Model comparison

GPT-5.6 Luna vs Qwen3.6-27B

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

GPT-5.6 Luna

OpenAI

66.9/100

Estimated · Public rank #23

90% interval 56.4–77.3

Qwen3.6-27B

Alibaba

52.9/100

Estimated · Public rank #101

90% interval 41.4–64.4

GPT-5.6 Luna has the higher public score estimate, 66.87 versus 52.88, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

5 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.

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-5.6 Luna
GPT-5.6 Luna 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
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are 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: 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.

17 GPT-5.6 Luna only

5 Shared

33 Qwen3.6-27B only

Shared results: 5
GPT-5.6 Luna only: 17
Qwen3.6-27B only: 33
Like-for-like categories: 0 / 8

4 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Agentic

Directional only

GPT-5.6 Luna: 84.1
Qwen3.6-27B: 59.3
Weighted basis: 2 vs 1 rows
Reading: Directional only

Coding

Directional only

GPT-5.6 Luna: 62.7
Qwen3.6-27B: 77.5
Weighted basis: 1 vs 3 rows
Reading: Directional only

Knowledge

Directional only

GPT-5.6 Luna: 92.3
Qwen3.6-27B: 53.3
Weighted basis: 1 vs 4 rows
Reading: Directional only

Multimodal

Directional only

GPT-5.6 Luna: 78.4
Qwen3.6-27B: 76.7
Weighted basis: 1 vs 2 rows
Reading: Directional only

Reasoning

Not comparable

GPT-5.6 Luna: 59.5
Qwen3.6-27B: Not measured
Weighted basis: 1 vs 0 rows
Reading: Not comparable

Math

Not comparable

GPT-5.6 Luna: 73.6
Qwen3.6-27B: 89.2
Weighted basis: 2 vs 2 rows
Reading: Not comparable

Multilingual

Not comparable

GPT-5.6 Luna: Not measured
Qwen3.6-27B: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Instruction following

Not comparable

GPT-5.6 Luna: Not measured
Qwen3.6-27B: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Category averages with the server-provided evidence basis for GPT-5.6 Luna and Qwen3.6-27B
Category	GPT-5.6 Luna	Qwen3.6-27B	Weighted basis	Reading
Agentic	84.1	59.3	Directional only2 vs 1 rows	Directional only
Coding	62.7	77.5	Directional only1 vs 3 rows	Directional only
Knowledge	92.3	53.3	Directional only1 vs 4 rows	Directional only
Multimodal	78.4	76.7	Directional only1 vs 2 rows	Directional only
Reasoning	59.5	Not measured	Not comparable1 vs 0 rows	Not comparable
Math	73.6	89.2	Not comparable2 vs 2 rows	Not comparable
Multilingual	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable
Instruction following	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable

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.

Terminal-Bench 2.0
Agentic
GPT-5.6 Luna: 84.7%Qwen3.6-27B: 59.3%Normalized gap 25.4GPT-5.6 Luna source Qwen3.6-27B source
SWE-bench Pro
Coding
GPT-5.6 Luna: 62.7%Qwen3.6-27B: 53.5%Normalized gap 9.2GPT-5.6 Luna source Qwen3.6-27B source
GPQA
Knowledge
GPT-5.6 Luna: 92.3%Qwen3.6-27B: 87.8%Normalized gap 4.5GPT-5.6 Luna source Qwen3.6-27B source
MMMU-Pro
Multimodal
GPT-5.6 Luna: 78.4%Qwen3.6-27B: 75.8%Normalized gap 2.6GPT-5.6 Luna 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-5.6 Luna: $0.0008; 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-5.6 Luna: $0.0136; 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-5.6 Luna: $0.02; 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-5.6 LunaQwen3.6-27B

Context window

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

GPT-5.6 Luna

1.05M

OpenAI model catalog

Qwen3.6-27B

262K

API model ID

GPT-5.6 Luna

gpt-5.6-luna

OpenAI model catalog

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-5.6 Luna

$0.02 per 1M cached input tokens

OpenAI pricing

Qwen3.6-27B

No comparable hosted API rate

Documented inputs

GPT-5.6 Luna

text, image

OpenAI model catalog

Qwen3.6-27B

Not sourced

Documented outputs

GPT-5.6 Luna

text

OpenAI model catalog

Qwen3.6-27B

Not sourced

Provider availability

GPT-5.6 Luna

Generally Available · OpenAI Responses API

OpenAI model catalog

Qwen3.6-27B

Not sourced

Reasoning profile

GPT-5.6 Luna

Reasoning

Qwen3.6-27B

Reasoning

Weight access

GPT-5.6 Luna

Proprietary

Qwen3.6-27B

Open Weight

License

GPT-5.6 Luna

Proprietary

Qwen3.6-27B

Open Weight

Release date

GPT-5.6 Luna

2026-07-09

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-5.6 Luna has the higher public score estimate, 66.87 versus 52.88, but the 90% score intervals overlap.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: GPT-5.6 Luna has the larger documented window (1.05M).

