Gemini 3.1 Pro vs Qwen3.7 Plus

Updated October 20, 2026. Rank says Gemini 3.1 Pro is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

Gemini 3.1 Pro has the higher public point estimate, 64.72 versus 53.74. Their conditional score ranges overlap. These ranges do not establish rank confidence. 10 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

Gemini 3.1 Pro

Google

64.72/100

Estimated · Public rank #38

Conditional range 55.0–74.5

Model B

Qwen3.7 Plus

Alibaba

53.74/100

Supported · Public rank #77

90% interval 42.7–64.8

Shared results: 10
Gemini 3.1 Pro only: 21
Qwen3.7 Plus only: 43
Like-for-like categories: 2 / 8

Estimated: Gemini 3.1 Pro · Supported: Qwen3.7 Plus. 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.

Agentic work
Tool use, computer use, and multi-step task completion
Gemini 3.1 Pro
Gemini 3.1 Pro has the higher public agentic point estimate, 38.4 to 35.1, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Confidence: limited

Show secondary and unsupported calls

Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Qwen3.7 Plus is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
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: 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.

39.2Gemini 3.1 Pro40.3Qwen3.7 Plus

Directional only · BenchAlign v5.8

Qwen3.7 Plus 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.

FrontierMath v2 (Tiers 1-3)Math
Normalized gap 25.3
Gemini 3.1 Pro:59.649%
Qwen3.7 Plus:34.386%
Shared source
CharXivMultimodal
Normalized gap 5.7
Gemini 3.1 Pro:80.2%
Qwen3.7 Plus:85.9%
Gemini 3.1 Pro source Qwen3.7 Plus source
MMMU-ProMultimodal
Normalized gap 4.9
Gemini 3.1 Pro:83.9%
Qwen3.7 Plus:79%
Gemini 3.1 Pro source Qwen3.7 Plus 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

Gemini 3.1 Pro: 38.4; Supported · #73/131
Qwen3.7 Plus: 35.1; Supported · #83/131
Basis: BenchAlign v5.8 lane · 7 vs 11 public rows
Reading: Gemini 3.1 Pro leads · intervals overlap

Multimodal

Like-for-like

Gemini 3.1 Pro: 80.1; #17/55
Qwen3.7 Plus: 82.0; #16/55
Basis: Provisional lane · 2 vs 2 weighted rows
Reading: Qwen3.7 Plus leads

Coding

Directional only

Gemini 3.1 Pro: 39.2; Supported · #68/152
Qwen3.7 Plus: 40.3; Estimated · #66/152
Basis: BenchAlign v5.8 lane · 6 vs 7 public rows
Reading: Directional only

Knowledge

Directional only

Gemini 3.1 Pro: 67.7; Supported · #26/187
Qwen3.7 Plus: 53.7; Estimated · #67/187
Basis: BenchAlign v5.8 lane · 6 vs 7 public rows
Reading: Directional only

Reasoning

Not comparable

Gemini 3.1 Pro: 58.2; Unranked · 2 rankable rows
Qwen3.7 Plus: 80.0; Unranked · 3 rankable rows
Basis: Provisional lane · 2 vs 1 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Gemini 3.1 Pro: Not ranked
Qwen3.7 Plus: 95.7; #3/17
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Gemini 3.1 Pro: Not ranked
Qwen3.7 Plus: 92.8; #17/149
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Math

Not comparable

Gemini 3.1 Pro: 65.0; Unranked · 2 rankable rows
Qwen3.7 Plus: 64.3; Unranked · 4 rankable rows
Basis: Provisional lane · 2 vs 2 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Gemini 3.1 Pro and Qwen3.7 Plus
Category	Gemini 3.1 Pro	Qwen3.7 Plus	Basis	Reading
Agentic	38.4Supported · #73/131	35.1Supported · #83/131	Like-for-likeBenchAlign v5.8 lane · 7 vs 11 public rows	Gemini 3.1 Pro leads · intervals overlap
Multimodal	80.1#17/55	82.0#16/55	Like-for-likeProvisional lane · 2 vs 2 weighted rows	Qwen3.7 Plus leads
Coding	39.2Supported · #68/152	40.3Estimated · #66/152	Directional onlyBenchAlign v5.8 lane · 6 vs 7 public rows	Directional only
Knowledge	67.7Supported · #26/187	53.7Estimated · #67/187	Directional onlyBenchAlign v5.8 lane · 6 vs 7 public rows	Directional only
Reasoning	58.2Unranked · 2 rankable rows	80.0Unranked · 3 rankable rows	Not comparableProvisional lane · 2 vs 1 weighted rows	Not comparable
Multilingual	Not ranked	95.7#3/17	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Instruction following	Not ranked	92.8#17/149	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Math	65.0Unranked · 2 rankable rows	64.3Unranked · 4 rankable rows	Not comparableProvisional lane · 2 vs 2 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

