Model A

OpenAI

73.4/100

Estimated · Public rank #11

90% interval 64.6–82.3

GPT-5.5 vs Qwen3.8-27B

Updated August 14, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B

Qwen3.8-27B

Alibaba

—

Evidence status unavailable

90% interval unavailable

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.

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.

Coding work
Code generation, repair, and software-engineering tasks
Qwen3.8-27B
Qwen3.8-27B leads on the same 1 weighted benchmark row.
Confidence: limited
Long documents
Prompts that approach the documented context limit
GPT-5.5
GPT-5.5 has the larger documented context window.
Confidence: documented

Show secondary and unsupported calls

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.

31 GPT-5.5 only

7 Shared

20 Qwen3.8-27B only

Shared results: 7
GPT-5.5 only: 31
Qwen3.8-27B only: 20
Like-for-like categories: 2 / 8

1 category uses 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.

Coding

Like-for-like

GPT-5.5: 58.6
Qwen3.8-27B: 61.7
Weighted basis: 1 vs 1 rows
Reading: Qwen3.8-27B leads

Knowledge

Like-for-like

GPT-5.5: 57.8
Qwen3.8-27B: 38.7
Weighted basis: 2 vs 2 rows
Reading: GPT-5.5 leads

Agentic

Directional only

GPT-5.5: 81.6
Qwen3.8-27B: 84.3
Weighted basis: 3 vs 1 rows
Reading: Directional only

Reasoning

Not comparable

GPT-5.5: 85.0
Qwen3.8-27B: Not measured
Weighted basis: 1 vs 0 rows
Reading: Not comparable

Math

Not comparable

GPT-5.5: 47.6
Qwen3.8-27B: Not measured
Weighted basis: 2 vs 0 rows
Reading: Not comparable

Multilingual

Not comparable

GPT-5.5: Not measured
Qwen3.8-27B: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Multimodal

Not comparable

GPT-5.5: 70.4
Qwen3.8-27B: 90.2
Weighted basis: 2 vs 1 rows
Reading: Not comparable

Instruction following

Not comparable

GPT-5.5: Not measured
Qwen3.8-27B: 79.5
Weighted basis: 0 vs 1 rows
Reading: Not comparable

Category averages with the server-provided evidence basis for GPT-5.5 and Qwen3.8-27B
Category	GPT-5.5	Qwen3.8-27B	Weighted basis	Reading
Coding	58.6	61.7	Like-for-like1 vs 1 rows	Qwen3.8-27B leads
Knowledge	57.8	38.7	Like-for-like2 vs 2 rows	GPT-5.5 leads
Agentic	81.6	84.3	Directional only3 vs 1 rows	Directional only
Reasoning	85.0	Not measured	Not comparable1 vs 0 rows	Not comparable
Math	47.6	Not measured	Not comparable2 vs 0 rows	Not comparable
Multilingual	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable
Multimodal	70.4	90.2	Not comparable2 vs 1 rows	Not comparable
Instruction following	Not measured	79.5	Not comparable0 vs 1 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.

HLE
Knowledge
GPT-5.5: 52.2%Qwen3.8-27B: 30.8%Normalized gap 21.4GPT-5.5 source Qwen3.8-27B source
OSWorld-Verified
Agentic
GPT-5.5: 78.7%Qwen3.8-27B: 84.3%Normalized gap 5.6GPT-5.5 source Qwen3.8-27B source
GPQA
Knowledge
GPT-5.5: 93.6%Qwen3.8-27B: 89.2%Normalized gap 4.4GPT-5.5 source Qwen3.8-27B source
SWE-bench Pro
Coding
GPT-5.5: 58.6%Qwen3.8-27B: 61.7%Normalized gap 3.1GPT-5.5 source Qwen3.8-27B 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-5.5: $0.02; Fits in one request
Qwen3.8-27B: Self-hosted; infrastructure cost varies; Fits in one request

Qwen3.8-27B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GPT-5.5: $0.34; Fits in one request
Qwen3.8-27B: Self-hosted; infrastructure cost varies; Fits in one request

Qwen3.8-27B has no comparable published API token rate.

