Model A

OpenAI

62.19/100

Supported · Public rank #61

90% interval 51.1–73.3

GPT-5.4 nano vs Qwen3.6-27B

Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B

Qwen3.6-27B

Alibaba

52.73/100

Estimated · Public rank #120

90% interval 47.0–58.5

Decision reading

GPT-5.4 nano has the higher public score estimate, 62.19 versus 52.73, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

4 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
Qwen3.6-27B
Qwen3.6-27B leads on the public coding lane, 42.6 to 37.5, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
GPT-5.4 nano
GPT-5.4 nano leads on the public agentic lane, 37.3 to 33.9, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Long documents
Prompts that approach the documented context limit
GPT-5.4 nano
GPT-5.4 nano 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: 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.

14 GPT-5.4 nano only

4 Shared

34 Qwen3.6-27B only

Shared results: 4
GPT-5.4 nano only: 14
Qwen3.6-27B only: 34
Like-for-like categories: 2 / 8

3 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-5.4 nano: 37.3; Supported · #127/151
Qwen3.6-27B: 33.9; Supported · #133/151
Basis: BenchAlign lane · 6 vs 6 public rows
Reading: GPT-5.4 nano leads · intervals overlap

Coding

Like-for-like

GPT-5.4 nano: 37.5; Supported · #150/183
Qwen3.6-27B: 42.6; Supported · #128/183
Basis: BenchAlign lane · 3 vs 6 public rows
Reading: Qwen3.6-27B leads · intervals overlap

Knowledge

Directional only

GPT-5.4 nano: 48.8; Supported · #97/181
Qwen3.6-27B: 49.0; Estimated · #95/181
Basis: BenchAlign lane · 5 vs 6 public rows
Reading: Directional only

Multimodal

Directional only

GPT-5.4 nano: 23.8; #45/48
Qwen3.6-27B: 51.5; #35/48
Basis: Provisional lane · 1 vs 2 weighted rows
Reading: Directional only

Instruction following

Directional only

GPT-5.4 nano: 92.9; #9/120
Qwen3.6-27B: 82.2; #50/120
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Directional only

Reasoning

Not comparable

GPT-5.4 nano: 72.8; Unranked · 2 rankable rows
Qwen3.6-27B: 73.7; Unranked · 2 rankable rows
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

GPT-5.4 nano: 43.9; Unranked · 2 rankable rows
Qwen3.6-27B: 72.8; Unranked · 5 rankable rows
Basis: Provisional lane · 2 vs 2 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GPT-5.4 nano: Not ranked
Qwen3.6-27B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GPT-5.4 nano and Qwen3.6-27B
Category	GPT-5.4 nano	Qwen3.6-27B	Basis	Reading
Agentic	37.3Supported · #127/151	33.9Supported · #133/151	Like-for-likeBenchAlign lane · 6 vs 6 public rows	GPT-5.4 nano leads · intervals overlap
Coding	37.5Supported · #150/183	42.6Supported · #128/183	Like-for-likeBenchAlign lane · 3 vs 6 public rows	Qwen3.6-27B leads · intervals overlap
Knowledge	48.8Supported · #97/181	49.0Estimated · #95/181	Directional onlyBenchAlign lane · 5 vs 6 public rows	Directional only
Multimodal	23.8#45/48	51.5#35/48	Directional onlyProvisional lane · 1 vs 2 weighted rows	Directional only
Instruction following	92.9#9/120	82.2#50/120	Directional onlyProvisional lane · 0 vs 0 weighted rows	Directional only
Reasoning	72.8Unranked · 2 rankable rows	73.7Unranked · 2 rankable rows	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	43.9Unranked · 2 rankable rows	72.8Unranked · 5 rankable rows	Not comparableProvisional lane · 2 vs 2 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.

HLE
Knowledge
GPT-5.4 nano: 37.7%Qwen3.6-27B: 24%Normalized gap 13.7GPT-5.4 nano source Qwen3.6-27B source
Terminal-Bench 2.0
Agentic
GPT-5.4 nano: 46.3%Qwen3.6-27B: 59.3%Normalized gap 13.0GPT-5.4 nano source Qwen3.6-27B source
MMMU-Pro
Multimodal
GPT-5.4 nano: 66.1%Qwen3.6-27B: 75.8%Normalized gap 9.7GPT-5.4 nano source Qwen3.6-27B source
GPQA
Knowledge
GPT-5.4 nano: 82.8%Qwen3.6-27B: 87.8%Normalized gap 5.0GPT-5.4 nano source Qwen3.6-27B 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 nano: $0.00082; Fits in one request
Qwen3.6-27B: Self-hosted; infrastructure cost varies; Fits in one request

Qwen3.6-27B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GPT-5.4 nano: $0.01375; Fits in one request
Qwen3.6-27B: Self-hosted; infrastructure cost varies; Fits in one request

Qwen3.6-27B has no comparable published API token rate.