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-5.6 Luna

API / mo$1,050

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 evidence55 rows

Agentic

Terminal-Bench 2.0
GPT-5.6 Luna84.7%
Source
Qwen3.6-27B59.3%
Source
GPT-5.6 Luna leads this result
BrowseComp
GPT-5.6 Luna83.3%
Source
Qwen3.6-27B—
Not directly comparable
OSWorld 2.0
GPT-5.6 Luna45.6%
Source
Qwen3.6-27B—
Not directly comparable
CyberGym
GPT-5.6 Luna77.9%
Source
Qwen3.6-27B—
Not directly comparable
ExploitGym
GPT-5.6 Luna12.4%
Source
Qwen3.6-27B—
Not directly comparable
Toolathlon
GPT-5.6 Luna53.4%
Source
Qwen3.6-27B—
Not directly comparable
Claw-Eval
GPT-5.6 Luna—
Qwen3.6-27B72.4%
Source
Not directly comparable
QwenClawBench
GPT-5.6 Luna—
Qwen3.6-27B53.4%
Source
Not directly comparable
QwenWebBench
GPT-5.6 Luna—
Qwen3.6-27B1487
Source
Not directly comparable
AndroidWorld
GPT-5.6 Luna—
Qwen3.6-27B70.3%
Source
Not directly comparable
Gert Labs
GPT-5.6 Luna—
Qwen3.6-27B54.84%
Source
Not directly comparable

Coding

SWE-bench Pro
GPT-5.6 Luna62.7%
Source
Qwen3.6-27B53.5%
Source
GPT-5.6 Luna leads this result
Terminal-Bench 2.0
GPT-5.6 Luna84.7%
Source
Qwen3.6-27B59.3%
Source
GPT-5.6 Luna leads this result
deepSwe
GPT-5.6 Luna67.2%
Source
Qwen3.6-27B—
Not directly comparable
FrontierCode 1.1 Extended
GPT-5.6 Luna55.1%
Source
Qwen3.6-27B—
Not directly comparable
cursorBench32
GPT-5.6 Luna61.1%
Source
Qwen3.6-27B—
Not directly comparable
SWE-bench Verified
GPT-5.6 Luna—
Qwen3.6-27B77.2%
Source
Not directly comparable
SWE Multilingual
GPT-5.6 Luna—
Qwen3.6-27B71.3%
Source
Not directly comparable
LiveCodeBench
GPT-5.6 Luna—
Qwen3.6-27B83.9%
Source
Not directly comparable
NL2Repo
GPT-5.6 Luna—
Qwen3.6-27B36.2%
Source
Not directly comparable

Reasoning

ARC-AGI-2
GPT-5.6 Luna59.5%
Source
Qwen3.6-27B—
Not directly comparable
ARC-AGI-3
GPT-5.6 Luna0.2%
Source
Qwen3.6-27B—
Not directly comparable

Knowledge

GPQA
GPT-5.6 Luna92.3%
Source
Qwen3.6-27B87.8%
Source
GPT-5.6 Luna leads this result
GPQA-D
GPT-5.6 Luna92.3%
Source
Qwen3.6-27B—
Not directly comparable
HealthBench Professional
GPT-5.6 Luna55.7%
Source
Qwen3.6-27B—
Not directly comparable
HealthBench Hard
GPT-5.6 Luna32.0%
Source
Qwen3.6-27B—
Not directly comparable
MMLU-Pro
GPT-5.6 Luna—
Qwen3.6-27B86.2%
Source
Not directly comparable
MMLU-Redux
GPT-5.6 Luna—
Qwen3.6-27B93.5%
Source
Not directly comparable
SuperGPQA
GPT-5.6 Luna—
Qwen3.6-27B66%
Source
Not directly comparable
C-Eval
GPT-5.6 Luna—
Qwen3.6-27B91.4%
Source
Not directly comparable
HLE
GPT-5.6 Luna—
Qwen3.6-27B24%
Source
Not directly comparable