Cache-heavy agent loop

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

GPT-5.5: $0.5; Fits in one request
Qwen3.8-27B: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable

Qwen3.8-27B 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-5.5Qwen3.8-27B

Context window

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

GPT-5.5

OpenAI pricing

Qwen3.8-27B

262K

Qwen3.8-27B model card

API model ID

GPT-5.5

gpt-5.5

OpenAI pricing

Qwen3.8-27B

Not sourced

Cached-input rate

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

GPT-5.5

$0.5 per 1M cached input tokens

OpenAI pricing

Qwen3.8-27B

No comparable hosted API rate

Qwen3.8-27B model card

Documented inputs

GPT-5.5

Not sourced

Qwen3.8-27B

Not sourced

Documented outputs

GPT-5.5

Not sourced

Qwen3.8-27B

Not sourced

Provider availability

GPT-5.5

Not sourced

Qwen3.8-27B

Not sourced

Reasoning profile

GPT-5.5

Reasoning

Qwen3.8-27B

Reasoning

Weight access

GPT-5.5

Proprietary

Qwen3.8-27B

Open Weight

License

GPT-5.5

Proprietary

Qwen3.8-27B

Open Weight

Release date

GPT-5.5

2026-04-23

Qwen3.8-27B

2026-08-05

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: 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: GPT-5.5 has the larger documented window (1M).

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 evidence58 rows

Agentic

Terminal-Bench 2.0
GPT-5.582%
Source
Qwen3.8-27B—
Not directly comparable
CyberGym
GPT-5.581.8%
Source
Qwen3.8-27B—
Not directly comparable
BrowseComp
GPT-5.584.4%
Source
Qwen3.8-27B—
Not directly comparable
OSWorld-Verified
GPT-5.578.7%
Source
Qwen3.8-27B84.3%
Source
Qwen3.8-27B leads this result
MCP Atlas
GPT-5.575.3%
Source
Qwen3.8-27B—
Not directly comparable
Toolathlon
GPT-5.555.6%
Source
Qwen3.8-27B—
Not directly comparable
τ²-bench results
GPT-5.598%
Source
Qwen3.8-27B—
Not directly comparable
Gert Labs
GPT-5.572.93%
Source
Qwen3.8-27B—
Not directly comparable
ResearchClawBench
GPT-5.517.0%
Source
Qwen3.8-27B—
Not directly comparable
OSWorld 2.0
GPT-5.513.0%
Source
Qwen3.8-27B—
Not directly comparable
JobBench
GPT-5.542.7%
Source
Qwen3.8-27B33.4%
Source
GPT-5.5 leads this result
ExploitGym
GPT-5.513.4%
Source
Qwen3.8-27B—
Not directly comparable
Terminal-Bench 2.1
GPT-5.5—
Qwen3.8-27B73.0%
Source
Not directly comparable
CoWorkBench
GPT-5.5—
Qwen3.8-27B70.7%
Source
Not directly comparable
Agents' Last Exam
GPT-5.5—
Qwen3.8-27B42.9%
Source
Not directly comparable
WebArena-Verified
GPT-5.5—
Qwen3.8-27B64.8%
Source
Not directly comparable
AndroidWorld
GPT-5.5—
Qwen3.8-27B81.9%
Source
Not directly comparable

Coding

SWE-bench Pro
GPT-5.558.6%
Source
Qwen3.8-27B61.7%
Source
Qwen3.8-27B leads this result
Terminal-Bench 2.0
GPT-5.582.0%
Source
Qwen3.8-27B—
Not directly comparable
Vibe Code Bench
GPT-5.569.85%
Source
Qwen3.8-27B—
Not directly comparable
React Native Evals
GPT-5.584.7%
Source
Qwen3.8-27B—
Not directly comparable
cursorBench31
GPT-5.559.2%
Source
Qwen3.8-27B—
Not directly comparable
cursorBench32
GPT-5.558.4%
Source
Qwen3.8-27B—
Not directly comparable
FrontierCode 1.1 Main
GPT-5.543.0%
Source
Qwen3.8-27B—
Not directly comparable
APEX-SWE
GPT-5.537.0%
Source
Qwen3.8-27B—
Not directly comparable
EEBench
GPT-5.542.3%
Source
Qwen3.8-27B—
Not directly comparable
CADGenBench Generation
GPT-5.529.7%
Source
Qwen3.8-27B—
Not directly comparable
SpaceXAI MTS Eval
GPT-5.546.4%
Source
Qwen3.8-27B—
Not directly comparable
InferenceEval
GPT-5.538.9%
Source
Qwen3.8-27B—
Not directly comparable
Terminal-Bench 2.1
GPT-5.5—
Qwen3.8-27B73.0%
Source
Not directly comparable
NL2Repo
GPT-5.5—
Qwen3.8-27B42.3%
Source
Not directly comparable
deepSwe
GPT-5.5—
Qwen3.8-27B42.2%
Source
Not directly comparable
LiveCodeBench v6
GPT-5.5—
Qwen3.8-27B90.3%
Source
Not directly comparable

