GPT-5.6 Luna vs Qwen3.7 Max

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 estimate, 65.6 versus 63.18, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 7 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

Qwen3.7 Max

Alibaba

63.18/100

Supported · Public rank #34

90% interval 54.1–72.2

Shared results: 7
GPT-5.6 Luna only: 22
Qwen3.7 Max only: 34
Like-for-like categories: 3 / 8

Supported: GPT-5.6 Luna and Qwen3.7 MaxHow 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.

Coding work
Code generation, repair, and software-engineering tasks
GPT-5.6 Luna
GPT-5.6 Luna leads on the public coding lane, 64.5 to 46.2, 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-5.6 Luna
GPT-5.6 Luna leads on the public agentic lane, 55.3 to 39.8, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
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

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 Luna46.2Qwen3.7 Max

Like-for-like · BenchAlign v5.7

GPT-5.6 Luna leads the like-for-like coding row, although the 90% intervals overlap.

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.

MMLU-Pro (Vals)Knowledge
Normalized gap 3.3
GPT-5.6 Luna:86.0%
Qwen3.7 Max:89.3%
GPT-5.6 Luna source Qwen3.7 Max source
SWE-bench ProCoding
Normalized gap 2.1
GPT-5.6 Luna:62.7%
Qwen3.7 Max:60.6%
GPT-5.6 Luna source Qwen3.7 Max source
GPQAKnowledge
Normalized gap 0.1
GPT-5.6 Luna:92.3%
Qwen3.7 Max:92.4%
GPT-5.6 Luna source Qwen3.7 Max 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.

Agentic

Like-for-like

GPT-5.6 Luna: 55.3; Supported · #28/105
Qwen3.7 Max: 39.8; Supported · #46/105
Basis: BenchAlign v5.7 lane · 9 vs 10 public rows
Reading: GPT-5.6 Luna leads · intervals overlap

Coding

Like-for-like

GPT-5.6 Luna: 64.5; Supported · #9/135
Qwen3.7 Max: 46.2; Supported · #45/135
Basis: BenchAlign v5.7 lane · 7 vs 10 public rows
Reading: GPT-5.6 Luna leads · intervals overlap

Knowledge

Like-for-like

GPT-5.6 Luna: 64.6; Supported · #22/158
Qwen3.7 Max: 60.8; Supported · #34/158
Basis: BenchAlign v5.7 lane · 6 vs 9 public rows
Reading: GPT-5.6 Luna leads · intervals overlap

Reasoning

Not comparable

GPT-5.6 Luna: 54.7; #18/19
Qwen3.7 Max: 76.1; 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
Qwen3.7 Max: Not ranked
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GPT-5.6 Luna: Not ranked
Qwen3.7 Max: 100.0; #1/12
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Instruction following

Not comparable

GPT-5.6 Luna: Not ranked
Qwen3.7 Max: 89.2; #17/124
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Math

Not comparable

GPT-5.6 Luna: 94.2; Unranked · 3 rankable rows
Qwen3.7 Max: 81.9; Unranked · 3 rankable rows
Basis: Provisional lane · 2 vs 1 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GPT-5.6 Luna and Qwen3.7 Max
Category	GPT-5.6 Luna	Qwen3.7 Max	Basis	Reading
Agentic	55.3Supported · #28/105	39.8Supported · #46/105	Like-for-likeBenchAlign v5.7 lane · 9 vs 10 public rows	GPT-5.6 Luna leads · intervals overlap
Coding	64.5Supported · #9/135	46.2Supported · #45/135	Like-for-likeBenchAlign v5.7 lane · 7 vs 10 public rows	GPT-5.6 Luna leads · intervals overlap
Knowledge	64.6Supported · #22/158	60.8Supported · #34/158	Like-for-likeBenchAlign v5.7 lane · 6 vs 9 public rows	GPT-5.6 Luna leads · intervals overlap
Reasoning	54.7#18/19	76.1Unranked · 3 rankable rows	Not comparableProvisional lane · 2 vs 1 weighted rows	Not comparable
Multimodal	67.1#22/50	Not ranked	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable
Multilingual	Not ranked	100.0#1/12	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Instruction following	Not ranked	89.2#17/124	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Math	94.2Unranked · 3 rankable rows	81.9Unranked · 3 rankable rows	Not comparableProvisional lane · 2 vs 1 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
Qwen3.7 Max: API rate not published; Fits in one request

Qwen3.7 Max 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
Qwen3.7 Max: API rate not published; Fits in one request

Qwen3.7 Max 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
Qwen3.7 Max: API rate not published; Fits in one request; Cached-input rate unavailable

Qwen3.7 Max 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 LunaQwen3.7 Max

Context window

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

GPT-5.6 Luna

1.05M

OpenAI model catalog

Qwen3.7 Max

API model ID

GPT-5.6 Luna

gpt-5.6-luna

OpenAI model catalog

Qwen3.7 Max

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

Qwen3.7 Max

No comparable hosted API rate

Documented inputs

GPT-5.6 Luna

text, image

OpenAI model catalog

Qwen3.7 Max

Not sourced

Documented outputs

GPT-5.6 Luna

text

OpenAI model catalog

Qwen3.7 Max

Not sourced

Provider availability

GPT-5.6 Luna

Generally Available · OpenAI Responses API

OpenAI model catalog

Qwen3.7 Max

Not sourced

Reasoning profile

GPT-5.6 Luna

Reasoning

Qwen3.7 Max

Reasoning

Weight access

GPT-5.6 Luna

Proprietary

Qwen3.7 Max

Proprietary

License

GPT-5.6 Luna

Proprietary

Qwen3.7 Max

Proprietary

Release date

GPT-5.6 Luna

2026-07-09

Qwen3.7 Max

2026-05-16

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, 65.6 versus 63.18, 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.

