Model A

OpenAI

42.95/100

Supported · Public rank #178

90% interval 31.5–54.4

GPT-OSS 20B 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

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.

0 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.

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
GPT-OSS 20B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-OSS 20B 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
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. GPT-OSS 20B does not fit this workload in one request. GPT-OSS 20B has no comparable published API token rate. Qwen3.6-27B 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

What is actually comparable

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

1 GPT-OSS 20B only

38 Qwen3.6-27B only

Shared results: 0
GPT-OSS 20B only: 1
Qwen3.6-27B only: 38
Like-for-like categories: 0 / 8

4 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-OSS 20B: 38.4; Estimated · #125/151
Qwen3.6-27B: 33.9; Supported · #133/151
Basis: BenchAlign lane · 0 vs 6 public rows
Reading: Directional only

Coding

Directional only

GPT-OSS 20B: 40.1; Estimated · #141/183
Qwen3.6-27B: 42.6; Supported · #128/183
Basis: BenchAlign lane · 1 vs 6 public rows
Reading: Directional only

Knowledge

Directional only

GPT-OSS 20B: 40.4; Estimated · #139/181
Qwen3.6-27B: 49.0; Estimated · #95/181
Basis: BenchAlign lane · 0 vs 6 public rows
Reading: Directional only

Instruction following

Directional only

GPT-OSS 20B: 78.9; #53/120
Qwen3.6-27B: 82.2; #50/120
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Directional only

Reasoning

Not comparable

GPT-OSS 20B: 45.0; 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-OSS 20B: Not ranked
Qwen3.6-27B: 72.8; Unranked · 5 rankable rows
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GPT-OSS 20B: Not ranked
Qwen3.6-27B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

GPT-OSS 20B: Not ranked
Qwen3.6-27B: 51.5; #35/48
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GPT-OSS 20B and Qwen3.6-27B
Category	GPT-OSS 20B	Qwen3.6-27B	Basis	Reading
Agentic	38.4Estimated · #125/151	33.9Supported · #133/151	Directional onlyBenchAlign lane · 0 vs 6 public rows	Directional only
Coding	40.1Estimated · #141/183	42.6Supported · #128/183	Directional onlyBenchAlign lane · 1 vs 6 public rows	Directional only
Knowledge	40.4Estimated · #139/181	49.0Estimated · #95/181	Directional onlyBenchAlign lane · 0 vs 6 public rows	Directional only
Instruction following	78.9#53/120	82.2#50/120	Directional onlyProvisional lane · 0 vs 0 weighted rows	Directional only
Reasoning	45.0Unranked · 2 rankable rows	73.7Unranked · 2 rankable rows	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	Not ranked	72.8Unranked · 5 rankable rows	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multimodal	Not ranked	51.5#35/48	Not comparableProvisional lane · 0 vs 2 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.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

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-OSS 20B: Self-hosted; infrastructure cost varies; Fits in one request
Qwen3.6-27B: Self-hosted; infrastructure cost varies; Fits in one request

GPT-OSS 20B has no comparable published API token rate. Qwen3.6-27B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GPT-OSS 20B: Self-hosted; infrastructure cost varies; Fits in one request
Qwen3.6-27B: Self-hosted; infrastructure cost varies; Fits in one request

GPT-OSS 20B has no comparable published API token rate. Qwen3.6-27B has no comparable published API token rate.

Cache-heavy agent loop

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

GPT-OSS 20B: Self-hosted; infrastructure cost varies; Does not fit in one request; Cached-input rate unavailable
Qwen3.6-27B: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable

GPT-OSS 20B does not fit this workload in one request. GPT-OSS 20B has no comparable published API token rate. 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-OSS 20BQwen3.6-27B

Context window

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

GPT-OSS 20B

128K

Qwen3.6-27B

262K

API model ID

GPT-OSS 20B

Not sourced

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-OSS 20B

No comparable hosted API rate

Qwen3.6-27B

No comparable hosted API rate

Documented inputs

GPT-OSS 20B

Not sourced

Qwen3.6-27B

Not sourced

Documented outputs

GPT-OSS 20B

Not sourced

Qwen3.6-27B

Not sourced

Provider availability

GPT-OSS 20B

Not sourced

Qwen3.6-27B

Not sourced

Reasoning profile

GPT-OSS 20B

Non-Reasoning

Qwen3.6-27B

Reasoning

Weight access

GPT-OSS 20B

Open Weight

Qwen3.6-27B

Open Weight

License

GPT-OSS 20B

Open Weight

Qwen3.6-27B

Open Weight

Release date

GPT-OSS 20B

2025-08-05

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: The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
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.

