Qwen3.5 397B vs Qwen3.7 Max

Updated October 2, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

Decision reading

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. 18 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

Qwen3.5 397B

Alibaba

—

Evidence status unavailable

90% interval unavailable

Model B

Qwen3.7 Max

Alibaba

63.46/100

Supported · Public rank #43

90% interval 54.0–72.9

Shared results: 18
Qwen3.5 397B only: 20
Qwen3.7 Max only: 23
Like-for-like categories: 1 / 8

Supported: Qwen3.7 MaxHow 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.

Long documents
Prompts that approach the documented context limit
Qwen3.7 Max
Qwen3.7 Max has the larger documented context window.
Confidence: documented

Show secondary and unsupported calls

Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Qwen3.5 397B is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Qwen3.5 397B is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
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
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Qwen3.5 397B does not fit this workload in one request. Qwen3.5 397B has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.7 Max has no comparable published API token rate.
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.

—Qwen3.5 397B45.4Qwen3.7 Max

Not comparable · BenchAlign v5.8

The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.

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.

Terminal-Bench 2.0Agentic
Normalized gap 17.2
Qwen3.5 397B:52.5%
Qwen3.7 Max:69.7%
Qwen3.5 397B source Qwen3.7 Max source
HLEKnowledge
Normalized gap 12.7
Qwen3.5 397B:28.7%
Qwen3.7 Max:41.4%
Qwen3.5 397B source Qwen3.7 Max source
SWE-bench ProCoding
Normalized gap 9.7
Qwen3.5 397B:50.9%
Qwen3.7 Max:60.6%
Qwen3.5 397B source Qwen3.7 Max source
HMMT Feb 2026Math
Normalized gap 9.2
Qwen3.5 397B:87.9%
Qwen3.7 Max:97.1%
Qwen3.5 397B source Qwen3.7 Max source
SWE-bench VerifiedCoding
Normalized gap 4.2
Qwen3.5 397B:76.2%
Qwen3.7 Max:80.4%
Qwen3.5 397B 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.

Multilingual

Like-for-like

Qwen3.5 397B: 93.8; #5/16
Qwen3.7 Max: 100.0; #1/16
Basis: Provisional lane · 1 vs 1 weighted rows
Reading: Qwen3.7 Max leads

Instruction following

Directional only

Qwen3.5 397B: 0.0; #125/125
Qwen3.7 Max: 89.2; #17/125
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Directional only

Agentic

Not comparable

Qwen3.5 397B: Not ranked
Qwen3.7 Max: 39.3; Supported · #60/119
Basis: BenchAlign v5.8 lane · 13 vs 10 public rows
Reading: Not comparable

Coding

Not comparable

Qwen3.5 397B: Not ranked
Qwen3.7 Max: 45.4; Supported · #54/144
Basis: BenchAlign v5.8 lane · 3 vs 10 public rows
Reading: Not comparable

Reasoning

Not comparable

Qwen3.5 397B: 60.9; Unranked · 2 rankable rows
Qwen3.7 Max: 76.3; Unranked · 3 rankable rows
Basis: Provisional lane · 1 vs 1 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Qwen3.5 397B: 63.9; #27/49
Qwen3.7 Max: Not ranked
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Knowledge

Not comparable

Qwen3.5 397B: Not ranked
Qwen3.7 Max: 59.8; Supported · #41/171
Basis: BenchAlign v5.8 lane · 6 vs 9 public rows
Reading: Not comparable

Math

Not comparable

Qwen3.5 397B: 73.5; Unranked · 5 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 Qwen3.5 397B and Qwen3.7 Max
Category	Qwen3.5 397B	Qwen3.7 Max	Basis	Reading
Multilingual	93.8#5/16	100.0#1/16	Like-for-likeProvisional lane · 1 vs 1 weighted rows	Qwen3.7 Max leads
Instruction following	0.0#125/125	89.2#17/125	Directional onlyProvisional lane · 0 vs 1 weighted rows	Directional only
Agentic	Not ranked	39.3Supported · #60/119	Not comparableBenchAlign v5.8 lane · 13 vs 10 public rows	Not comparable
Coding	Not ranked	45.4Supported · #54/144	Not comparableBenchAlign v5.8 lane · 3 vs 10 public rows	Not comparable
Reasoning	60.9Unranked · 2 rankable rows	76.3Unranked · 3 rankable rows	Not comparableProvisional lane · 1 vs 1 weighted rows	Not comparable
Multimodal	63.9#27/49	Not ranked	Not comparableProvisional lane · 2 vs 0 weighted rows	Not comparable
Knowledge	Not ranked	59.8Supported · #41/171	Not comparableBenchAlign v5.8 lane · 6 vs 9 public rows	Not comparable
Math	73.5Unranked · 5 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

Qwen3.5 397B: $0.0024; 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

Qwen3.5 397B: $0.0408; 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

Qwen3.5 397B: $0.168; Does not fit 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

Qwen3.5 397B does not fit this workload in one request. Qwen3.5 397B 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.

SpecificationQwen3.5 397BQwen3.7 Max

Context window

Maximum documented context; output-token limits may be lower.