Questions

Which is better, GPT-5.6 Luna or Qwen3.7 Max?

GPT-5.6 Luna has the higher public score estimate, 65.6 versus 63.18, 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 Qwen3.7 Max?

GPT-5.6 Luna leads the public coding lane, 64.5 to 46.2, with Supported evidence for both models, although the 90% intervals overlap.

Which is better for agentic tasks, GPT-5.6 Luna or Qwen3.7 Max?

GPT-5.6 Luna leads the public agentic tasks lane, 55.3 to 39.8, with Supported evidence for both models, although the 90% intervals overlap.

Which costs less, GPT-5.6 Luna or Qwen3.7 Max?

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 Qwen3.7 Max?

GPT-5.6 Luna has the larger documented context window: 1.05M, compared with 1M.

Benchmark evidence

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

Browse raw public benchmark evidence63 rows

Agentic

Terminal-Bench 3.0
GPT-5.6 Luna14.3%
Source
Qwen3.7 Max—
Not directly comparable
Terminal-Bench 2.1
GPT-5.6 Luna84.7%
Source
Qwen3.7 Max—
Not directly comparable
BrowseComp
GPT-5.6 Luna83.3%
Source
Qwen3.7 Max—
Not directly comparable
OSWorld 2.0
GPT-5.6 Luna45.6%
Source
Qwen3.7 Max—
Not directly comparable
CyberGym
GPT-5.6 Luna77.9%
Source
Qwen3.7 Max—
Not directly comparable
ExploitGym
GPT-5.6 Luna12.4%
Source
Qwen3.7 Max—
Not directly comparable
Toolathlon
GPT-5.6 Luna53.4%
Source
Qwen3.7 Max—
Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.6 Luna79.0%
Source
Qwen3.7 Max61.0%
Source
GPT-5.6 Luna leads this result
ApprenticeBench
GPT-5.6 Luna7%
Source
Qwen3.7 Max—
Not directly comparable
Terminal-Bench 2.0
GPT-5.6 Luna—
Qwen3.7 Max69.7%
Source
Not directly comparable
QwenClawBench
GPT-5.6 Luna—
Qwen3.7 Max64.3%
Source
Not directly comparable
Claw-Eval
GPT-5.6 Luna—
Qwen3.7 Max65.2%
Source
Not directly comparable
BFCL v4
GPT-5.6 Luna—
Qwen3.7 Max75.0%
Source
Not directly comparable
MCP Atlas
GPT-5.6 Luna—
Qwen3.7 Max76.4%
Source
Not directly comparable
VITA-Bench
GPT-5.6 Luna—
Qwen3.7 Max47.9%
Source
Not directly comparable
HLE w/ tools
GPT-5.6 Luna—
Qwen3.7 Max53.5%
Source
Not directly comparable
Gert Labs
GPT-5.6 Luna—
Qwen3.7 Max64.27%
Source
Not directly comparable
ResearchClawBench
GPT-5.6 Luna—
Qwen3.7 Max18.7%
Source
Not directly comparable

Coding

SWE-bench Pro
GPT-5.6 Luna62.7%
Source
Qwen3.7 Max60.6%
Source
GPT-5.6 Luna leads this result
Terminal-Bench 2.1
GPT-5.6 Luna84.7%
Source
Qwen3.7 Max—
Not directly comparable
DeepSWE
GPT-5.6 Luna67.2%
Source
Qwen3.7 Max—
Not directly comparable
FrontierCode 1.1 Extended
GPT-5.6 Luna55.1%
Source
Qwen3.7 Max—
Not directly comparable
cursorBench32
GPT-5.6 Luna61.1%
Source
Qwen3.7 Max—
Not directly comparable
VulcanBench v3
GPT-5.6 Luna85.5%
Source
Qwen3.7 Max—
Not directly comparable
SWE-bench (Vals)
GPT-5.6 Luna93.0%
Source
Qwen3.7 Max68.8%
Source
GPT-5.6 Luna leads this result
SWE-bench Verified
GPT-5.6 Luna—
Qwen3.7 Max80.4%
Source
Not directly comparable
SWE Multilingual
GPT-5.6 Luna—
Qwen3.7 Max78.3%
Source
Not directly comparable
NL2Repo
GPT-5.6 Luna—
Qwen3.7 Max47.2%
Source
Not directly comparable
SciCode
GPT-5.6 Luna—
Qwen3.7 Max53.5%
Source
Not directly comparable
LiveCodeBench
GPT-5.6 Luna—
Qwen3.7 Max91.6%
Source
Not directly comparable
Terminal-Bench 2.0
GPT-5.6 Luna—
Qwen3.7 Max69.7%
Source
Not directly comparable
OpenHarmony Bench
GPT-5.6 Luna—
Qwen3.7 Max53.4%
Source
Not directly comparable
LiveCodeBench (Vals)
GPT-5.6 Luna—
Qwen3.7 Max87.1%
Source
Not directly comparable

