for-likeBenchAlign v5.8 lane · 6 vs 10 public rows	Claude Opus 4.7 leads · intervals overlap
Knowledge	63.5Estimated · #35/171	59.8Supported · #41/171	Directional onlyBenchAlign v5.8 lane · 2 vs 9 public rows	Directional only
Reasoning	Not ranked	76.3Unranked · 3 rankable rows	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Multimodal	Not ranked	Not ranked	Not comparableProvisional lane · 0 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	60.6Unranked · 2 rankable rows	81.9Unranked · 3 rankable rows	Not comparableProvisional lane · 2 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.

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 4.7: $0.0175; Fits in one request
Qwen3.7 Max: API rate not published; Fits in one request

Qwen3.7 Max has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Claude Opus 4.7: $0.325; Fits in one request
Qwen3.7 Max: API rate not published; Fits in one request

Qwen3.7 Max has no comparable published API token rate.

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
Qwen3.7 Max: API rate not published; Fits in one request; Cached-input rate unavailable

Claude Opus 4.7 has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.7 Max has no comparable published API token rate.

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 4.7Qwen3.7 Max

Claude Opus 4.7

Non-Reasoning

Qwen3.7 Max

Reasoning

Weight access

Claude Opus 4.7

Proprietary

Qwen3.7 Max

Proprietary

License

Claude Opus 4.7

Proprietary

Qwen3.7 Max

Proprietary

Release date

Claude Opus 4.7

2026-04-16

Qwen3.7 Max

2026-05-16

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 4.7 has the higher public point estimate, 65.82 versus 63.46. Their conditional score ranges overlap. These ranges do not establish rank confidence.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: Both models list 1M.

Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Claude Opus 4.7 or Qwen3.7 Max?

Claude Opus 4.7 has the higher public point estimate, 65.82 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.

Which is better for coding, Claude Opus 4.7 or Qwen3.7 Max?

Claude Opus 4.7 has the higher public coding point estimate, 57.9 to 45.4, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which is better for agentic tasks, Claude Opus 4.7 or Qwen3.7 Max?

Claude Opus 4.7 has the higher public agentic tasks point estimate, 53.2 to 39.3, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which costs less, Claude Opus 4.7 or Qwen3.7 Max?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, Claude Opus 4.7 or Qwen3.7 Max?

Both models list the same context window, 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 evidence49 rows

Agentic

Gert Labs
Shared source
Claude Opus 4.765.59%
Qwen3.7 Max64.27%
Claude Opus 4.7 leads this result
ResearchClawBench
Shared source
Claude Opus 4.720.7%
Qwen3.7 Max18.7%
Claude Opus 4.7 leads this result
OSWorld 2.0
Claude Opus 4.713.9%
Source
Qwen3.7 Max—
Not directly comparable
Terminal-Bench 2.1 (Vals)
Claude Opus 4.768.5%
Source
Qwen3.7 Max61.0%
Source
Claude Opus 4.7 leads this result
ApprenticeBench
Claude Opus 4.77%
Source
Qwen3.7 Max—
Not directly comparable
Terminal-Bench 2.0
Claude Opus 4.7—
Qwen3.7 Max69.7%
Source
Not directly comparable
QwenClawBench
Claude Opus 4.7—
Qwen3.7 Max64.3%
Source
Not directly comparable
Claw-Eval
Claude Opus 4.7—
Qwen3.7 Max65.2%
Source
Not directly comparable
BFCL v4
Claude Opus 4.7—
Qwen3.7 Max75.0%
Source
Not directly comparable
MCP Atlas
Claude Opus 4.7—
Qwen3.7 Max76.4%
Source
Not directly comparable
VITA-Bench
Claude Opus 4.7—
Qwen3.7 Max47.9%
Source
Not directly comparable
HLE w/ tools
Claude Opus 4.7—
Qwen3.7 Max53.5%
Source
Not directly comparable

