Model A

OpenAI

60.01/100

Estimated · Public rank #66

90% interval 48.5–71.5

GPT-5.4 Pro 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

Qwen3.8-27B has the higher public score estimate, 64.52 versus 60.01, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

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

Share or export

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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.

Long documents
Prompts that approach the documented context limit
GPT-5.4 Pro
GPT-5.4 Pro 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
GPT-5.4 Pro is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-5.4 Pro is scored on Estimated evidence for agentic, so the reading is 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: 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.

8 GPT-5.4 Pro only

2 Shared

31 Qwen3.8-27B only

Shared results: 2
GPT-5.4 Pro only: 8
Qwen3.8-27B only: 31
Like-for-like categories: 0 / 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

Directional only

GPT-5.4 Pro: 55.9; Estimated · #35/152
Qwen3.8-27B: 63.4; Supported · #13/152
Basis: BenchAlign lane · 1 vs 8 public rows
Reading: Directional only

Knowledge

Directional only

GPT-5.4 Pro: 58.5; Estimated · #40/183
Qwen3.8-27B: 54.1; Supported · #58/183
Basis: BenchAlign lane · 4 vs 6 public rows
Reading: Directional only

Coding

Not comparable

GPT-5.4 Pro: Not ranked
Qwen3.8-27B: 53.9; Supported · #43/151
Basis: BenchAlign lane · 0 vs 8 public rows
Reading: Not comparable

Reasoning

Not comparable

GPT-5.4 Pro: 70.1; Unranked · 2 rankable rows
Qwen3.8-27B: 77.4; #7/20
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

GPT-5.4 Pro: 69.0; Unranked · 4 rankable rows
Qwen3.8-27B: Not ranked
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GPT-5.4 Pro: Not ranked
Qwen3.8-27B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

GPT-5.4 Pro: Not ranked
Qwen3.8-27B: 80.0; #11/48
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Instruction following

Not comparable

GPT-5.4 Pro: Not ranked
Qwen3.8-27B: 84.7; #45/123
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GPT-5.4 Pro and Qwen3.8-27B
Category	GPT-5.4 Pro	Qwen3.8-27B	Basis	Reading
Agentic	55.9Estimated · #35/152	63.4Supported · #13/152	Directional onlyBenchAlign lane · 1 vs 8 public rows	Directional only
Knowledge	58.5Estimated · #40/183	54.1Supported · #58/183	Directional onlyBenchAlign lane · 4 vs 6 public rows	Directional only
Coding	Not ranked	53.9Supported · #43/151	Not comparableBenchAlign lane · 0 vs 8 public rows	Not comparable
Reasoning	70.1Unranked · 2 rankable rows	77.4#7/20	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable
Math	69.0Unranked · 4 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
Multimodal	Not ranked	80.0#11/48	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Instruction following	Not ranked	84.7#45/123	Not comparableProvisional lane · 0 vs 1 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.

HLE
Knowledge
GPT-5.4 Pro: 58.7%Qwen3.8-27B: 30.8%Normalized gap 27.9GPT-5.4 Pro source Qwen3.8-27B source
HLE w/o tools
Knowledge
GPT-5.4 Pro: 42.7%Qwen3.8-27B: 30.8%Normalized gap 11.9GPT-5.4 Pro 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-5.4 Pro: $0.12; 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-5.4 Pro: $2.04; 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-5.4 Pro: $8.40; 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-5.4 Pro 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-5.4 ProQwen3.8-27B

Context window

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

GPT-5.4 Pro

1.05M

OpenAI GPT-5.4 Pro model documentation

Qwen3.8-27B

262K

Qwen3.8-27B model card

API model ID

GPT-5.4 Pro

gpt-5.4-pro

OpenAI GPT-5.4 Pro model documentation

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-5.4 Pro

Not published

OpenAI pricing

Qwen3.8-27B

No comparable hosted API rate

Qwen3.8-27B model card

Documented inputs

GPT-5.4 Pro

text, image

OpenAI model catalog

Qwen3.8-27B

Not sourced

Documented outputs

GPT-5.4 Pro

text

OpenAI model catalog

Qwen3.8-27B

Not sourced

Provider availability

GPT-5.4 Pro

Generally Available · OpenAI Responses API

OpenAI model catalog

Qwen3.8-27B

Not sourced

Reasoning profile

GPT-5.4 Pro

Reasoning

Qwen3.8-27B

Reasoning

Weight access

GPT-5.4 Pro

Proprietary

Qwen3.8-27B

Open Weight

License

GPT-5.4 Pro

Proprietary

Qwen3.8-27B

Open Weight

Release date

GPT-5.4 Pro

2026-03-05

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: Qwen3.8-27B has the higher public score estimate, 64.52 versus 60.01, but the 90% score intervals overlap.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: GPT-5.4 Pro 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 evidence41 rows

Agentic

BrowseComp
GPT-5.4 Pro89.3%
Source
Qwen3.8-27B—
Not directly comparable
Terminal-Bench 2.1
GPT-5.4 Pro—
Qwen3.8-27B73.0%
Source
Not directly comparable
CoWorkBench
GPT-5.4 Pro—
Qwen3.8-27B70.7%
Source
Not directly comparable
JobBench
GPT-5.4 Pro—
Qwen3.8-27B33.4%
Source
Not directly comparable
Agents' Last Exam
GPT-5.4 Pro—
Qwen3.8-27B42.9%
Source
Not directly comparable
OSWorld-Verified
GPT-5.4 Pro—
Qwen3.8-27B84.3%
Source
Not directly comparable
WebArena-Verified
GPT-5.4 Pro—
Qwen3.8-27B64.8%
Source
Not directly comparable
AndroidWorld
GPT-5.4 Pro—
Qwen3.8-27B81.9%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.4 Pro—
Qwen3.8-27B58.4%
Source
Not directly comparable

