Model A

OpenAI

64.88/100

Supported · Public rank #39

90% interval 59.4–70.4

GPT-5.2 vs Qwen3.8-27B

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

Model B

Qwen3.8-27B

Alibaba

64.52/100

Supported · Public rank #41

90% interval 59.1–69.9

Decision reading

GPT-5.2 has the higher public score estimate, 64.88 versus 64.52, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

6 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

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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 public coding lane, 53.9 to 46.4, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Qwen3.8-27B
Qwen3.8-27B leads on the public agentic lane, 63.4 to 42.9, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Long documents
Prompts that approach the documented context limit
GPT-5.2
GPT-5.2 has the larger documented context window.
Confidence: documented

Show secondary and unsupported calls

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: 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

What is actually comparable

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

9 GPT-5.2 only

6 Shared

27 Qwen3.8-27B only

Shared results: 6
GPT-5.2 only: 9
Qwen3.8-27B only: 27
Like-for-like categories: 3 / 8

2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign 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

GPT-5.2: 42.9; Supported · #104/152
Qwen3.8-27B: 63.4; Supported · #13/152
Basis: BenchAlign lane · 4 vs 8 public rows
Reading: Qwen3.8-27B leads

Coding

Like-for-like

GPT-5.2: 46.4; Supported · #81/151
Qwen3.8-27B: 53.9; Supported · #43/151
Basis: BenchAlign lane · 3 vs 8 public rows
Reading: Qwen3.8-27B leads · intervals overlap

Knowledge

Like-for-like

GPT-5.2: 61.7; Supported · #30/183
Qwen3.8-27B: 54.1; Supported · #58/183
Basis: BenchAlign lane · 1 vs 6 public rows
Reading: GPT-5.2 leads · intervals overlap

Multimodal

Directional only

GPT-5.2: 66.3; #23/48
Qwen3.8-27B: 80.0; #11/48
Basis: Provisional lane · 2 vs 1 weighted rows
Reading: Directional only

Instruction following

Directional only

GPT-5.2: 92.6; #14/123
Qwen3.8-27B: 84.7; #45/123
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Directional only

Reasoning

Not comparable

GPT-5.2: 53.7; Unranked · 3 rankable rows
Qwen3.8-27B: 77.4; #7/20
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

GPT-5.2: 57.5; Unranked · 2 rankable rows
Qwen3.8-27B: Not ranked
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GPT-5.2: Not ranked
Qwen3.8-27B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GPT-5.2 and Qwen3.8-27B
Category	GPT-5.2	Qwen3.8-27B	Basis	Reading
Agentic	42.9Supported · #104/152	63.4Supported · #13/152	Like-for-likeBenchAlign lane · 4 vs 8 public rows	Qwen3.8-27B leads
Coding	46.4Supported · #81/151	53.9Supported · #43/151	Like-for-likeBenchAlign lane · 3 vs 8 public rows	Qwen3.8-27B leads · intervals overlap
Knowledge	61.7Supported · #30/183	54.1Supported · #58/183	Like-for-likeBenchAlign lane · 1 vs 6 public rows	GPT-5.2 leads · intervals overlap
Multimodal	66.3#23/48	80.0#11/48	Directional onlyProvisional lane · 2 vs 1 weighted rows	Directional only
Instruction following	92.6#14/123	84.7#45/123	Directional onlyProvisional lane · 0 vs 1 weighted rows	Directional only
Reasoning	53.7Unranked · 3 rankable rows	77.4#7/20	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable
Math	57.5Unranked · 2 rankable rows	Not ranked	Not comparableProvisional lane · 2 vs 0 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

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.