Gemini 3.1 Pro: $0.008; Fits in one request
Qwen3.7 Plus: API rate not published; Fits in one request

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

Repository review

50K fresh input + 3K output tokens

Gemini 3.1 Pro: $0.136; Fits in one request
Qwen3.7 Plus: API rate not published; Fits in one request

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

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

Gemini 3.1 Pro: $0.2; Fits in one request
Qwen3.7 Plus: API rate not published; Fits in one request; Cached-input rate unavailable

Qwen3.7 Plus 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.

SpecificationGemini 3.1 ProQwen3.7 Plus

Context window

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

Gemini 3.1 Pro

Google Gemini 3.1 Pro Preview model documentation

Qwen3.7 Plus

API model ID

Gemini 3.1 Pro

gemini-3.1-pro-preview

Google Gemini 3.1 Pro Preview model documentation

Qwen3.7 Plus

Not sourced

Cached-input rate

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

Gemini 3.1 Pro

$0.2 per 1M cached input tokens

Google Gemini API pricing

Qwen3.7 Plus

No comparable hosted API rate

Documented inputs

Gemini 3.1 Pro

text, image, video, audio, pdf

Google Gemini 3.1 Pro Preview model documentation

Qwen3.7 Plus

Not sourced

Documented outputs

Gemini 3.1 Pro

text

Google Gemini 3.1 Pro Preview model documentation

Qwen3.7 Plus

Not sourced

Provider availability

Gemini 3.1 Pro

Preview · Gemini API, Google AI Studio

Google Gemini 3.1 Pro Preview model documentation

Qwen3.7 Plus

Not sourced

Reasoning profile

Gemini 3.1 Pro

Reasoning

Qwen3.7 Plus

Reasoning

Weight access

Gemini 3.1 Pro

Proprietary

Qwen3.7 Plus

Proprietary

License

Gemini 3.1 Pro

Proprietary

Qwen3.7 Plus

Proprietary

Release date

Gemini 3.1 Pro

2026-02-19

Qwen3.7 Plus

2026-06-03

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: Gemini 3.1 Pro has the higher public point estimate, 64.72 versus 53.74. 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, Gemini 3.1 Pro or Qwen3.7 Plus?

Gemini 3.1 Pro has the higher public point estimate, 64.72 versus 53.74. 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, Gemini 3.1 Pro or Qwen3.7 Plus?

Qwen3.7 Plus scores higher for coding on the public lane, 40.3 to 39.2. Qwen3.7 Plus 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, Gemini 3.1 Pro or Qwen3.7 Plus?

Gemini 3.1 Pro has the higher public agentic tasks point estimate, 38.4 to 35.1, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which costs less, Gemini 3.1 Pro or Qwen3.7 Plus?

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, Gemini 3.1 Pro or Qwen3.7 Plus?

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 evidence74 rows

Agentic

Claw-Eval
Gemini 3.1 Pro57.8%
Source
Qwen3.7 Plus62.7%
Source
Qwen3.7 Plus leads this result
DeepSearchQA
Gemini 3.1 Pro69.7%
Source
Qwen3.7 Plus—
Not directly comparable
τ²-bench results
Gemini 3.1 Pro95.6%
Source
Qwen3.7 Plus—
Not directly comparable
Gert Labs
Gemini 3.1 Pro56.87%
Source
Qwen3.7 Plus—
Not directly comparable
ResearchClawBench
Gemini 3.1 Pro13.3%
Source
Qwen3.7 Plus—
Not directly comparable
Terminal-Bench 2.1 (Vals)
Gemini 3.1 Pro70.8%
Source
Qwen3.7 Plus52.8%
Source
Gemini 3.1 Pro leads this result
MazeBench
Gemini 3.1 Pro1%
Source
Qwen3.7 Plus—
Not directly comparable
Terminal-Bench 2.0
Gemini 3.1 Pro—
Qwen3.7 Plus70.3%
Source
Not directly comparable
QwenClawBench
Gemini 3.1 Pro—
Qwen3.7 Plus61.8%
Source
Not directly comparable
BFCL v4
Gemini 3.1 Pro—
Qwen3.7 Plus72.9%
Source
Not directly comparable
MCP Atlas
Gemini 3.1 Pro—
Qwen3.7 Plus73.2%
Source
Not directly comparable
VITA-Bench
Gemini 3.1 Pro—
Qwen3.7 Plus45.6%
Source
Not directly comparable
DeepPlanning
Gemini 3.1 Pro—
Qwen3.7 Plus62.3%
Source
Not directly comparable
OSWorld-Verified
Gemini 3.1 Pro—
Qwen3.7 Plus73.3%
Source
Not directly comparable
AndroidWorld
Gemini 3.1 Pro—
Qwen3.7 Plus81.0%
Source
Not directly comparable
OSWorld 2.0
Gemini 3.1 Pro—
Qwen3.7 Plus2.8%
Source
Not directly comparable