Cache-heavy agent loop

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

GPT-5.4 nano: $0.0205; Fits in one request
Qwen3.6-27B: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable

Qwen3.6-27B 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 nanoQwen3.6-27B

Context window

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

GPT-5.4 nano

400K

OpenAI GPT-5.4 nano model documentation

Qwen3.6-27B

262K

API model ID

GPT-5.4 nano

gpt-5.4-nano

OpenAI GPT-5.4 nano model documentation

Qwen3.6-27B

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 nano

$0.02 per 1M cached input tokens

OpenAI pricing

Qwen3.6-27B

No comparable hosted API rate

Documented inputs

GPT-5.4 nano

text, image

OpenAI model catalog

Qwen3.6-27B

Not sourced

Documented outputs

GPT-5.4 nano

text

OpenAI model catalog

Qwen3.6-27B

Not sourced

Provider availability

GPT-5.4 nano

Generally Available · OpenAI Responses API

OpenAI model catalog

Qwen3.6-27B

Not sourced

Reasoning profile

GPT-5.4 nano

Reasoning

Qwen3.6-27B

Reasoning

Weight access

GPT-5.4 nano

Proprietary

Qwen3.6-27B

Open Weight

License

GPT-5.4 nano

Proprietary

Qwen3.6-27B

Open Weight

Release date

GPT-5.4 nano

2026-03-17

Qwen3.6-27B

2026-04-21

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-5.4 nano has the higher public score estimate, 62.19 versus 52.73, 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 nano has the larger documented window (400K).

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

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

GPT-5.4 nano

API / mo$1,088

Self-host / moNot listed

Break-even—

Proprietary model — self-hosting not applicable.

Qwen3.6-27B

API / mo$0

Self-host / mo$429

Break-even—

Model the full break-even

Benchmark evidence

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

Browse raw public benchmark evidence52 rows

Agentic

Terminal-Bench 2.0
GPT-5.4 nano46.3%
Source
Qwen3.6-27B59.3%
Source
Qwen3.6-27B leads this result
OSWorld-Verified
GPT-5.4 nano39%
Source
Qwen3.6-27B—
Not directly comparable
MCP Atlas
GPT-5.4 nano56.1%
Source
Qwen3.6-27B—
Not directly comparable
Toolathlon
GPT-5.4 nano35.5%
Source
Qwen3.6-27B—
Not directly comparable
τ²-bench results
GPT-5.4 nano92.5%
Source
Qwen3.6-27B—
Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.4 nano41.6%
Source
Qwen3.6-27B—
Not directly comparable
Claw-Eval
GPT-5.4 nano—
Qwen3.6-27B72.4%
Source
Not directly comparable
QwenClawBench
GPT-5.4 nano—
Qwen3.6-27B53.4%
Source
Not directly comparable
QwenWebBench
GPT-5.4 nano—
Qwen3.6-27B1487
Source
Not directly comparable
AndroidWorld
GPT-5.4 nano—
Qwen3.6-27B70.3%
Source
Not directly comparable
Gert Labs
GPT-5.4 nano—
Qwen3.6-27B54.84%
Source
Not directly comparable

Coding

Vibe Code Bench
GPT-5.4 nano26.10%
Source
Qwen3.6-27B—
Not directly comparable
LiveCodeBench (Vals)
GPT-5.4 nano84.0%
Source
Qwen3.6-27B—
Not directly comparable
SWE-bench (Vals)
GPT-5.4 nano69.8%
Source
Qwen3.6-27B—
Not directly comparable
SWE-bench Verified
GPT-5.4 nano—
Qwen3.6-27B77.2%
Source
Not directly comparable
SWE Multilingual
GPT-5.4 nano—
Qwen3.6-27B71.3%
Source
Not directly comparable
SWE-bench Pro
GPT-5.4 nano—
Qwen3.6-27B53.5%
Source
Not directly comparable
Terminal-Bench 2.0
GPT-5.4 nano—
Qwen3.6-27B59.3%
Source
Not directly comparable
LiveCodeBench
GPT-5.4 nano—
Qwen3.6-27B83.9%
Source
Not directly comparable
NL2Repo
GPT-5.4 nano—
Qwen3.6-27B36.2%
Source
Not directly comparable

