Qwen3.6-27B vs QwQ-32B

Updated October 14, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

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

Model A

Qwen3.6-27B

Alibaba

48.43/100

Estimated · Public rank #99

Conditional range 34.1–62.8

Model B

QwQ-32B

Alibaba

—

Evidence status unavailable

90% interval unavailable

Shared results: 3
Qwen3.6-27B only: 37
QwQ-32B only: 8
Like-for-like categories: 0 / 8

Estimated: Qwen3.6-27B. Conditional ranges do not establish rank confidence.How the comparison works

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
Qwen3.6-27B
Qwen3.6-27B 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
QwQ-32B 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
QwQ-32B is not ranked on the public lane for agentic, so no winner is named for agentic.
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
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. QwQ-32B does not fit this workload in one request. Qwen3.6-27B has no comparable published API token rate. QwQ-32B has no comparable published API token rate.
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

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

Pick the job you would use them for

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

35.4Qwen3.6-27B—QwQ-32B

Not comparable · BenchAlign v5.8

The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboard Shortlist models for this job

Same basis rules as the category table below

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.

GPQAKnowledge
Normalized gap 22.2
Qwen3.6-27B:87.8%
QwQ-32B:65.6%
Qwen3.6-27B source QwQ-32B source

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.8 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

Not comparable

Qwen3.6-27B: 28.2; Supported · #91/124
QwQ-32B: Not ranked
Basis: BenchAlign v5.8 lane · 6 vs 1 public rows
Reading: Not comparable

Coding

Not comparable

Qwen3.6-27B: 35.4; Supported · #76/149
QwQ-32B: Not ranked
Basis: BenchAlign v5.8 lane · 7 vs 1 public rows
Reading: Not comparable

Reasoning

Not comparable

Qwen3.6-27B: 79.0; Unranked · 2 rankable rows
QwQ-32B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Qwen3.6-27B: 67.3; #38/54
QwQ-32B: Not ranked
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Knowledge

Not comparable

Qwen3.6-27B: 46.9; Estimated · #88/181
QwQ-32B: Not ranked
Basis: BenchAlign v5.8 lane · 6 vs 4 public rows
Reading: Not comparable

Multilingual

Not comparable

Qwen3.6-27B: Not ranked
QwQ-32B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Qwen3.6-27B: 82.4; #52/128
QwQ-32B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Qwen3.6-27B: 65.9; #8/12
QwQ-32B: Not ranked
Basis: Provisional lane · 3 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Qwen3.6-27B and QwQ-32B
Category	Qwen3.6-27B	QwQ-32B	Basis	Reading
Agentic	28.2Supported · #91/124	Not ranked	Not comparableBenchAlign v5.8 lane · 6 vs 1 public rows	Not comparable
Coding	35.4Supported · #76/149	Not ranked	Not comparableBenchAlign v5.8 lane · 7 vs 1 public rows	Not comparable
Reasoning	79.0Unranked · 2 rankable rows	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multimodal	67.3#38/54	Not ranked	Not comparableProvisional lane · 2 vs 0 weighted rows	Not comparable
Knowledge	46.9Estimated · #88/181	Not ranked	Not comparableBenchAlign v5.8 lane · 6 vs 4 public rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	82.4#52/128	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	65.9#8/12	Not ranked	Not comparableProvisional lane · 3 vs 0 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

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

Qwen3.6-27B: Self-hosted; infrastructure cost varies; Fits in one request
QwQ-32B: API rate not published; Fits in one request

Qwen3.6-27B has no comparable published API token rate. QwQ-32B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Qwen3.6-27B: Self-hosted; infrastructure cost varies; Fits in one request
QwQ-32B: API rate not published; Fits in one request

Qwen3.6-27B has no comparable published API token rate. QwQ-32B has no comparable published API token rate.

Cache-heavy agent loop

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

Qwen3.6-27B: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable
QwQ-32B: API rate not published; Does not fit in one request; Cached-input rate unavailable

QwQ-32B does not fit this workload in one request. Qwen3.6-27B has no comparable published API token rate. QwQ-32B has no comparable published API token rate.