Self-host vs API cost

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

GPT-OSS 20B

API / mo$0

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 evidence39 rows

Agentic

Terminal-Bench 2.0
GPT-OSS 20B—
Qwen3.6-27B59.3%
Source
Not directly comparable
Claw-Eval
GPT-OSS 20B—
Qwen3.6-27B72.4%
Source
Not directly comparable
QwenClawBench
GPT-OSS 20B—
Qwen3.6-27B53.4%
Source
Not directly comparable
QwenWebBench
GPT-OSS 20B—
Qwen3.6-27B1487
Source
Not directly comparable
AndroidWorld
GPT-OSS 20B—
Qwen3.6-27B70.3%
Source
Not directly comparable
Gert Labs
GPT-OSS 20B—
Qwen3.6-27B54.84%
Source
Not directly comparable

Coding

React Native Evals
GPT-OSS 20B71%
Source
Qwen3.6-27B—
Not directly comparable
SWE-bench Verified
GPT-OSS 20B—
Qwen3.6-27B77.2%
Source
Not directly comparable
SWE Multilingual
GPT-OSS 20B—
Qwen3.6-27B71.3%
Source
Not directly comparable
SWE-bench Pro
GPT-OSS 20B—
Qwen3.6-27B53.5%
Source
Not directly comparable
Terminal-Bench 2.0
GPT-OSS 20B—
Qwen3.6-27B59.3%
Source
Not directly comparable
LiveCodeBench
GPT-OSS 20B—
Qwen3.6-27B83.9%
Source
Not directly comparable
NL2Repo
GPT-OSS 20B—
Qwen3.6-27B36.2%
Source
Not directly comparable

Knowledge

MMLU-Pro
GPT-OSS 20B—
Qwen3.6-27B86.2%
Source
Not directly comparable
MMLU-Redux
GPT-OSS 20B—
Qwen3.6-27B93.5%
Source
Not directly comparable
SuperGPQA
GPT-OSS 20B—
Qwen3.6-27B66%
Source
Not directly comparable
C-Eval
GPT-OSS 20B—
Qwen3.6-27B91.4%
Source
Not directly comparable
GPQA
GPT-OSS 20B—
Qwen3.6-27B87.8%
Source
Not directly comparable
HLE
GPT-OSS 20B—
Qwen3.6-27B24%
Source
Not directly comparable

Math

HMMT Feb 2025
GPT-OSS 20B—
Qwen3.6-27B93.8%
Source
Not directly comparable
HMMT Nov 2025
GPT-OSS 20B—
Qwen3.6-27B90.7%
Source
Not directly comparable
HMMT Feb 2026
GPT-OSS 20B—
Qwen3.6-27B84.3%
Source
Not directly comparable
MMAnswerBench
GPT-OSS 20B—
Qwen3.6-27B80.8%
Source
Not directly comparable
AIME26
GPT-OSS 20B—
Qwen3.6-27B94.1%
Source
Not directly comparable

Multimodal

MMMU
GPT-OSS 20B—
Qwen3.6-27B82.9%
Source
Not directly comparable
MMMU-Pro
GPT-OSS 20B—
Qwen3.6-27B75.8%
Source
Not directly comparable
RealWorldQA
GPT-OSS 20B—
Qwen3.6-27B84.1%
Source
Not directly comparable
DynaMath
GPT-OSS 20B—
Qwen3.6-27B85.6%
Source
Not directly comparable
MStar
GPT-OSS 20B—
Qwen3.6-27B81.4%
Source
Not directly comparable
SimpleVQA
GPT-OSS 20B—
Qwen3.6-27B56.1%
Source
Not directly comparable
CharXiv
GPT-OSS 20B—
Qwen3.6-27B78.4%
Source
Not directly comparable
CC-OCR
GPT-OSS 20B—
Qwen3.6-27B81.2%
Source
Not directly comparable
CountBench
GPT-OSS 20B—
Qwen3.6-27B97.8%
Source
Not directly comparable
RefCOCO (avg)
GPT-OSS 20B—
Qwen3.6-27B92.5%
Source
Not directly comparable
ERQA
GPT-OSS 20B—
Qwen3.6-27B62.5%
Source
Not directly comparable
Video-MME (with subtitle)
GPT-OSS 20B—
Qwen3.6-27B87.7%
Source
Not directly comparable
VideoMMMU
GPT-OSS 20B—
Qwen3.6-27B84.4%
Source
Not directly comparable
MLVU (M-Avg)
GPT-OSS 20B—
Qwen3.6-27B86.6%
Source
Not directly comparable
V*
GPT-OSS 20B—
Qwen3.6-27B94.7%
Source
Not directly comparable

Frequently asked questions

Which is better, GPT-OSS 20B or Qwen3.6-27B?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, GPT-OSS 20B or Qwen3.6-27B?

Qwen3.6-27B scores higher for coding on the public lane, 42.6 to 40.1. GPT-OSS 20B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, GPT-OSS 20B or Qwen3.6-27B?

GPT-OSS 20B scores higher for agentic tasks on the public lane, 38.4 to 33.9. GPT-OSS 20B 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-OSS 20B 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-OSS 20B or Qwen3.6-27B?

Qwen3.6-27B has the larger documented context window: 262K, compared with 128K.

Related comparisons

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Last updated September 4, 2026

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GPT-OSS 20B vs Qwen3.6-27B

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

Coding

Knowledge

Instruction following

Reasoning

Math

Multilingual

Multimodal

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-OSS 20B or Qwen3.6-27B?

Which is better for coding, GPT-OSS 20B or Qwen3.6-27B?

Which is better for agentic tasks, GPT-OSS 20B or Qwen3.6-27B?

Which costs less, GPT-OSS 20B or Qwen3.6-27B?

Which has the larger context window, GPT-OSS 20B or Qwen3.6-27B?

Related comparisons

Watch GPT-OSS 20B vs Qwen3.6-27B