Coding

Terminal-Bench 2.1
GPT-5.4 Pro—
Qwen3.8-27B73.0%
Source
Not directly comparable
SWE-bench Pro
GPT-5.4 Pro—
Qwen3.8-27B61.7%
Source
Not directly comparable
NL2Repo
GPT-5.4 Pro—
Qwen3.8-27B42.3%
Source
Not directly comparable
DeepSWE
GPT-5.4 Pro—
Qwen3.8-27B42.2%
Source
Not directly comparable
LiveCodeBench v6
GPT-5.4 Pro—
Qwen3.8-27B90.3%
Source
Not directly comparable
VulcanBench v3
GPT-5.4 Pro—
Qwen3.8-27B82.6%
Source
Not directly comparable
LiveCodeBench (Vals)
GPT-5.4 Pro—
Qwen3.8-27B84.0%
Source
Not directly comparable
SWE-bench (Vals)
GPT-5.4 Pro—
Qwen3.8-27B86.0%
Source
Not directly comparable

Reasoning

ARC-AGI-2
GPT-5.4 Pro83.3%
Source
Qwen3.8-27B—
Not directly comparable

Knowledge

HLE
GPT-5.4 Pro58.7%
Source
Qwen3.8-27B30.8%
Source
GPT-5.4 Pro leads this result
FrontierScience
GPT-5.4 Pro36.7%
Source
Qwen3.8-27B—
Not directly comparable
FrontierScience Research
GPT-5.4 Pro36.7%
Source
Qwen3.8-27B—
Not directly comparable
HLE w/o tools
GPT-5.4 Pro42.7%
Source
Qwen3.8-27B30.8%
Source
GPT-5.4 Pro leads this result
GPQA
GPT-5.4 Pro—
Qwen3.8-27B89.2%
Source
Not directly comparable
GPQA-D
GPT-5.4 Pro—
Qwen3.8-27B89.2%
Source
Not directly comparable
GPQA Diamond (Vals)
GPT-5.4 Pro—
Qwen3.8-27B88.9%
Source
Not directly comparable
MMLU-Pro (Vals)
GPT-5.4 Pro—
Qwen3.8-27B84.3%
Source
Not directly comparable

Math

IPhO 2025 (Theory)
GPT-5.4 Pro93.5%
Source
Qwen3.8-27B—
Not directly comparable
FrontierMath (legacy)
GPT-5.4 Pro50%
Source
Qwen3.8-27B—
Not directly comparable
FrontierMath v2 (Tiers 1-3)
GPT-5.4 Pro50.000%
Source
Qwen3.8-27B—
Not directly comparable
FrontierMath v2 (Tier 4)
GPT-5.4 Pro37.500%
Source
Qwen3.8-27B—
Not directly comparable

Multimodal

MathVision
GPT-5.4 Pro—
Qwen3.8-27B90.0%
Source
Not directly comparable
MathVision w/ Python
GPT-5.4 Pro—
Qwen3.8-27B94.6%
Source
Not directly comparable
BabyVision
GPT-5.4 Pro—
Qwen3.8-27B65.7%
Source
Not directly comparable
BabyVision w/ Python
GPT-5.4 Pro—
Qwen3.8-27B85.6%
Source
Not directly comparable
Vision2Web
GPT-5.4 Pro—
Qwen3.8-27B62.9%
Source
Not directly comparable
CharXiv w/o tools
GPT-5.4 Pro—
Qwen3.8-27B83.7%
Source
Not directly comparable
CharXiv
GPT-5.4 Pro—
Qwen3.8-27B90.2%
Source
Not directly comparable
OmniDocBench 1.5
GPT-5.4 Pro—
Qwen3.8-27B91.1%
Source
Not directly comparable
RealWorldQA
GPT-5.4 Pro—
Qwen3.8-27B85.9%
Source
Not directly comparable
ERQA
GPT-5.4 Pro—
Qwen3.8-27B65.5%
Source
Not directly comparable

Instruction following

IFBench
GPT-5.4 Pro—
Qwen3.8-27B79.5%
Source
Not directly comparable

Frequently asked questions

Which is better, GPT-5.4 Pro or Qwen3.8-27B?

Qwen3.8-27B has the higher public score estimate, 64.52 versus 60.01, 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-5.4 Pro or Qwen3.8-27B?

GPT-5.4 Pro is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, GPT-5.4 Pro or Qwen3.8-27B?

Qwen3.8-27B scores higher for agentic tasks on the public lane, 63.4 to 55.9. GPT-5.4 Pro is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, GPT-5.4 Pro 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-5.4 Pro or Qwen3.8-27B?

GPT-5.4 Pro has the larger documented context window: 1.05M, compared with 262K.

Related comparisons

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Last updated September 10, 2026

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GPT-5.4 Pro vs Qwen3.8-27B

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

Knowledge

Coding

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-5.4 Pro or Qwen3.8-27B?

Which is better for coding, GPT-5.4 Pro or Qwen3.8-27B?

Which is better for agentic tasks, GPT-5.4 Pro or Qwen3.8-27B?

Which costs less, GPT-5.4 Pro or Qwen3.8-27B?

Which has the larger context window, GPT-5.4 Pro or Qwen3.8-27B?

Related comparisons

Watch GPT-5.4 Pro vs Qwen3.8-27B