Reasoning

MRCR v2 64K-128K
GPT-5.583.1%
Source
Qwen3.8-27B—
Not directly comparable
MRCR v2 128K-256K
GPT-5.587.5%
Source
Qwen3.8-27B—
Not directly comparable
ARC-AGI-2
GPT-5.585%
Source
Qwen3.8-27B—
Not directly comparable
ARC-AGI-3
GPT-5.50.4%
Source
Qwen3.8-27B—
Not directly comparable

Knowledge

GPQA
GPT-5.593.6%
Source
Qwen3.8-27B89.2%
Source
GPT-5.5 leads this result
GPQA-D
GPT-5.593.6%
Source
Qwen3.8-27B89.2%
Source
GPT-5.5 leads this result
HLE
GPT-5.552.2%
Source
Qwen3.8-27B30.8%
Source
GPT-5.5 leads this result
HLE w/o tools
GPT-5.541.4%
Source
Qwen3.8-27B30.8%
Source
GPT-5.5 leads this result

Math

FrontierMath (legacy)
GPT-5.551.7%
Source
Qwen3.8-27B—
Not directly comparable
FrontierMath v2 (Tiers 1-3)
GPT-5.551.700%
Source
Qwen3.8-27B—
Not directly comparable
FrontierMath v2 (Tier 4)
GPT-5.535.400%
Source
Qwen3.8-27B—
Not directly comparable

Multimodal

MMMU-Pro
GPT-5.581.2%
Source
Qwen3.8-27B—
Not directly comparable
MMMU-Pro w/ Python
GPT-5.583.2%
Source
Qwen3.8-27B—
Not directly comparable
OfficeQA Pro
GPT-5.554.1%
Source
Qwen3.8-27B—
Not directly comparable
MathVision
GPT-5.5—
Qwen3.8-27B90.0%
Source
Not directly comparable
MathVision w/ Python
GPT-5.5—
Qwen3.8-27B94.6%
Source
Not directly comparable
BabyVision
GPT-5.5—
Qwen3.8-27B65.7%
Source
Not directly comparable
BabyVision w/ Python
GPT-5.5—
Qwen3.8-27B85.6%
Source
Not directly comparable
Vision2Web
GPT-5.5—
Qwen3.8-27B62.9%
Source
Not directly comparable
CharXiv w/o tools
GPT-5.5—
Qwen3.8-27B83.7%
Source
Not directly comparable
CharXiv
GPT-5.5—
Qwen3.8-27B90.2%
Source
Not directly comparable
OmniDocBench 1.5
GPT-5.5—
Qwen3.8-27B91.1%
Source
Not directly comparable
RealWorldQA
GPT-5.5—
Qwen3.8-27B85.9%
Source
Not directly comparable
ERQA
GPT-5.5—
Qwen3.8-27B65.5%
Source
Not directly comparable

Instruction following

IFBench
GPT-5.5—
Qwen3.8-27B79.5%
Source
Not directly comparable

Frequently asked questions

Which is better, GPT-5.5 or Qwen3.8-27B?

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, GPT-5.5 or Qwen3.8-27B?

Qwen3.8-27B leads the like-for-like coding comparison across 1 shared weighted benchmark row.

Which is better for agentic tasks, GPT-5.5 or Qwen3.8-27B?

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-5.5 or Qwen3.8-27B?

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-5.5 or Qwen3.8-27B?

GPT-5.5 has the larger documented context window: 1M, compared with 262K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 14, 2026

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GPT-5.5 vs Qwen3.8-27B

Which one for your work

Coding work

Long documents

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

What is actually comparable

Category results, on a stated basis

Coding

Knowledge

Agentic

Reasoning

Math

Multilingual

Multimodal

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

Multimodal

Instruction following

Frequently asked questions

Which is better, GPT-5.5 or Qwen3.8-27B?

Which is better for coding, GPT-5.5 or Qwen3.8-27B?

Which is better for agentic tasks, GPT-5.5 or Qwen3.8-27B?

Which costs less, GPT-5.5 or Qwen3.8-27B?

Which has the larger context window, GPT-5.5 or Qwen3.8-27B?

Related comparisons

Watch GPT-5.5 vs Qwen3.8-27B