GPT-5.6 Luna vs Qwen3.5-35B-A3B

Updated September 24, 2026. Rank says GPT-5.6 Luna is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

GPT-5.6 Luna has the higher public score, 65.6 versus 47.05, and the 90% score intervals do not overlap. 2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

GPT-5.6 Luna

OpenAI

65.6/100

Supported · Public rank #25

90% interval 60.5–70.7

Model B

Qwen3.5-35B-A3B

Alibaba

47.05/100

Supported · Public rank #87

90% interval 36.7–57.4

Shared results: 2
GPT-5.6 Luna only: 27
Qwen3.5-35B-A3B only: 14
Like-for-like categories: 1 / 8

Supported: GPT-5.6 Luna and Qwen3.5-35B-A3BHow 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
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
Qwen3.5-35B-A3B 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
Qwen3.5-35B-A3B 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: 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.

64.5GPT-5.6 Luna26.8Qwen3.5-35B-A3B

Directional only · BenchAlign v5.7

GPT-5.6 Luna scores higher, but at least one score rests on Estimated evidence or a different benchmark set. Directional only, no winner.

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.

BrowseCompAgentic
Normalized gap 22.3
GPT-5.6 Luna:83.3%
Qwen3.5-35B-A3B:61%
GPT-5.6 Luna source Qwen3.5-35B-A3B source
GPQAKnowledge
Normalized gap 8.1
GPT-5.6 Luna:92.3%
Qwen3.5-35B-A3B:84.2%
GPT-5.6 Luna source Qwen3.5-35B-A3B 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 v5.7 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.

Knowledge

Like-for-like

GPT-5.6 Luna: 64.6; Supported · #22/158
Qwen3.5-35B-A3B: 39.5; Supported · #93/158
Basis: BenchAlign v5.7 lane · 6 vs 3 public rows
Reading: GPT-5.6 Luna leads

Agentic

Directional only

GPT-5.6 Luna: 55.3; Supported · #28/105
Qwen3.5-35B-A3B: 28.9; Estimated · #69/105
Basis: BenchAlign v5.7 lane · 9 vs 4 public rows
Reading: Directional only

Coding

Directional only

GPT-5.6 Luna: 64.5; Supported · #9/135
Qwen3.5-35B-A3B: 26.8; Estimated · #95/135
Basis: BenchAlign v5.7 lane · 7 vs 2 public rows
Reading: Directional only

Reasoning

Not comparable

GPT-5.6 Luna: 54.7; #18/19
Qwen3.5-35B-A3B: 41.3; Unranked · 3 rankable rows
Basis: Provisional lane · 2 vs 1 weighted rows
Reading: Not comparable

Multimodal

Not comparable

GPT-5.6 Luna: 67.1; #22/50
Qwen3.5-35B-A3B: 67.2; Unranked · 3 rankable rows
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GPT-5.6 Luna: Not ranked
Qwen3.5-35B-A3B: 21.1; #11/12
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Instruction following

Not comparable

GPT-5.6 Luna: Not ranked
Qwen3.5-35B-A3B: 87.4; #30/124
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

GPT-5.6 Luna: 94.2; Unranked · 3 rankable rows
Qwen3.5-35B-A3B: Not ranked
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GPT-5.6 Luna and Qwen3.5-35B-A3B
Category	GPT-5.6 Luna	Qwen3.5-35B-A3B	Basis	Reading
Knowledge	64.6Supported · #22/158	39.5Supported · #93/158	Like-for-likeBenchAlign v5.7 lane · 6 vs 3 public rows	GPT-5.6 Luna leads
Agentic	55.3Supported · #28/105	28.9Estimated · #69/105	Directional onlyBenchAlign v5.7 lane · 9 vs 4 public rows	Directional only
Coding	64.5Supported · #9/135	26.8Estimated · #95/135	Directional onlyBenchAlign v5.7 lane · 7 vs 2 public rows	Directional only
Reasoning	54.7#18/19	41.3Unranked · 3 rankable rows	Not comparableProvisional lane · 2 vs 1 weighted rows	Not comparable
Multimodal	67.1#22/50	67.2Unranked · 3 rankable rows	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable
Multilingual	Not ranked	21.1#11/12	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Instruction following	Not ranked	87.4#30/124	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	94.2Unranked · 3 rankable rows	Not ranked	Not comparableProvisional lane · 2 vs 0 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) 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

GPT-5.6 Luna: $0.0008; Fits in one request
Qwen3.5-35B-A3B: Self-hosted; infrastructure cost varies; Fits in one request

Qwen3.5-35B-A3B 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
Qwen3.5-35B-A3B: Self-hosted; infrastructure cost varies; Fits in one request

Qwen3.5-35B-A3B 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
Qwen3.5-35B-A3B: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable

Qwen3.5-35B-A3B 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.

