Model comparison

GPT-5.6 Sol vs Qwen3.7 Plus

Updated August 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

GPT-5.6 Sol

OpenAI

81.5/100

Supported · Public rank #4

90% interval 77.7–85.3

Qwen3.7 Plus

Alibaba

66.2/100

Supported · Public rank #27

90% interval 56.8–75.5

GPT-5.6 Sol has the higher public score, 81.48 versus 66.16, and the 90% score intervals do not overlap.

7 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.

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
GPT-5.6 Sol
GPT-5.6 Sol 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
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
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
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

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

17 GPT-5.6 Sol only

7 Shared

44 Qwen3.7 Plus only

Shared results: 7
GPT-5.6 Sol only: 17
Qwen3.7 Plus only: 44
Like-for-like categories: 0 / 8

4 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Agentic

Directional only

GPT-5.6 Sol: 92.0
Qwen3.7 Plus: 71.7
Weighted basis: 2 vs 2 rows
Reading: Directional only

Coding

Directional only

GPT-5.6 Sol: 64.6
Qwen3.7 Plus: 75.6
Weighted basis: 1 vs 4 rows
Reading: Directional only

Knowledge

Directional only

GPT-5.6 Sol: 94.6
Qwen3.7 Plus: 60.1
Weighted basis: 1 vs 4 rows
Reading: Directional only

Multimodal

Directional only

GPT-5.6 Sol: 83.0
Qwen3.7 Plus: 81.5
Weighted basis: 1 vs 2 rows
Reading: Directional only

Reasoning

Not comparable

GPT-5.6 Sol: 92.5
Qwen3.7 Plus: 91.7
Weighted basis: 1 vs 1 rows
Reading: Not comparable

Math

Not comparable

GPT-5.6 Sol: 87.5
Qwen3.7 Plus: 92.9
Weighted basis: 2 vs 1 rows
Reading: Not comparable

Multilingual

Not comparable

GPT-5.6 Sol: Not measured
Qwen3.7 Plus: 85.4
Weighted basis: 0 vs 1 rows
Reading: Not comparable

Instruction following

Not comparable

GPT-5.6 Sol: Not measured
Qwen3.7 Plus: 84.5
Weighted basis: 0 vs 2 rows
Reading: Not comparable

Category averages with the server-provided evidence basis for GPT-5.6 Sol and Qwen3.7 Plus
Category	GPT-5.6 Sol	Qwen3.7 Plus	Weighted basis	Reading
Agentic	92.0	71.7	Directional only2 vs 2 rows	Directional only
Coding	64.6	75.6	Directional only1 vs 4 rows	Directional only
Knowledge	94.6	60.1	Directional only1 vs 4 rows	Directional only
Multimodal	83.0	81.5	Directional only1 vs 2 rows	Directional only
Reasoning	92.5	91.7	Not comparable1 vs 1 rows	Not comparable
Math	87.5	92.9	Not comparable2 vs 1 rows	Not comparable
Multilingual	Not measured	85.4	Not comparable0 vs 1 rows	Not comparable
Instruction following	Not measured	84.5	Not comparable0 vs 2 rows	Not comparable

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 2.0
Agentic
GPT-5.6 Sol: 91.9%Qwen3.7 Plus: 70.3%Normalized gap 21.6GPT-5.6 Sol source Qwen3.7 Plus source
SWE-bench Pro
Coding
GPT-5.6 Sol: 64.6%Qwen3.7 Plus: 57.6%Normalized gap 7.0GPT-5.6 Sol source Qwen3.7 Plus source
GPQA
Knowledge
GPT-5.6 Sol: 94.6%Qwen3.7 Plus: 90.3%Normalized gap 4.3GPT-5.6 Sol source Qwen3.7 Plus source
MMMU-Pro
Multimodal
GPT-5.6 Sol: 83%Qwen3.7 Plus: 79%Normalized gap 4.0GPT-5.6 Sol source Qwen3.7 Plus source

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

GPT-5.6 Sol: $0.02; 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

GPT-5.6 Sol: $0.34; 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

GPT-5.6 Sol: $0.5; 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.