Coding

Vibe Code Bench
Claude Opus 4.771.00%
Source
Qwen3.7 Max—
Not directly comparable
React Native Evals
Claude Opus 4.782.8%
Source
Qwen3.7 Max—
Not directly comparable
FrontierCode 1.1 Main
Claude Opus 4.738.5%
Source
Qwen3.7 Max—
Not directly comparable
LiveCodeBench (Vals)
Claude Opus 4.785.1%
Source
Qwen3.7 Max87.1%
Source
Qwen3.7 Max leads this result
SWE-bench (Vals)
Claude Opus 4.782.0%
Source
Qwen3.7 Max68.8%
Source
Claude Opus 4.7 leads this result
PostTrainBench v1.1
Claude Opus 4.728.6%
Source
Qwen3.7 Max—
Not directly comparable
SWE-bench Verified
Claude Opus 4.7—
Qwen3.7 Max80.4%
Source
Not directly comparable
SWE-bench Pro
Claude Opus 4.7—
Qwen3.7 Max60.6%
Source
Not directly comparable
SWE Multilingual
Claude Opus 4.7—
Qwen3.7 Max78.3%
Source
Not directly comparable
NL2Repo
Claude Opus 4.7—
Qwen3.7 Max47.2%
Source
Not directly comparable
SciCode
Claude Opus 4.7—
Qwen3.7 Max53.5%
Source
Not directly comparable
LiveCodeBench
Claude Opus 4.7—
Qwen3.7 Max91.6%
Source
Not directly comparable
Terminal-Bench 2.0
Claude Opus 4.7—
Qwen3.7 Max69.7%
Source
Not directly comparable
OpenHarmony Bench
Claude Opus 4.7—
Qwen3.7 Max53.4%
Source
Not directly comparable

Reasoning

MRCRv2
Claude Opus 4.7—
Qwen3.7 Max90.4%
Source
Not directly comparable
CritPt
Claude Opus 4.7—
Qwen3.7 Max13.4%
Source
Not directly comparable

Knowledge

GPQA Diamond (Vals)
Claude Opus 4.790.2%
Source
Qwen3.7 Max90.2%
Source
Tie
MMLU-Pro (Vals)
Claude Opus 4.789.9%
Source
Qwen3.7 Max89.3%
Source
Claude Opus 4.7 leads this result
GPQA
Claude Opus 4.7—
Qwen3.7 Max92.4%
Source
Not directly comparable
GPQA-D
Claude Opus 4.7—
Qwen3.7 Max92.4%
Source
Not directly comparable
HLE
Claude Opus 4.7—
Qwen3.7 Max41.4%
Source
Not directly comparable
MMLU-Pro
Claude Opus 4.7—
Qwen3.7 Max89.6%
Source
Not directly comparable
MMLU-Redux
Claude Opus 4.7—
Qwen3.7 Max95%
Source
Not directly comparable
SuperGPQA
Claude Opus 4.7—
Qwen3.7 Max73.6%
Source
Not directly comparable
MMMLU
Claude Opus 4.7—
Qwen3.7 Max90.3%
Source
Not directly comparable

Multilingual

MMLU-ProX
Claude Opus 4.7—
Qwen3.7 Max87%
Source
Not directly comparable
NOVA-63
Claude Opus 4.7—
Qwen3.7 Max59.0%
Source
Not directly comparable
INCLUDE
Claude Opus 4.7—
Qwen3.7 Max86.2%
Source
Not directly comparable
MAXIFE
Claude Opus 4.7—
Qwen3.7 Max89.2%
Source
Not directly comparable
PolyMath
Claude Opus 4.7—
Qwen3.7 Max86.5%
Source
Not directly comparable

Instruction following

IFEval
Claude Opus 4.7—
Qwen3.7 Max94.3%
Source
Not directly comparable
IFBench
Claude Opus 4.7—
Qwen3.7 Max79.1%
Source
Not directly comparable

Math

FrontierMath v2 (Tiers 1-3)
Claude Opus 4.743.793%
Source
Qwen3.7 Max—
Not directly comparable
FrontierMath v2 (Tier 4)
Claude Opus 4.722.917%
Source
Qwen3.7 Max—
Not directly comparable
HMMT Feb 2026
Claude Opus 4.7—
Qwen3.7 Max97.1%
Source
Not directly comparable
IMOAnswerBench
Claude Opus 4.7—
Qwen3.7 Max90.0%
Source
Not directly comparable
Apex
Claude Opus 4.7—
Qwen3.7 Max44.5%
Source
Not directly comparable

49 public results · 7 shared

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Last updated October 2, 2026

Claude Opus 4.7 vs Qwen3.7 Max

Decision reading

Which one for your work

Coding work

Agentic work

Long documents

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Agentic

Coding

Knowledge

Reasoning

Multimodal

Multilingual

Instruction following

Math

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

If you already use one of these models

Questions

Which is better, Claude Opus 4.7 or Qwen3.7 Max?

Which is better for coding, Claude Opus 4.7 or Qwen3.7 Max?

Which is better for agentic tasks, Claude Opus 4.7 or Qwen3.7 Max?

Which costs less, Claude Opus 4.7 or Qwen3.7 Max?

Which has the larger context window, Claude Opus 4.7 or Qwen3.7 Max?

Benchmark evidence

Agentic

Coding

Reasoning

Knowledge

Multilingual

Instruction following

Math

Related comparisons

Watch Claude Opus 4.7 vs Qwen3.7 Max