Coding

LiveCodeBench Pro
Gemini 3.1 Pro82.9%
Source
Qwen3.7 Plus—
Not directly comparable
React Native Evals
Gemini 3.1 Pro78.9%
Source
Qwen3.7 Plus—
Not directly comparable
Vibe Code Bench
Gemini 3.1 Pro32.03%
Source
Qwen3.7 Plus—
Not directly comparable
LiveCodeBench (Vals)
Gemini 3.1 Pro88.5%
Source
Qwen3.7 Plus—
Not directly comparable
SWE-bench (Vals)
Gemini 3.1 Pro78.8%
Source
Qwen3.7 Plus—
Not directly comparable
PostTrainBench v1.1
Gemini 3.1 Pro22.0%
Source
Qwen3.7 Plus—
Not directly comparable
Terminal-Bench 2.0
Gemini 3.1 Pro—
Qwen3.7 Plus70.3%
Source
Not directly comparable
SWE-bench Verified
Gemini 3.1 Pro—
Qwen3.7 Plus77.7%
Source
Not directly comparable
SWE-bench Pro
Gemini 3.1 Pro—
Qwen3.7 Plus57.6%
Source
Not directly comparable
SWE Multilingual
Gemini 3.1 Pro—
Qwen3.7 Plus75.8%
Source
Not directly comparable
NL2Repo
Gemini 3.1 Pro—
Qwen3.7 Plus41.1%
Source
Not directly comparable
SciCode
Gemini 3.1 Pro—
Qwen3.7 Plus51.3%
Source
Not directly comparable
LiveCodeBench v6
Gemini 3.1 Pro—
Qwen3.7 Plus89.6%
Source
Not directly comparable

Reasoning

ARC-AGI-2
Gemini 3.1 Pro77.1%
Source
Qwen3.7 Plus—
Not directly comparable
ARC-AGI-3
Gemini 3.1 Pro0.4%
Source
Qwen3.7 Plus—
Not directly comparable
CritPt
Gemini 3.1 Pro—
Qwen3.7 Plus9.1%
Source
Not directly comparable
MRCRv2
Gemini 3.1 Pro—
Qwen3.7 Plus91.7%
Source
Not directly comparable

Multimodal

ParseBench
Gemini 3.1 Pro69.14%
Source
Qwen3.7 Plus—
Not directly comparable
MMMU-Pro
Gemini 3.1 Pro83.9%
Source
Qwen3.7 Plus79%
Source
Gemini 3.1 Pro leads this result
CharXiv
Gemini 3.1 Pro80.2%
Source
Qwen3.7 Plus85.9%
Source
Qwen3.7 Plus leads this result
ERQA
Gemini 3.1 Pro69.4%
Source
Qwen3.7 Plus69.8%
Source
Qwen3.7 Plus leads this result
SimpleVQA
Gemini 3.1 Pro72.4%
Source
Qwen3.7 Plus81.7%
Source
Qwen3.7 Plus leads this result
ScreenSpot Pro
Gemini 3.1 Pro84.4%
Source
Qwen3.7 Plus79.0%
Source
Gemini 3.1 Pro leads this result
ZeroBench
Gemini 3.1 Pro29.0%
Source
Qwen3.7 Plus—
Not directly comparable
MedXpertQA (MM)
Gemini 3.1 Pro81.3%
Source
Qwen3.7 Plus71.0%
Source
Gemini 3.1 Pro leads this result
MathVision
Gemini 3.1 Pro—
Qwen3.7 Plus90.3%
Source
Not directly comparable
MMSearch-Plus
Gemini 3.1 Pro—
Qwen3.7 Plus41.4%
Source
Not directly comparable
RealWorldQA
Gemini 3.1 Pro—
Qwen3.7 Plus86.9%
Source
Not directly comparable
OmniDocBench 1.5
Gemini 3.1 Pro—
Qwen3.7 Plus91.4%
Source
Not directly comparable
OCRBench V2
Gemini 3.1 Pro—
Qwen3.7 Plus70.7%
Source
Not directly comparable
ODINW13
Gemini 3.1 Pro—
Qwen3.7 Plus51.1%
Source
Not directly comparable
Video-MME (with subtitle)
Gemini 3.1 Pro—
Qwen3.7 Plus88.0%
Source
Not directly comparable
VideoMMMU
Gemini 3.1 Pro—
Qwen3.7 Plus85.4%
Source
Not directly comparable
MLVU (M-Avg)
Gemini 3.1 Pro—
Qwen3.7 Plus87.4%
Source
Not directly comparable