OSWorld-Verified
Agentic
GPT-5.2: 47.3%Qwen3.8-27B: 84.3%Normalized gap 37.0GPT-5.2 source Qwen3.8-27B source
CharXiv
Multimodal
GPT-5.2: 82.1%Qwen3.8-27B: 90.2%Normalized gap 8.1GPT-5.2 source Qwen3.8-27B source
SWE-bench Pro
Coding
GPT-5.2: 55.6%Qwen3.8-27B: 61.7%Normalized gap 6.1GPT-5.2 source Qwen3.8-27B source
GPQA
Knowledge
GPT-5.2: 92.4%Qwen3.8-27B: 89.2%Normalized gap 3.2GPT-5.2 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-5.2: $0.00875; 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-5.2: $0.1295; 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-5.2: $0.525; Fits in one request; Cached input priced at the published list-input rate
Qwen3.8-27B: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable

GPT-5.2 has no published cached-input rate, so cached tokens use its listed input rate. 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-5.2Qwen3.8-27B

Context window

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

GPT-5.2

400K

Qwen3.8-27B

262K

Qwen3.8-27B model card

API model ID

GPT-5.2

Not sourced

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-5.2

Not published

Qwen3.8-27B

No comparable hosted API rate

Qwen3.8-27B model card

Documented inputs

GPT-5.2

Not sourced

Qwen3.8-27B

Not sourced

Documented outputs

GPT-5.2

Not sourced

Qwen3.8-27B

Not sourced

Provider availability

GPT-5.2

Not sourced

Qwen3.8-27B

Not sourced

Reasoning profile

GPT-5.2

Reasoning

Qwen3.8-27B

Reasoning

Weight access

GPT-5.2

Proprietary

Qwen3.8-27B

Open Weight

License

GPT-5.2

Proprietary

Qwen3.8-27B

Open Weight

Release date

GPT-5.2

2025-12-11

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: GPT-5.2 has the higher public score estimate, 64.88 versus 64.52, but the 90% score intervals overlap.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: GPT-5.2 has the larger documented window (400K).

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 evidence42 rows

Agentic

BrowseComp
GPT-5.265.8%
Source
Qwen3.8-27B—
Not directly comparable
OSWorld-Verified
GPT-5.247.3%
Source
Qwen3.8-27B84.3%
Source
Qwen3.8-27B leads this result
Gert Labs
GPT-5.246.54%
Source
Qwen3.8-27B—
Not directly comparable
JobBench
GPT-5.234.3%
Source
Qwen3.8-27B33.4%
Source
GPT-5.2 leads this result
Terminal-Bench 2.1
GPT-5.2—
Qwen3.8-27B73.0%
Source
Not directly comparable
CoWorkBench
GPT-5.2—
Qwen3.8-27B70.7%
Source
Not directly comparable
Agents' Last Exam
GPT-5.2—
Qwen3.8-27B42.9%
Source
Not directly comparable
WebArena-Verified
GPT-5.2—
Qwen3.8-27B64.8%
Source
Not directly comparable
AndroidWorld
GPT-5.2—
Qwen3.8-27B81.9%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.2—
Qwen3.8-27B58.4%
Source
Not directly comparable

Coding

SWE-bench Verified
GPT-5.280%
Source
Qwen3.8-27B—
Not directly comparable
SWE-bench Pro
GPT-5.255.6%
Source
Qwen3.8-27B61.7%
Source
Qwen3.8-27B leads this result
Vibe Code Bench
GPT-5.253.50%
Source
Qwen3.8-27B—
Not directly comparable
Terminal-Bench 2.1
GPT-5.2—
Qwen3.8-27B73.0%
Source
Not directly comparable
NL2Repo
GPT-5.2—
Qwen3.8-27B42.3%
Source
Not directly comparable
DeepSWE
GPT-5.2—
Qwen3.8-27B42.2%
Source
Not directly comparable
LiveCodeBench v6
GPT-5.2—
Qwen3.8-27B90.3%
Source
Not directly comparable
VulcanBench v3
GPT-5.2—
Qwen3.8-27B82.6%
Source
Not directly comparable
LiveCodeBench (Vals)
GPT-5.2—
Qwen3.8-27B84.0%
Source
Not directly comparable
SWE-bench (Vals)
GPT-5.2—
Qwen3.8-27B86.0%
Source
Not directly comparable

