DeepSeek V3.2 vs Qwen3.7 Max

Updated October 2, 2026. Rank says Qwen3.7 Max is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

Qwen3.7 Max has the higher public score estimate, 63.46 versus 50.94, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 3 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

DeepSeek V3.2

DeepSeek

50.94/100

Supported · Public rank #87

90% interval 37.5–64.3

Model B

Qwen3.7 Max

Alibaba

63.46/100

Supported · Public rank #43

90% interval 54.0–72.9

Shared results: 3
DeepSeek V3.2 only: 4
Qwen3.7 Max only: 38
Like-for-like categories: 0 / 8

Supported: DeepSeek V3.2 and 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
DeepSeek V3.2 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
DeepSeek V3.2 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. DeepSeek V3.2 does not fit this workload in one request. Qwen3.7 Max has no comparable published API token rate.
Confidence: listed-rates
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.

31.5DeepSeek V3.245.4Qwen3.7 Max

Directional only · BenchAlign v5.8

Qwen3.7 Max 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.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

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.

Coding

Directional only

DeepSeek V3.2: 31.5; Estimated · #90/144
Qwen3.7 Max: 45.4; Supported · #54/144
Basis: BenchAlign v5.8 lane · 2 vs 10 public rows
Reading: Directional only

Knowledge

Directional only

DeepSeek V3.2: 41.6; Estimated · #98/171
Qwen3.7 Max: 59.8; Supported · #41/171
Basis: BenchAlign v5.8 lane · 0 vs 9 public rows
Reading: Directional only

Instruction following

Directional only

DeepSeek V3.2: 56.7; #75/125
Qwen3.7 Max: 89.2; #17/125
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Directional only

Agentic

Not comparable

DeepSeek V3.2: Not ranked
Qwen3.7 Max: 39.3; Supported · #60/119
Basis: BenchAlign v5.8 lane · 3 vs 10 public rows
Reading: Not comparable

Reasoning

Not comparable

DeepSeek V3.2: 53.6; Unranked · 2 rankable rows
Qwen3.7 Max: 76.3; Unranked · 3 rankable rows
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Multimodal

Not comparable

DeepSeek V3.2: Not ranked
Qwen3.7 Max: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

DeepSeek V3.2: Not ranked
Qwen3.7 Max: 100.0; #1/16
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Math

Not comparable

DeepSeek V3.2: 40.0; 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 DeepSeek V3.2 and Qwen3.7 Max
Category	DeepSeek V3.2	Qwen3.7 Max	Basis	Reading
Coding	31.5Estimated · #90/144	45.4Supported · #54/144	Directional onlyBenchAlign v5.8 lane · 2 vs 10 public rows	Directional only
Knowledge	41.6Estimated · #98/171	59.8Supported · #41/171	Directional onlyBenchAlign v5.8 lane · 0 vs 9 public rows	Directional only
Instruction following	56.7#75/125	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 · 3 vs 10 public rows	Not comparable
Reasoning	53.6Unranked · 2 rankable rows	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
Math	40.0Unranked · 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

DeepSeek V3.2: $0.00049; 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

DeepSeek V3.2: $0.01526; 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

DeepSeek V3.2: $0.0154; Does not fit in one request
Qwen3.7 Max: API rate not published; Fits in one request; Cached-input rate unavailable

DeepSeek V3.2 does not fit this workload in one request. 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.

SpecificationDeepSeek V3.2Qwen3.7 Max

Context window

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

DeepSeek V3.2

128K

Qwen3.7 Max

API model ID

DeepSeek V3.2

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.

DeepSeek V3.2

$0.028 per 1M cached input tokens

Qwen3.7 Max

No comparable hosted API rate

Documented inputs

DeepSeek V3.2

Not sourced

Qwen3.7 Max

Not sourced

Documented outputs

DeepSeek V3.2

Not sourced

Qwen3.7 Max

Not sourced

Provider availability

DeepSeek V3.2

Not sourced

Qwen3.7 Max

Not sourced

Reasoning profile

DeepSeek V3.2

Non-Reasoning

Qwen3.7 Max

Reasoning

Weight access

DeepSeek V3.2

Open Weight

Qwen3.7 Max

Proprietary

License

DeepSeek V3.2

Open Weight

Qwen3.7 Max

Proprietary

Release date

DeepSeek V3.2

2025-12-01

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: Qwen3.7 Max has the higher public score estimate, 63.46 versus 50.94, but the 90% score intervals overlap.
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, DeepSeek V3.2 or Qwen3.7 Max?

Qwen3.7 Max has the higher public score estimate, 63.46 versus 50.94, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, DeepSeek V3.2 or Qwen3.7 Max?

Qwen3.7 Max scores higher for coding on the public lane, 45.4 to 31.5. DeepSeek V3.2 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.

Which is better for agentic tasks, DeepSeek V3.2 or Qwen3.7 Max?