Cached input falls back to the list input rate only where a cached rate is unpublished

Specification differences

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

SpecificationQwen3.6-27BQwQ-32B

Context window

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

Qwen3.6-27B

262K

QwQ-32B

131K

API model ID

Qwen3.6-27B

Not sourced

QwQ-32B

Not sourced

Cached-input rate

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

Qwen3.6-27B

No comparable hosted API rate

QwQ-32B

No comparable hosted API rate

Documented inputs

Qwen3.6-27B

Not sourced

QwQ-32B

Not sourced

Documented outputs

Qwen3.6-27B

Not sourced

QwQ-32B

Not sourced

Provider availability

Qwen3.6-27B

Not sourced

QwQ-32B

Not sourced

Reasoning profile

Qwen3.6-27B

Reasoning

QwQ-32B

Reasoning

Weight access

Qwen3.6-27B

Open Weight

QwQ-32B

Open Weight

License

Qwen3.6-27B

Open Weight

QwQ-32B

Open Weight

Release date

Qwen3.6-27B

2026-04-21

QwQ-32B

2025-03-06

If you already use one of these models

Deployment change: Both entries list Alibaba as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
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: Qwen3.6-27B has the larger documented window (262K).

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

Questions

Which is better, Qwen3.6-27B or QwQ-32B?

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, Qwen3.6-27B or QwQ-32B?

QwQ-32B is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Qwen3.6-27B or QwQ-32B?

QwQ-32B is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Qwen3.6-27B or QwQ-32B?

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, Qwen3.6-27B or QwQ-32B?

Qwen3.6-27B has the larger documented context window: 262K, compared with 131K.

Self-host vs API cost

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

Qwen3.6-27B

API / mo$0

Self-host / mo$429

Break-even—

QwQ-32B

API / mo$0

Self-host / moNot listed

Break-even—

Proprietary model — self-hosting not applicable.

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 evidence48 rows

Agentic

Terminal-Bench 2.0
Qwen3.6-27B59.3%
Source
QwQ-32B—
Not directly comparable
Claw-Eval
Qwen3.6-27B72.4%
Source
QwQ-32B—
Not directly comparable
QwenClawBench
Qwen3.6-27B53.4%
Source
QwQ-32B—
Not directly comparable
QwenWebBench
Qwen3.6-27B1487
Source
QwQ-32B—
Not directly comparable
AndroidWorld
Qwen3.6-27B70.3%
Source
QwQ-32B—
Not directly comparable
Gert Labs
Qwen3.6-27B54.84%
Source
QwQ-32B—
Not directly comparable
BFCL v3
Qwen3.6-27B—
QwQ-32B66.4%
Source
Not directly comparable

Coding

SWE-bench Verified
Qwen3.6-27B77.2%
Source
QwQ-32B—
Not directly comparable
SWE Multilingual
Qwen3.6-27B71.3%
Source
QwQ-32B—
Not directly comparable
SWE-bench Pro
Qwen3.6-27B53.5%
Source
QwQ-32B—
Not directly comparable
Terminal-Bench 2.0
Qwen3.6-27B59.3%
Source
QwQ-32B—
Not directly comparable
LiveCodeBench v6
Qwen3.6-27B83.9%
Source
QwQ-32B—
Not directly comparable
NL2Repo
Qwen3.6-27B36.2%
Source
QwQ-32B—
Not directly comparable
SWE-bench (Vals)
Qwen3.6-27B70.0%
Source
QwQ-32B—
Not directly comparable
LiveCodeBench v5
Qwen3.6-27B—
QwQ-32B62.7%
Source
Not directly comparable