Reasoning

ARC-AGI-2
GPT-5.252.9%
Source
Qwen3.8-27B—
Not directly comparable

Knowledge

GPQA
GPT-5.292.4%
Source
Qwen3.8-27B89.2%
Source
GPT-5.2 leads this result
GPQA-D
GPT-5.2—
Qwen3.8-27B89.2%
Source
Not directly comparable
HLE
GPT-5.2—
Qwen3.8-27B30.8%
Source
Not directly comparable
HLE w/o tools
GPT-5.2—
Qwen3.8-27B30.8%
Source
Not directly comparable
GPQA Diamond (Vals)
GPT-5.2—
Qwen3.8-27B88.9%
Source
Not directly comparable
MMLU-Pro (Vals)
GPT-5.2—
Qwen3.8-27B84.3%
Source
Not directly comparable

Math

FrontierMath v2 (Tiers 1-3)
GPT-5.240.700%
Source
Qwen3.8-27B—
Not directly comparable
FrontierMath v2 (Tier 4)
GPT-5.218.800%
Source
Qwen3.8-27B—
Not directly comparable

Multimodal

MMMU-Pro
GPT-5.279.5%
Source
Qwen3.8-27B—
Not directly comparable
MathVision
GPT-5.283.0%
Source
Qwen3.8-27B90.0%
Source
Qwen3.8-27B leads this result
CharXiv
GPT-5.282.1%
Source
Qwen3.8-27B90.2%
Source
Qwen3.8-27B leads this result
V*
GPT-5.275.9%
Source
Qwen3.8-27B—
Not directly comparable
MathVision w/ Python
GPT-5.2—
Qwen3.8-27B94.6%
Source
Not directly comparable
BabyVision
GPT-5.2—
Qwen3.8-27B65.7%
Source
Not directly comparable
BabyVision w/ Python
GPT-5.2—
Qwen3.8-27B85.6%
Source
Not directly comparable
Vision2Web
GPT-5.2—
Qwen3.8-27B62.9%
Source
Not directly comparable
CharXiv w/o tools
GPT-5.2—
Qwen3.8-27B83.7%
Source
Not directly comparable
OmniDocBench 1.5
GPT-5.2—
Qwen3.8-27B91.1%
Source
Not directly comparable
RealWorldQA
GPT-5.2—
Qwen3.8-27B85.9%
Source
Not directly comparable
ERQA
GPT-5.2—
Qwen3.8-27B65.5%
Source
Not directly comparable

Instruction following

IFBench
GPT-5.2—
Qwen3.8-27B79.5%
Source
Not directly comparable

Frequently asked questions

Which is better, GPT-5.2 or Qwen3.8-27B?

GPT-5.2 has the higher public score estimate, 64.88 versus 64.52, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, GPT-5.2 or Qwen3.8-27B?

Qwen3.8-27B leads the public coding lane, 53.9 to 46.4, with Supported evidence for both models, although the 90% intervals overlap.

Which is better for agentic tasks, GPT-5.2 or Qwen3.8-27B?

Qwen3.8-27B leads the public agentic tasks lane, 63.4 to 42.9, with Supported evidence for both models and non-overlapping 90% intervals.

Which costs less, GPT-5.2 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-5.2 or Qwen3.8-27B?

GPT-5.2 has the larger documented context window: 400K, compared with 262K.

Related comparisons

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Last updated September 10, 2026

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GPT-5.2 vs Qwen3.8-27B

Which one for your work

Coding work

Agentic work

Long documents

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

Instruction following

Reasoning

Math

Multilingual

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-5.2 or Qwen3.8-27B?

Which is better for coding, GPT-5.2 or Qwen3.8-27B?

Which is better for agentic tasks, GPT-5.2 or Qwen3.8-27B?

Which costs less, GPT-5.2 or Qwen3.8-27B?

Which has the larger context window, GPT-5.2 or Qwen3.8-27B?

Related comparisons

Watch GPT-5.2 vs Qwen3.8-27B