Knowledge

GPQA-D
Gemini 3.1 Pro94.3%
Source
Qwen3.7 Plus90.3%
Source
Gemini 3.1 Pro leads this result
HLE w/o tools
Gemini 3.1 Pro45.4%
Source
Qwen3.7 Plus—
Not directly comparable
HealthBench Hard
Gemini 3.1 Pro20.6%
Source
Qwen3.7 Plus—
Not directly comparable
MedXpertQA (Text)
Gemini 3.1 Pro71.5%
Source
Qwen3.7 Plus—
Not directly comparable
GPQA Diamond (Vals)
Gemini 3.1 Pro95.5%
Source
Qwen3.7 Plus—
Not directly comparable
MMLU-Pro (Vals)
Gemini 3.1 Pro91.0%
Source
Qwen3.7 Plus—
Not directly comparable
GPQA
Gemini 3.1 Pro—
Qwen3.7 Plus90.3%
Source
Not directly comparable
HLE
Gemini 3.1 Pro—
Qwen3.7 Plus34.7%
Source
Not directly comparable
MMLU-Pro
Gemini 3.1 Pro—
Qwen3.7 Plus88.5%
Source
Not directly comparable
MMLU-Redux
Gemini 3.1 Pro—
Qwen3.7 Plus94.5%
Source
Not directly comparable
SuperGPQA
Gemini 3.1 Pro—
Qwen3.7 Plus71.4%
Source
Not directly comparable
MMMLU
Gemini 3.1 Pro—
Qwen3.7 Plus89.0%
Source
Not directly comparable

Multilingual

MMLU-ProX
Gemini 3.1 Pro—
Qwen3.7 Plus85.4%
Source
Not directly comparable
NOVA-63
Gemini 3.1 Pro—
Qwen3.7 Plus58.8%
Source
Not directly comparable
INCLUDE
Gemini 3.1 Pro—
Qwen3.7 Plus83.0%
Source
Not directly comparable
MAXIFE
Gemini 3.1 Pro—
Qwen3.7 Plus88.8%
Source
Not directly comparable
PolyMath
Gemini 3.1 Pro—
Qwen3.7 Plus84.0%
Source
Not directly comparable

Instruction following

IFEval
Gemini 3.1 Pro—
Qwen3.7 Plus94.6%
Source
Not directly comparable
IFBench
Gemini 3.1 Pro—
Qwen3.7 Plus79.1%
Source
Not directly comparable

Math

FrontierMath v2 (Tiers 1-3)
Shared source
Gemini 3.1 Pro59.649%
Qwen3.7 Plus34.386%
Gemini 3.1 Pro leads this result
FrontierMath v2 (Tier 4)
Gemini 3.1 Pro26.829%
Source
Qwen3.7 Plus—
Not directly comparable
HMMT Feb 2026
Gemini 3.1 Pro—
Qwen3.7 Plus92.9%
Source
Not directly comparable
IMOAnswerBench
Gemini 3.1 Pro—
Qwen3.7 Plus86.0%
Source
Not directly comparable
Apex
Gemini 3.1 Pro—
Qwen3.7 Plus22.7%
Source
Not directly comparable

74 public results · 10 shared

Related comparisons

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Last updated October 20, 2026

Gemini 3.1 Pro vs Qwen3.7 Plus

Decision reading

Which one for your work

Agentic work

Coding 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

Multimodal

Coding

Knowledge

Reasoning

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, Gemini 3.1 Pro or Qwen3.7 Plus?

Which is better for coding, Gemini 3.1 Pro or Qwen3.7 Plus?

Which is better for agentic tasks, Gemini 3.1 Pro or Qwen3.7 Plus?

Which costs less, Gemini 3.1 Pro or Qwen3.7 Plus?

Which has the larger context window, Gemini 3.1 Pro or Qwen3.7 Plus?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Multilingual

Instruction following

Math

Related comparisons

Watch Gemini 3.1 Pro vs Qwen3.7 Plus