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

SpecificationGPT-5.6 SolQwen3.7 Plus

Context window

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

GPT-5.6 Sol

1.05M

OpenAI model catalog

Qwen3.7 Plus

API model ID

GPT-5.6 Sol

gpt-5.6-sol

OpenAI model catalog

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.

GPT-5.6 Sol

$0.5 per 1M cached input tokens

OpenAI pricing

Qwen3.7 Plus

No comparable hosted API rate

Documented inputs

GPT-5.6 Sol

text, image

OpenAI model catalog

Qwen3.7 Plus

Not sourced

Documented outputs

GPT-5.6 Sol

text

OpenAI model catalog

Qwen3.7 Plus

Not sourced

Provider availability

GPT-5.6 Sol

Generally Available · OpenAI Responses API

OpenAI model catalog

Qwen3.7 Plus

Not sourced

Reasoning profile

GPT-5.6 Sol

Reasoning

Qwen3.7 Plus

Reasoning

Weight access

GPT-5.6 Sol

Proprietary

Qwen3.7 Plus

Proprietary

License

GPT-5.6 Sol

Proprietary

Qwen3.7 Plus

Proprietary

Release date

GPT-5.6 Sol

2026-07-09

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: GPT-5.6 Sol has the higher public score, 81.48 versus 66.16, and the 90% score intervals do not overlap.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: GPT-5.6 Sol has the larger documented window (1.05M).

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

Benchmark evidence

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

Browse raw public benchmark evidence68 rows

Agentic

Terminal-Bench 2.0
GPT-5.6 Sol91.9%
Source
Qwen3.7 Plus70.3%
Source
GPT-5.6 Sol leads this result
BrowseComp
GPT-5.6 Sol92.2%
Source
Qwen3.7 Plus—
Not directly comparable
OSWorld 2.0
GPT-5.6 Sol62.6%
Source
Qwen3.7 Plus2.8%
Source
GPT-5.6 Sol leads this result
CyberGym
GPT-5.6 Sol84.5%
Source
Qwen3.7 Plus—
Not directly comparable
ExploitGym
GPT-5.6 Sol33.7%
Source
Qwen3.7 Plus—
Not directly comparable
Toolathlon
GPT-5.6 Sol58%
Source
Qwen3.7 Plus—
Not directly comparable
QwenClawBench
GPT-5.6 Sol—
Qwen3.7 Plus61.8%
Source
Not directly comparable
Claw-Eval
GPT-5.6 Sol—
Qwen3.7 Plus62.7%
Source
Not directly comparable
BFCL v4
GPT-5.6 Sol—
Qwen3.7 Plus72.9%
Source
Not directly comparable
MCP Atlas
GPT-5.6 Sol—
Qwen3.7 Plus73.2%
Source
Not directly comparable
VITA-Bench
GPT-5.6 Sol—
Qwen3.7 Plus45.6%
Source
Not directly comparable
DeepPlanning
GPT-5.6 Sol—
Qwen3.7 Plus62.3%
Source
Not directly comparable
OSWorld-Verified
GPT-5.6 Sol—
Qwen3.7 Plus73.3%
Source
Not directly comparable
AndroidWorld
GPT-5.6 Sol—
Qwen3.7 Plus81.0%
Source
Not directly comparable