Knowledge

GPQA
GPT-5.4 nano82.8%
Source
Qwen3.6-27B87.8%
Source
Qwen3.6-27B leads this result
HLE
GPT-5.4 nano37.7%
Source
Qwen3.6-27B24%
Source
GPT-5.4 nano leads this result
HLE w/o tools
GPT-5.4 nano24.3%
Source
Qwen3.6-27B—
Not directly comparable
GPQA Diamond (Vals)
GPT-5.4 nano77.5%
Source
Qwen3.6-27B—
Not directly comparable
MMLU-Pro (Vals)
GPT-5.4 nano77.2%
Source
Qwen3.6-27B—
Not directly comparable
MMLU-Pro
GPT-5.4 nano—
Qwen3.6-27B86.2%
Source
Not directly comparable
MMLU-Redux
GPT-5.4 nano—
Qwen3.6-27B93.5%
Source
Not directly comparable
SuperGPQA
GPT-5.4 nano—
Qwen3.6-27B66%
Source
Not directly comparable
C-Eval
GPT-5.4 nano—
Qwen3.6-27B91.4%
Source
Not directly comparable

Math

FrontierMath v2 (Tiers 1-3)
GPT-5.4 nano25.860%
Source
Qwen3.6-27B—
Not directly comparable
FrontierMath v2 (Tier 4)
GPT-5.4 nano6.250%
Source
Qwen3.6-27B—
Not directly comparable
HMMT Feb 2025
GPT-5.4 nano—
Qwen3.6-27B93.8%
Source
Not directly comparable
HMMT Nov 2025
GPT-5.4 nano—
Qwen3.6-27B90.7%
Source
Not directly comparable
HMMT Feb 2026
GPT-5.4 nano—
Qwen3.6-27B84.3%
Source
Not directly comparable
MMAnswerBench
GPT-5.4 nano—
Qwen3.6-27B80.8%
Source
Not directly comparable
AIME26
GPT-5.4 nano—
Qwen3.6-27B94.1%
Source
Not directly comparable

Multimodal

MMMU-Pro
GPT-5.4 nano66.1%
Source
Qwen3.6-27B75.8%
Source
Qwen3.6-27B leads this result
MMMU-Pro w/ Python
GPT-5.4 nano69.5%
Source
Qwen3.6-27B—
Not directly comparable
MMMU
GPT-5.4 nano—
Qwen3.6-27B82.9%
Source
Not directly comparable
RealWorldQA
GPT-5.4 nano—
Qwen3.6-27B84.1%
Source
Not directly comparable
DynaMath
GPT-5.4 nano—
Qwen3.6-27B85.6%
Source
Not directly comparable
MStar
GPT-5.4 nano—
Qwen3.6-27B81.4%
Source
Not directly comparable
SimpleVQA
GPT-5.4 nano—
Qwen3.6-27B56.1%
Source
Not directly comparable
CharXiv
GPT-5.4 nano—
Qwen3.6-27B78.4%
Source
Not directly comparable
CC-OCR
GPT-5.4 nano—
Qwen3.6-27B81.2%
Source
Not directly comparable
CountBench
GPT-5.4 nano—
Qwen3.6-27B97.8%
Source
Not directly comparable
RefCOCO (avg)
GPT-5.4 nano—
Qwen3.6-27B92.5%
Source
Not directly comparable
ERQA
GPT-5.4 nano—
Qwen3.6-27B62.5%
Source
Not directly comparable
Video-MME (with subtitle)
GPT-5.4 nano—
Qwen3.6-27B87.7%
Source
Not directly comparable
VideoMMMU
GPT-5.4 nano—
Qwen3.6-27B84.4%
Source
Not directly comparable
MLVU (M-Avg)
GPT-5.4 nano—
Qwen3.6-27B86.6%
Source
Not directly comparable
V*
GPT-5.4 nano—
Qwen3.6-27B94.7%
Source
Not directly comparable

Frequently asked questions

Which is better, GPT-5.4 nano or Qwen3.6-27B?

GPT-5.4 nano has the higher public score estimate, 62.19 versus 52.73, 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 nano or Qwen3.6-27B?

Qwen3.6-27B leads the public coding lane, 42.6 to 37.5, with Supported evidence for both models, although the 90% intervals overlap.

Which is better for agentic tasks, GPT-5.4 nano or Qwen3.6-27B?

GPT-5.4 nano leads the public agentic tasks lane, 37.3 to 33.9, with Supported evidence for both models, although the 90% intervals overlap.

Which costs less, GPT-5.4 nano or Qwen3.6-27B?

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 nano or Qwen3.6-27B?

GPT-5.4 nano has the larger documented context window: 400K, compared with 262K.

Related comparisons

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Last updated September 4, 2026

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GPT-5.4 nano vs Qwen3.6-27B

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

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Knowledge

Math

Multimodal

Frequently asked questions

Which is better, GPT-5.4 nano or Qwen3.6-27B?

Which is better for coding, GPT-5.4 nano or Qwen3.6-27B?

Which is better for agentic tasks, GPT-5.4 nano or Qwen3.6-27B?

Which costs less, GPT-5.4 nano or Qwen3.6-27B?

Which has the larger context window, GPT-5.4 nano or Qwen3.6-27B?

Related comparisons

Watch GPT-5.4 nano vs Qwen3.6-27B