Reasoning

ARC-AGI-2
GPT-5.6 Luna59.5%
Source
Qwen3.7 Max—
Not directly comparable
ARC-AGI-3
GPT-5.6 Luna0.2%
Source
Qwen3.7 Max—
Not directly comparable
MRCRv2
GPT-5.6 Luna—
Qwen3.7 Max90.4%
Source
Not directly comparable
CritPt
GPT-5.6 Luna—
Qwen3.7 Max13.4%
Source
Not directly comparable

Multimodal

MMMU-Pro
GPT-5.6 Luna78.4%
Source
Qwen3.7 Max—
Not directly comparable
MMMU-Pro w/ Python
GPT-5.6 Luna79.5%
Source
Qwen3.7 Max—
Not directly comparable

Knowledge

GPQA
GPT-5.6 Luna92.3%
Source
Qwen3.7 Max92.4%
Source
Qwen3.7 Max leads this result
GPQA-D
GPT-5.6 Luna92.3%
Source
Qwen3.7 Max92.4%
Source
Qwen3.7 Max leads this result
HealthBench Professional
GPT-5.6 Luna55.7%
Source
Qwen3.7 Max—
Not directly comparable
HealthBench Hard
GPT-5.6 Luna32.0%
Source
Qwen3.7 Max—
Not directly comparable
GPQA Diamond (Vals)
GPT-5.6 Luna91.7%
Source
Qwen3.7 Max90.2%
Source
GPT-5.6 Luna leads this result
MMLU-Pro (Vals)
GPT-5.6 Luna86.0%
Source
Qwen3.7 Max89.3%
Source
Qwen3.7 Max leads this result
HLE
GPT-5.6 Luna—
Qwen3.7 Max41.4%
Source
Not directly comparable
MMLU-Pro
GPT-5.6 Luna—
Qwen3.7 Max89.6%
Source
Not directly comparable
MMLU-Redux
GPT-5.6 Luna—
Qwen3.7 Max95%
Source
Not directly comparable
SuperGPQA
GPT-5.6 Luna—
Qwen3.7 Max73.6%
Source
Not directly comparable
MMMLU
GPT-5.6 Luna—
Qwen3.7 Max90.3%
Source
Not directly comparable

Multilingual

MMLU-ProX
GPT-5.6 Luna—
Qwen3.7 Max87%
Source
Not directly comparable
NOVA-63
GPT-5.6 Luna—
Qwen3.7 Max59.0%
Source
Not directly comparable
INCLUDE
GPT-5.6 Luna—
Qwen3.7 Max86.2%
Source
Not directly comparable
MAXIFE
GPT-5.6 Luna—
Qwen3.7 Max89.2%
Source
Not directly comparable
PolyMath
GPT-5.6 Luna—
Qwen3.7 Max86.5%
Source
Not directly comparable

Instruction following

IFEval
GPT-5.6 Luna—
Qwen3.7 Max94.3%
Source
Not directly comparable
IFBench
GPT-5.6 Luna—
Qwen3.7 Max79.1%
Source
Not directly comparable

Math

FrontierMath (legacy)
GPT-5.6 Luna78.6%
Source
Qwen3.7 Max—
Not directly comparable
FrontierMath v2 (Tiers 1-3)
GPT-5.6 Luna78.600%
Source
Qwen3.7 Max—
Not directly comparable
FrontierMath v2 (Tier 4)
GPT-5.6 Luna58.500%
Source
Qwen3.7 Max—
Not directly comparable
HMMT Feb 2026
GPT-5.6 Luna—
Qwen3.7 Max97.1%
Source
Not directly comparable
IMOAnswerBench
GPT-5.6 Luna—
Qwen3.7 Max90.0%
Source
Not directly comparable
Apex
GPT-5.6 Luna—
Qwen3.7 Max44.5%
Source
Not directly comparable

63 public results · 7 shared

Related comparisons

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Last updated September 24, 2026

GPT-5.6 Luna vs Qwen3.7 Max

Decision reading

Which one for your work

Coding work

Agentic work

Long documents

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

Knowledge

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 Qwen3.7 Max?

Which is better for coding, GPT-5.6 Luna or Qwen3.7 Max?

Which is better for agentic tasks, GPT-5.6 Luna or Qwen3.7 Max?

Which costs less, GPT-5.6 Luna or Qwen3.7 Max?

Which has the larger context window, GPT-5.6 Luna or Qwen3.7 Max?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Multilingual

Instruction following

Math

Related comparisons

Watch GPT-5.6 Luna vs Qwen3.7 Max