SpecificationGPT-5.6 LunaQwen3.5-35B-A3B

Context window

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

GPT-5.6 Luna

1.05M

OpenAI model catalog

Qwen3.5-35B-A3B

262K

API model ID

GPT-5.6 Luna

gpt-5.6-luna

OpenAI model catalog

Qwen3.5-35B-A3B

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

Qwen3.5-35B-A3B

No comparable hosted API rate

Documented inputs

GPT-5.6 Luna

text, image

OpenAI model catalog

Qwen3.5-35B-A3B

Not sourced

Documented outputs

GPT-5.6 Luna

text

OpenAI model catalog

Qwen3.5-35B-A3B

Not sourced

Provider availability

GPT-5.6 Luna

Generally Available · OpenAI Responses API

OpenAI model catalog

Qwen3.5-35B-A3B

Not sourced

Reasoning profile

GPT-5.6 Luna

Reasoning

Qwen3.5-35B-A3B

Reasoning

Weight access

GPT-5.6 Luna

Proprietary

Qwen3.5-35B-A3B

Open Weight

License

GPT-5.6 Luna

Proprietary

Qwen3.5-35B-A3B

Open Weight

Release date

GPT-5.6 Luna

2026-07-09

Qwen3.5-35B-A3B

2026-03-04

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, 65.6 versus 47.05, and the 90% score intervals do not 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.

Questions

Which is better, GPT-5.6 Luna or Qwen3.5-35B-A3B?

GPT-5.6 Luna has the higher public score, 65.6 versus 47.05, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.

Which is better for coding, GPT-5.6 Luna or Qwen3.5-35B-A3B?

GPT-5.6 Luna scores higher for coding on the public lane, 64.5 to 26.8. Qwen3.5-35B-A3B 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-5.6 Luna or Qwen3.5-35B-A3B?

GPT-5.6 Luna scores higher for agentic tasks on the public lane, 55.3 to 28.9. Qwen3.5-35B-A3B 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-5.6 Luna or Qwen3.5-35B-A3B?

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 Qwen3.5-35B-A3B?

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

Benchmark evidence

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

Browse raw public benchmark evidence43 rows

Agentic

Terminal-Bench 3.0
GPT-5.6 Luna14.3%
Source
Qwen3.5-35B-A3B—
Not directly comparable
Terminal-Bench 2.1
GPT-5.6 Luna84.7%
Source
Qwen3.5-35B-A3B—
Not directly comparable
BrowseComp
GPT-5.6 Luna83.3%
Source
Qwen3.5-35B-A3B61%
Source
GPT-5.6 Luna leads this result
OSWorld 2.0
GPT-5.6 Luna45.6%
Source
Qwen3.5-35B-A3B—
Not directly comparable
CyberGym
GPT-5.6 Luna77.9%
Source
Qwen3.5-35B-A3B—
Not directly comparable
ExploitGym
GPT-5.6 Luna12.4%
Source
Qwen3.5-35B-A3B—
Not directly comparable
Toolathlon
GPT-5.6 Luna53.4%
Source
Qwen3.5-35B-A3B—
Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.6 Luna79.0%
Source
Qwen3.5-35B-A3B—
Not directly comparable
ApprenticeBench
GPT-5.6 Luna7%
Source
Qwen3.5-35B-A3B—
Not directly comparable
Terminal-Bench 2.0
GPT-5.6 Luna—
Qwen3.5-35B-A3B40.5%
Source
Not directly comparable
OSWorld-Verified
GPT-5.6 Luna—
Qwen3.5-35B-A3B54.5%
Source
Not directly comparable
Gert Labs
GPT-5.6 Luna—
Qwen3.5-35B-A3B28.96%
Source
Not directly comparable