Qwen3.5 397B

128K

Qwen3.7 Max

API model ID

Qwen3.5 397B

Not sourced

Qwen3.7 Max

Not sourced

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

Qwen3.5 397B

Not published

Qwen3.7 Max

No comparable hosted API rate

Documented inputs

Qwen3.5 397B

Not sourced

Qwen3.7 Max

Not sourced

Documented outputs

Qwen3.5 397B

Not sourced

Qwen3.7 Max

Not sourced

Provider availability

Qwen3.5 397B

Not sourced

Qwen3.7 Max

Not sourced

Reasoning profile

Qwen3.5 397B

Non-Reasoning

Qwen3.7 Max

Reasoning

Weight access

Qwen3.5 397B

Open Weight

Qwen3.7 Max

Proprietary

License

Qwen3.5 397B

Open Weight

Qwen3.7 Max

Proprietary

Release date

Qwen3.5 397B

2026-02-16

Qwen3.7 Max

2026-05-16

If you already use one of these models

Deployment change: Both entries list Alibaba as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
Quality signal: At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: Qwen3.7 Max has the larger documented window (1M).

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

Questions

Which is better, Qwen3.5 397B or Qwen3.7 Max?

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Qwen3.5 397B or Qwen3.7 Max?

Qwen3.5 397B is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Qwen3.5 397B or Qwen3.7 Max?

Qwen3.5 397B is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Qwen3.5 397B 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, Qwen3.5 397B or Qwen3.7 Max?

Qwen3.7 Max has the larger documented context window: 1M, compared with 128K.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence61 rows

Agentic

Terminal-Bench 2.0
Qwen3.5 397B52.5%
Source
Qwen3.7 Max69.7%
Source
Qwen3.7 Max leads this result
BrowseComp
Qwen3.5 397B62%
Source
Qwen3.7 Max—
Not directly comparable
Claw-Eval
Qwen3.5 397B56.8%
Source
Qwen3.7 Max65.2%
Source
Qwen3.7 Max leads this result
QwenClawBench
Qwen3.5 397B51.8%
Source
Qwen3.7 Max64.3%
Source
Qwen3.7 Max leads this result
τ³-bench results
Qwen3.5 397B68.4%
Source
Qwen3.7 Max—
Not directly comparable
VITA-Bench
Qwen3.5 397B43.7%
Source
Qwen3.7 Max47.9%
Source
Qwen3.7 Max leads this result
DeepPlanning
Qwen3.5 397B37.6%
Source
Qwen3.7 Max—
Not directly comparable
Toolathlon
Qwen3.5 397B36.3%
Source
Qwen3.7 Max—
Not directly comparable
MCP Atlas
Qwen3.5 397B46.1%
Source
Qwen3.7 Max76.4%
Source
Qwen3.7 Max leads this result
MCP-Tasks
Qwen3.5 397B74.2%
Source
Qwen3.7 Max—
Not directly comparable
WideResearch
Qwen3.5 397B74.0%
Source
Qwen3.7 Max—
Not directly comparable
Gert Labs
Shared source
Qwen3.5 397B46.76%
Qwen3.7 Max64.27%
Qwen3.7 Max leads this result
ResearchClawBench
Shared source
Qwen3.5 397B14.2%
Qwen3.7 Max18.7%
Qwen3.7 Max leads this result
BFCL v4
Qwen3.5 397B—
Qwen3.7 Max75.0%
Source
Not directly comparable
HLE w/ tools
Qwen3.5 397B—
Qwen3.7 Max53.5%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
Qwen3.5 397B—
Qwen3.7 Max61.0%
Source
Not directly comparable

Coding

SWE-bench Verified
Qwen3.5 397B76.2%
Source
Qwen3.7 Max80.4%
Source
Qwen3.7 Max leads this result
LiveCodeBench v6
Qwen3.5 397B83.6%
Source
Qwen3.7 Max—
Not directly comparable
SWE-bench Pro
Qwen3.5 397B50.9%
Source
Qwen3.7 Max60.6%
Source
Qwen3.7 Max leads this result
SWE Multilingual
Qwen3.5 397B—
Qwen3.7 Max78.3%
Source
Not directly comparable
NL2Repo
Qwen3.5 397B—
Qwen3.7 Max47.2%
Source
Not directly comparable
SciCode
Qwen3.5 397B—
Qwen3.7 Max53.5%
Source
Not directly comparable
LiveCodeBench
Qwen3.5 397B—
Qwen3.7 Max91.6%
Source
Not directly comparable
Terminal-Bench 2.0
Qwen3.5 397B—
Qwen3.7 Max69.7%
Source
Not directly comparable
OpenHarmony Bench
Qwen3.5 397B—
Qwen3.7 Max53.4%
Source
Not directly comparable
LiveCodeBench (Vals)
Qwen3.5 397B—
Qwen3.7 Max87.1%
Source
Not directly comparable
SWE-bench (Vals)
Qwen3.5 397B—
Qwen3.7 Max68.8%
Source
Not directly comparable