Coding

SWE-bench Pro
GPT-5.6 Sol64.6%
Source
Qwen3.7 Plus57.6%
Source
GPT-5.6 Sol leads this result
Terminal-Bench 2.0
GPT-5.6 Sol91.9%
Source
Qwen3.7 Plus70.3%
Source
GPT-5.6 Sol leads this result
deepSwe
GPT-5.6 Sol72.7%
Source
Qwen3.7 Plus—
Not directly comparable
FrontierCode 1.1 Extended
GPT-5.6 Sol60.6%
Source
Qwen3.7 Plus—
Not directly comparable
cursorBench32
GPT-5.6 Sol67.2%
Source
Qwen3.7 Plus—
Not directly comparable
VulcanBench v3
GPT-5.6 Sol87.0%
Source
Qwen3.7 Plus—
Not directly comparable
SWE-bench Verified
GPT-5.6 Sol—
Qwen3.7 Plus77.7%
Source
Not directly comparable
SWE Multilingual
GPT-5.6 Sol—
Qwen3.7 Plus75.8%
Source
Not directly comparable
NL2Repo
GPT-5.6 Sol—
Qwen3.7 Plus41.1%
Source
Not directly comparable
SciCode
GPT-5.6 Sol—
Qwen3.7 Plus51.3%
Source
Not directly comparable
LiveCodeBench
GPT-5.6 Sol—
Qwen3.7 Plus89.6%
Source
Not directly comparable

Reasoning

ARC-AGI-2
GPT-5.6 Sol92.5%
Source
Qwen3.7 Plus—
Not directly comparable
ARC-AGI-3
GPT-5.6 Sol7.8%
Source
Qwen3.7 Plus—
Not directly comparable
GeneBench-Pro
GPT-5.6 Sol28.7%
Source
Qwen3.7 Plus—
Not directly comparable
CritPt
GPT-5.6 Sol—
Qwen3.7 Plus9.1%
Source
Not directly comparable
MRCRv2
GPT-5.6 Sol—
Qwen3.7 Plus91.7%
Source
Not directly comparable

Knowledge

GPQA
GPT-5.6 Sol94.6%
Source
Qwen3.7 Plus90.3%
Source
GPT-5.6 Sol leads this result
GPQA-D
GPT-5.6 Sol94.6%
Source
Qwen3.7 Plus90.3%
Source
GPT-5.6 Sol leads this result
HealthBench Professional
GPT-5.6 Sol60.5%
Source
Qwen3.7 Plus—
Not directly comparable
HealthBench Hard
GPT-5.6 Sol33.1%
Source
Qwen3.7 Plus—
Not directly comparable
HLE
GPT-5.6 Sol—
Qwen3.7 Plus34.7%
Source
Not directly comparable
MMLU-Pro
GPT-5.6 Sol—
Qwen3.7 Plus88.5%
Source
Not directly comparable
MMLU-Redux
GPT-5.6 Sol—
Qwen3.7 Plus94.5%
Source
Not directly comparable
SuperGPQA
GPT-5.6 Sol—
Qwen3.7 Plus71.4%
Source
Not directly comparable
MMMLU
GPT-5.6 Sol—
Qwen3.7 Plus89.0%
Source
Not directly comparable

Math

FrontierMath (legacy)
GPT-5.6 Sol89%
Source
Qwen3.7 Plus—
Not directly comparable
FrontierMath v2 (Tiers 1-3)
GPT-5.6 Sol89.000%
Source
Qwen3.7 Plus—
Not directly comparable
FrontierMath v2 (Tier 4)
GPT-5.6 Sol83.000%
Source
Qwen3.7 Plus—
Not directly comparable
HMMT Feb 2026
GPT-5.6 Sol—
Qwen3.7 Plus92.9%
Source
Not directly comparable
IMOAnswerBench
GPT-5.6 Sol—
Qwen3.7 Plus86.0%
Source
Not directly comparable
Apex
GPT-5.6 Sol—
Qwen3.7 Plus22.7%
Source
Not directly comparable

Multilingual

MMLU-ProX
GPT-5.6 Sol—
Qwen3.7 Plus85.4%
Source
Not directly comparable
NOVA-63
GPT-5.6 Sol—
Qwen3.7 Plus58.8%
Source
Not directly comparable
INCLUDE
GPT-5.6 Sol—
Qwen3.7 Plus83.0%
Source
Not directly comparable
MAXIFE
GPT-5.6 Sol—
Qwen3.7 Plus88.8%
Source
Not directly comparable
PolyMath
GPT-5.6 Sol—
Qwen3.7 Plus84.0%
Source
Not directly comparable