Coding

SWE-bench Pro
GPT-5.6 Luna62.7%
Source
Qwen3.5-35B-A3B—
Not directly comparable
Terminal-Bench 2.1
GPT-5.6 Luna84.7%
Source
Qwen3.5-35B-A3B—
Not directly comparable
DeepSWE
GPT-5.6 Luna67.2%
Source
Qwen3.5-35B-A3B—
Not directly comparable
FrontierCode 1.1 Extended
GPT-5.6 Luna55.1%
Source
Qwen3.5-35B-A3B—
Not directly comparable
cursorBench32
GPT-5.6 Luna61.1%
Source
Qwen3.5-35B-A3B—
Not directly comparable
VulcanBench v3
GPT-5.6 Luna85.5%
Source
Qwen3.5-35B-A3B—
Not directly comparable
SWE-bench (Vals)
GPT-5.6 Luna93.0%
Source
Qwen3.5-35B-A3B—
Not directly comparable
SWE-bench Verified
GPT-5.6 Luna—
Qwen3.5-35B-A3B69.2%
Source
Not directly comparable
SWE-Rebench
GPT-5.6 Luna—
Qwen3.5-35B-A3B53.7%
Source
Not directly comparable

Reasoning

ARC-AGI-2
GPT-5.6 Luna59.5%
Source
Qwen3.5-35B-A3B—
Not directly comparable
ARC-AGI-3
GPT-5.6 Luna0.2%
Source
Qwen3.5-35B-A3B—
Not directly comparable
LongBench v2
GPT-5.6 Luna—
Qwen3.5-35B-A3B59%
Source
Not directly comparable

Multimodal

MMMU-Pro
GPT-5.6 Luna78.4%
Source
Qwen3.5-35B-A3B—
Not directly comparable
MMMU-Pro w/ Python
GPT-5.6 Luna79.5%
Source
Qwen3.5-35B-A3B—
Not directly comparable
MMMU
GPT-5.6 Luna—
Qwen3.5-35B-A3B81.4%
Source
Not directly comparable
MMVU
GPT-5.6 Luna—
Qwen3.5-35B-A3B72.3%
Source
Not directly comparable
MathVision
GPT-5.6 Luna—
Qwen3.5-35B-A3B83.9%
Source
Not directly comparable
V*
GPT-5.6 Luna—
Qwen3.5-35B-A3B92.7%
Source
Not directly comparable

Knowledge

GPQA
GPT-5.6 Luna92.3%
Source
Qwen3.5-35B-A3B84.2%
Source
GPT-5.6 Luna leads this result
GPQA-D
GPT-5.6 Luna92.3%
Source
Qwen3.5-35B-A3B—
Not directly comparable
HealthBench Professional
GPT-5.6 Luna55.7%
Source
Qwen3.5-35B-A3B—
Not directly comparable
HealthBench Hard
GPT-5.6 Luna32.0%
Source
Qwen3.5-35B-A3B—
Not directly comparable
GPQA Diamond (Vals)
GPT-5.6 Luna91.7%
Source
Qwen3.5-35B-A3B—
Not directly comparable
MMLU-Pro (Vals)
GPT-5.6 Luna86.0%
Source
Qwen3.5-35B-A3B—
Not directly comparable
MMLU-Pro
GPT-5.6 Luna—
Qwen3.5-35B-A3B85.3%
Source
Not directly comparable
SuperGPQA
GPT-5.6 Luna—
Qwen3.5-35B-A3B63.4%
Source
Not directly comparable

Multilingual

MMLU-ProX
GPT-5.6 Luna—
Qwen3.5-35B-A3B81%
Source
Not directly comparable

Instruction following

IFEval
GPT-5.6 Luna—
Qwen3.5-35B-A3B91.9%
Source
Not directly comparable

Math

FrontierMath (legacy)
GPT-5.6 Luna78.6%
Source
Qwen3.5-35B-A3B—
Not directly comparable
FrontierMath v2 (Tiers 1-3)
GPT-5.6 Luna78.600%
Source
Qwen3.5-35B-A3B—
Not directly comparable
FrontierMath v2 (Tier 4)
GPT-5.6 Luna58.500%
Source
Qwen3.5-35B-A3B—
Not directly comparable

43 public results · 2 shared

Related comparisons

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Last updated September 24, 2026

GPT-5.6 Luna vs Qwen3.5-35B-A3B

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

Knowledge

Agentic

Coding

Reasoning

Multimodal

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, GPT-5.6 Luna or Qwen3.5-35B-A3B?

Which is better for coding, GPT-5.6 Luna or Qwen3.5-35B-A3B?

Which is better for agentic tasks, GPT-5.6 Luna or Qwen3.5-35B-A3B?

Which costs less, GPT-5.6 Luna or Qwen3.5-35B-A3B?

Which has the larger context window, GPT-5.6 Luna or Qwen3.5-35B-A3B?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Multilingual

Instruction following

Math

Related comparisons

Watch GPT-5.6 Luna vs Qwen3.5-35B-A3B