Reasoning

LongBench v2
Qwen3.5 397B63.2%
Source
Qwen3.7 Max—
Not directly comparable
AI-Needle
Qwen3.5 397B68.7%
Source
Qwen3.7 Max—
Not directly comparable
MRCRv2
Qwen3.5 397B—
Qwen3.7 Max90.4%
Source
Not directly comparable
CritPt
Qwen3.5 397B—
Qwen3.7 Max13.4%
Source
Not directly comparable

Multimodal

MMMU-Pro
Qwen3.5 397B79%
Source
Qwen3.7 Max—
Not directly comparable
MathVision
Qwen3.5 397B88.6%
Source
Qwen3.7 Max—
Not directly comparable
CharXiv
Qwen3.5 397B80.8%
Source
Qwen3.7 Max—
Not directly comparable
VideoMMMU
Qwen3.5 397B84.7%
Source
Qwen3.7 Max—
Not directly comparable
ScreenSpot Pro
Qwen3.5 397B65.6%
Source
Qwen3.7 Max—
Not directly comparable
V*
Qwen3.5 397B95.8%
Source
Qwen3.7 Max—
Not directly comparable

Knowledge

GPQA
Qwen3.5 397B88.4%
Source
Qwen3.7 Max92.4%
Source
Qwen3.7 Max leads this result
SuperGPQA
Qwen3.5 397B70.4%
Source
Qwen3.7 Max73.6%
Source
Qwen3.7 Max leads this result
MMLU-Pro
Qwen3.5 397B87.8%
Source
Qwen3.7 Max89.6%
Source
Qwen3.7 Max leads this result
MMLU-Redux
Qwen3.5 397B94.9%
Source
Qwen3.7 Max95%
Source
Qwen3.7 Max leads this result
C-Eval
Qwen3.5 397B93%
Source
Qwen3.7 Max—
Not directly comparable
HLE
Qwen3.5 397B28.7%
Source
Qwen3.7 Max41.4%
Source
Qwen3.7 Max leads this result
GPQA-D
Qwen3.5 397B—
Qwen3.7 Max92.4%
Source
Not directly comparable
MMMLU
Qwen3.5 397B—
Qwen3.7 Max90.3%
Source
Not directly comparable
GPQA Diamond (Vals)
Qwen3.5 397B—
Qwen3.7 Max90.2%
Source
Not directly comparable
MMLU-Pro (Vals)
Qwen3.5 397B—
Qwen3.7 Max89.3%
Source
Not directly comparable

Multilingual

MMLU-ProX
Qwen3.5 397B84.7%
Source
Qwen3.7 Max87%
Source
Qwen3.7 Max leads this result
NOVA-63
Qwen3.5 397B59.1%
Source
Qwen3.7 Max59.0%
Source
Qwen3.5 397B leads this result
INCLUDE
Qwen3.5 397B—
Qwen3.7 Max86.2%
Source
Not directly comparable
MAXIFE
Qwen3.5 397B—
Qwen3.7 Max89.2%
Source
Not directly comparable
PolyMath
Qwen3.5 397B—
Qwen3.7 Max86.5%
Source
Not directly comparable

Instruction following

IFEval
Qwen3.5 397B92.6%
Source
Qwen3.7 Max94.3%
Source
Qwen3.7 Max leads this result
IFBench
Qwen3.5 397B—
Qwen3.7 Max79.1%
Source
Not directly comparable

Math

AIME26
Qwen3.5 397B93.3%
Source
Qwen3.7 Max—
Not directly comparable
HMMT Feb 2025
Qwen3.5 397B94.8%
Source
Qwen3.7 Max—
Not directly comparable
HMMT Nov 2025
Qwen3.5 397B92.7%
Source
Qwen3.7 Max—
Not directly comparable
HMMT Feb 2026
Qwen3.5 397B87.9%
Source
Qwen3.7 Max97.1%
Source
Qwen3.7 Max leads this result
MMAnswerBench
Qwen3.5 397B80.9%
Source
Qwen3.7 Max—
Not directly comparable
IMOAnswerBench
Qwen3.5 397B—
Qwen3.7 Max90.0%
Source
Not directly comparable
Apex
Qwen3.5 397B—
Qwen3.7 Max44.5%
Source
Not directly comparable

61 public results · 18 shared

Related comparisons

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Last updated October 2, 2026

Qwen3.5 397B vs Qwen3.7 Max

Decision reading

Which one for your work

Long documents

Coding work

Agentic work

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

Multilingual

Instruction following

Agentic

Coding

Reasoning

Multimodal

Knowledge

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, Qwen3.5 397B or Qwen3.7 Max?

Which is better for coding, Qwen3.5 397B or Qwen3.7 Max?

Which is better for agentic tasks, Qwen3.5 397B or Qwen3.7 Max?

Which costs less, Qwen3.5 397B or Qwen3.7 Max?

Which has the larger context window, Qwen3.5 397B or Qwen3.7 Max?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Multilingual

Instruction following

Math

Related comparisons

Watch Qwen3.5 397B vs Qwen3.7 Max