Multimodal

MMMU-Pro
GPT-5.6 Sol83%
Source
Qwen3.7 Plus79%
Source
GPT-5.6 Sol leads this result
MMMU-Pro w/ Python
GPT-5.6 Sol84.6%
Source
Qwen3.7 Plus—
Not directly comparable
MathVision
GPT-5.6 Sol—
Qwen3.7 Plus90.3%
Source
Not directly comparable
CharXiv
GPT-5.6 Sol—
Qwen3.7 Plus85.9%
Source
Not directly comparable
ERQA
GPT-5.6 Sol—
Qwen3.7 Plus69.8%
Source
Not directly comparable
MedXpertQA (MM)
GPT-5.6 Sol—
Qwen3.7 Plus71.0%
Source
Not directly comparable
ScreenSpot Pro
GPT-5.6 Sol—
Qwen3.7 Plus79.0%
Source
Not directly comparable
SimpleVQA
GPT-5.6 Sol—
Qwen3.7 Plus81.7%
Source
Not directly comparable
MMSearch-Plus
GPT-5.6 Sol—
Qwen3.7 Plus41.4%
Source
Not directly comparable
RealWorldQA
GPT-5.6 Sol—
Qwen3.7 Plus86.9%
Source
Not directly comparable
OmniDocBench 1.5
GPT-5.6 Sol—
Qwen3.7 Plus91.4%
Source
Not directly comparable
OCRBench V2
GPT-5.6 Sol—
Qwen3.7 Plus70.7%
Source
Not directly comparable
ODINW13
GPT-5.6 Sol—
Qwen3.7 Plus51.1%
Source
Not directly comparable
Video-MME (with subtitle)
GPT-5.6 Sol—
Qwen3.7 Plus88.0%
Source
Not directly comparable
VideoMMMU
GPT-5.6 Sol—
Qwen3.7 Plus85.4%
Source
Not directly comparable
MLVU (M-Avg)
GPT-5.6 Sol—
Qwen3.7 Plus87.4%
Source
Not directly comparable

Instruction following

IFEval
GPT-5.6 Sol—
Qwen3.7 Plus94.6%
Source
Not directly comparable
IFBench
GPT-5.6 Sol—
Qwen3.7 Plus79.1%
Source
Not directly comparable

Frequently asked questions

Which is better, GPT-5.6 Sol or Qwen3.7 Plus?

GPT-5.6 Sol has the higher public score, 81.48 versus 66.16, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.

Which is better for coding, GPT-5.6 Sol or Qwen3.7 Plus?

The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, GPT-5.6 Sol or Qwen3.7 Plus?

The current agentic tasks averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.

Which costs less, GPT-5.6 Sol 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, GPT-5.6 Sol or Qwen3.7 Plus?

GPT-5.6 Sol has the larger documented context window: 1.05M, compared with 1M.

Related comparisons

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Last updated August 10, 2026

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GPT-5.6 Sol vs Qwen3.7 Plus

Which one for your work

Long documents

Coding work

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

What is actually comparable

Category results, on a stated basis

Agentic

Coding

Knowledge

Multimodal

Reasoning

Math

Multilingual

Instruction following

Shape of the matched evidence

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

Benchmark evidence

Agentic

Coding

Reasoning

Knowledge

Math

Multilingual

Multimodal

Instruction following

Frequently asked questions

Which is better, GPT-5.6 Sol or Qwen3.7 Plus?

Which is better for coding, GPT-5.6 Sol or Qwen3.7 Plus?

Which is better for agentic tasks, GPT-5.6 Sol or Qwen3.7 Plus?

Which costs less, GPT-5.6 Sol or Qwen3.7 Plus?

Which has the larger context window, GPT-5.6 Sol or Qwen3.7 Plus?

Related comparisons

Watch GPT-5.6 Sol vs Qwen3.7 Plus