DeepSeek V3.2 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, DeepSeek V3.2 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, DeepSeek V3.2 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 evidence45 rows

Agentic

Claw-Eval
DeepSeek V3.240.2%
Source
Qwen3.7 Max65.2%
Source
Qwen3.7 Max leads this result
VITA-Bench
DeepSeek V3.218.5%
Source
Qwen3.7 Max47.9%
Source
Qwen3.7 Max leads this result
Gert Labs
Shared source
DeepSeek V3.229.57%
Qwen3.7 Max64.27%
Qwen3.7 Max leads this result
Terminal-Bench 2.0
DeepSeek V3.2—
Qwen3.7 Max69.7%
Source
Not directly comparable
QwenClawBench
DeepSeek V3.2—
Qwen3.7 Max64.3%
Source
Not directly comparable
BFCL v4
DeepSeek V3.2—
Qwen3.7 Max75.0%
Source
Not directly comparable
MCP Atlas
DeepSeek V3.2—
Qwen3.7 Max76.4%
Source
Not directly comparable
HLE w/ tools
DeepSeek V3.2—
Qwen3.7 Max53.5%
Source
Not directly comparable
ResearchClawBench
DeepSeek V3.2—
Qwen3.7 Max18.7%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
DeepSeek V3.2—
Qwen3.7 Max61.0%
Source
Not directly comparable

Coding

SWE-Rebench
DeepSeek V3.260.9%
Source
Qwen3.7 Max—
Not directly comparable
React Native Evals
DeepSeek V3.271.5%
Source
Qwen3.7 Max—
Not directly comparable
SWE-bench Verified
DeepSeek V3.2—
Qwen3.7 Max80.4%
Source
Not directly comparable
SWE-bench Pro
DeepSeek V3.2—
Qwen3.7 Max60.6%
Source
Not directly comparable
SWE Multilingual
DeepSeek V3.2—
Qwen3.7 Max78.3%
Source
Not directly comparable
NL2Repo
DeepSeek V3.2—
Qwen3.7 Max47.2%
Source
Not directly comparable
SciCode
DeepSeek V3.2—
Qwen3.7 Max53.5%
Source
Not directly comparable
LiveCodeBench
DeepSeek V3.2—
Qwen3.7 Max91.6%
Source
Not directly comparable
Terminal-Bench 2.0
DeepSeek V3.2—
Qwen3.7 Max69.7%
Source
Not directly comparable
OpenHarmony Bench
DeepSeek V3.2—
Qwen3.7 Max53.4%
Source
Not directly comparable
LiveCodeBench (Vals)
DeepSeek V3.2—
Qwen3.7 Max87.1%
Source
Not directly comparable
SWE-bench (Vals)
DeepSeek V3.2—
Qwen3.7 Max68.8%
Source
Not directly comparable

Reasoning

MRCRv2
DeepSeek V3.2—
Qwen3.7 Max90.4%
Source
Not directly comparable
CritPt
DeepSeek V3.2—
Qwen3.7 Max13.4%
Source
Not directly comparable

Knowledge

GPQA
DeepSeek V3.2—
Qwen3.7 Max92.4%
Source
Not directly comparable
GPQA-D
DeepSeek V3.2—
Qwen3.7 Max92.4%
Source
Not directly comparable
HLE
DeepSeek V3.2—
Qwen3.7 Max41.4%
Source
Not directly comparable
MMLU-Pro
DeepSeek V3.2—
Qwen3.7 Max89.6%
Source
Not directly comparable
MMLU-Redux
DeepSeek V3.2—
Qwen3.7 Max95%
Source
Not directly comparable
SuperGPQA
DeepSeek V3.2—
Qwen3.7 Max73.6%
Source
Not directly comparable
MMMLU
DeepSeek V3.2—
Qwen3.7 Max90.3%
Source
Not directly comparable
GPQA Diamond (Vals)
DeepSeek V3.2—
Qwen3.7 Max90.2%
Source
Not directly comparable
MMLU-Pro (Vals)
DeepSeek V3.2—
Qwen3.7 Max89.3%
Source
Not directly comparable

Multilingual

MMLU-ProX
DeepSeek V3.2—
Qwen3.7 Max87%
Source
Not directly comparable
NOVA-63
DeepSeek V3.2—
Qwen3.7 Max59.0%
Source
Not directly comparable
INCLUDE
DeepSeek V3.2—
Qwen3.7 Max86.2%
Source
Not directly comparable
MAXIFE
DeepSeek V3.2—
Qwen3.7 Max89.2%
Source
Not directly comparable
PolyMath
DeepSeek V3.2—
Qwen3.7 Max86.5%
Source
Not directly comparable

Instruction following

IFEval
DeepSeek V3.2—
Qwen3.7 Max94.3%
Source
Not directly comparable
IFBench
DeepSeek V3.2—
Qwen3.7 Max79.1%
Source
Not directly comparable

Math

FrontierMath v2 (Tiers 1-3)
DeepSeek V3.222.100%
Source
Qwen3.7 Max—
Not directly comparable
FrontierMath v2 (Tier 4)
DeepSeek V3.22.100%
Source
Qwen3.7 Max—
Not directly comparable
HMMT Feb 2026
DeepSeek V3.2—
Qwen3.7 Max97.1%
Source
Not directly comparable
IMOAnswerBench
DeepSeek V3.2—
Qwen3.7 Max90.0%
Source
Not directly comparable
Apex
DeepSeek V3.2—
Qwen3.7 Max44.5%
Source
Not directly comparable

45 public results · 3 shared

Related comparisons

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Last updated October 2, 2026

DeepSeek V3.2 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

Coding

Knowledge

Instruction following

Agentic

Reasoning

Multimodal

Multilingual

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, DeepSeek V3.2 or Qwen3.7 Max?

Which is better for coding, DeepSeek V3.2 or Qwen3.7 Max?

Which is better for agentic tasks, DeepSeek V3.2 or Qwen3.7 Max?

Which costs less, DeepSeek V3.2 or Qwen3.7 Max?

Which has the larger context window, DeepSeek V3.2 or Qwen3.7 Max?

Benchmark evidence

Agentic

Coding

Reasoning

Knowledge

Multilingual

Instruction following

Math

Related comparisons

Watch DeepSeek V3.2 vs Qwen3.7 Max