Multimodal

MMMU
Qwen3.6-27B82.9%
Source
QwQ-32B—
Not directly comparable
MMMU-Pro
Qwen3.6-27B75.8%
Source
QwQ-32B—
Not directly comparable
RealWorldQA
Qwen3.6-27B84.1%
Source
QwQ-32B—
Not directly comparable
DynaMath
Qwen3.6-27B85.6%
Source
QwQ-32B—
Not directly comparable
MStar
Qwen3.6-27B81.4%
Source
QwQ-32B—
Not directly comparable
SimpleVQA
Qwen3.6-27B56.1%
Source
QwQ-32B—
Not directly comparable
CharXiv
Qwen3.6-27B78.4%
Source
QwQ-32B—
Not directly comparable
CC-OCR
Qwen3.6-27B81.2%
Source
QwQ-32B—
Not directly comparable
CountBench
Qwen3.6-27B97.8%
Source
QwQ-32B—
Not directly comparable
RefCOCO (avg)
Qwen3.6-27B92.5%
Source
QwQ-32B—
Not directly comparable
ERQA
Qwen3.6-27B62.5%
Source
QwQ-32B—
Not directly comparable
Video-MME (with subtitle)
Qwen3.6-27B87.7%
Source
QwQ-32B—
Not directly comparable
VideoMMMU
Qwen3.6-27B84.4%
Source
QwQ-32B—
Not directly comparable
MLVU (M-Avg)
Qwen3.6-27B86.6%
Source
QwQ-32B—
Not directly comparable
V*
Qwen3.6-27B94.7%
Source
QwQ-32B—
Not directly comparable

Knowledge

MMLU-Pro
Qwen3.6-27B86.2%
Source
QwQ-32B—
Not directly comparable
MMLU-Redux
Qwen3.6-27B93.5%
Source
QwQ-32B90%
Source
Qwen3.6-27B leads this result
SuperGPQA
Qwen3.6-27B66%
Source
QwQ-32B—
Not directly comparable
C-Eval
Qwen3.6-27B91.4%
Source
QwQ-32B88.4%
Source
Qwen3.6-27B leads this result
GPQA
Qwen3.6-27B87.8%
Source
QwQ-32B65.6%
Source
Qwen3.6-27B leads this result
HLE
Qwen3.6-27B24%
Source
QwQ-32B—
Not directly comparable
MMMLU
Qwen3.6-27B—
QwQ-32B80.9%
Source
Not directly comparable

Multilingual

INCLUDE
Qwen3.6-27B—
QwQ-32B69.7%
Source
Not directly comparable

Instruction following

IFEval
Qwen3.6-27B—
QwQ-32B83.9%
Source
Not directly comparable

Math

HMMT Feb 2025
Qwen3.6-27B93.8%
Source
QwQ-32B—
Not directly comparable
HMMT Nov 2025
Qwen3.6-27B90.7%
Source
QwQ-32B—
Not directly comparable
HMMT Feb 2026
Qwen3.6-27B84.3%
Source
QwQ-32B—
Not directly comparable
MMAnswerBench
Qwen3.6-27B80.8%
Source
QwQ-32B—
Not directly comparable
AIME26
Qwen3.6-27B94.1%
Source
QwQ-32B—
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Qwen3.6-27B35.088%
Source
QwQ-32B—
Not directly comparable
MATH-500
Qwen3.6-27B—
QwQ-32B98%
Source
Not directly comparable
AIME 2024
Qwen3.6-27B—
QwQ-32B79.5%
Source
Not directly comparable
AIME 2025
Qwen3.6-27B—
QwQ-32B69.5%
Source
Not directly comparable

48 public results · 3 shared

Related comparisons

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Last updated October 14, 2026

Qwen3.6-27B vs QwQ-32B

Decision reading

Which one for your work

Long documents

Coding work

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Agentic

Coding

Reasoning

Multimodal

Knowledge

Multilingual

Instruction following

Math

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

If you already use one of these models

Questions

Which is better, Qwen3.6-27B or QwQ-32B?

Which is better for coding, Qwen3.6-27B or QwQ-32B?

Which is better for agentic tasks, Qwen3.6-27B or QwQ-32B?

Which costs less, Qwen3.6-27B or QwQ-32B?

Which has the larger context window, Qwen3.6-27B or QwQ-32B?

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Multimodal

Knowledge

Multilingual

Instruction following

Math

Related comparisons

Watch Qwen3.6-27B vs QwQ-32B