Math

FrontierMath (legacy)
GPT-5.6 Luna78.6%
Source
Qwen3.6-27B—
Not directly comparable
FrontierMath v2 (Tiers 1-3)
GPT-5.6 Luna78.600%
Source
Qwen3.6-27B—
Not directly comparable
FrontierMath v2 (Tier 4)
GPT-5.6 Luna58.500%
Source
Qwen3.6-27B—
Not directly comparable
HMMT Feb 2025
GPT-5.6 Luna—
Qwen3.6-27B93.8%
Source
Not directly comparable
HMMT Nov 2025
GPT-5.6 Luna—
Qwen3.6-27B90.7%
Source
Not directly comparable
HMMT Feb 2026
GPT-5.6 Luna—
Qwen3.6-27B84.3%
Source
Not directly comparable
MMAnswerBench
GPT-5.6 Luna—
Qwen3.6-27B80.8%
Source
Not directly comparable
AIME26
GPT-5.6 Luna—
Qwen3.6-27B94.1%
Source
Not directly comparable

Multimodal

MMMU-Pro
GPT-5.6 Luna78.4%
Source
Qwen3.6-27B75.8%
Source
GPT-5.6 Luna leads this result
MMMU-Pro w/ Python
GPT-5.6 Luna79.5%
Source
Qwen3.6-27B—
Not directly comparable
MMMU
GPT-5.6 Luna—
Qwen3.6-27B82.9%
Source
Not directly comparable
RealWorldQA
GPT-5.6 Luna—
Qwen3.6-27B84.1%
Source
Not directly comparable
DynaMath
GPT-5.6 Luna—
Qwen3.6-27B85.6%
Source
Not directly comparable
MStar
GPT-5.6 Luna—
Qwen3.6-27B81.4%
Source
Not directly comparable
SimpleVQA
GPT-5.6 Luna—
Qwen3.6-27B56.1%
Source
Not directly comparable
CharXiv
GPT-5.6 Luna—
Qwen3.6-27B78.4%
Source
Not directly comparable
CC-OCR
GPT-5.6 Luna—
Qwen3.6-27B81.2%
Source
Not directly comparable
CountBench
GPT-5.6 Luna—
Qwen3.6-27B97.8%
Source
Not directly comparable
RefCOCO (avg)
GPT-5.6 Luna—
Qwen3.6-27B92.5%
Source
Not directly comparable
ERQA
GPT-5.6 Luna—
Qwen3.6-27B62.5%
Source
Not directly comparable
Video-MME (with subtitle)
GPT-5.6 Luna—
Qwen3.6-27B87.7%
Source
Not directly comparable
VideoMMMU
GPT-5.6 Luna—
Qwen3.6-27B84.4%
Source
Not directly comparable
MLVU (M-Avg)
GPT-5.6 Luna—
Qwen3.6-27B86.6%
Source
Not directly comparable
V*
GPT-5.6 Luna—
Qwen3.6-27B94.7%
Source
Not directly comparable

Frequently asked questions

Which is better, GPT-5.6 Luna or Qwen3.6-27B?

GPT-5.6 Luna has the higher public score estimate, 66.87 versus 52.88, 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-5.6 Luna or Qwen3.6-27B?

The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, GPT-5.6 Luna or Qwen3.6-27B?

The current agentic tasks averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.

Which costs less, GPT-5.6 Luna 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-5.6 Luna or Qwen3.6-27B?

GPT-5.6 Luna has the larger documented context window: 1.05M, compared with 262K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 10, 2026

Watch GPT-5.6 Luna vs Qwen3.6-27B

One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.

Read a sample issue

Join 2,000+ readers.

GPT-5.6 Luna 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

Multimodal

Reasoning

Math

Multilingual

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

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Reasoning

Knowledge

Math

Multimodal

Frequently asked questions

Which is better, GPT-5.6 Luna or Qwen3.6-27B?

Which is better for coding, GPT-5.6 Luna or Qwen3.6-27B?

Which is better for agentic tasks, GPT-5.6 Luna or Qwen3.6-27B?

Which costs less, GPT-5.6 Luna or Qwen3.6-27B?

Which has the larger context window, GPT-5.6 Luna or Qwen3.6-27B?

Related comparisons

Watch GPT-5.6 Luna vs Qwen3.6-27B