GPT-5.6 Luna vs Qwen3.8 Max (0902)

Qwen3.8 Max (0902) leads GPT-5.6 Luna on coding, 62.4 to 58.0 on the Vibe Coding Index, and GPT-5.6 Luna costs 5.0x less per output token ($1.20 vs $6.00 per million). Updated October 22, 2026; 102 benchmarks have a result for both.

Replace GPT-5.6 Luna Replace Qwen3.8 Max (0902)

GPT-5.6 LunaOpenAI

Coding: 58.0
Overall: 61.6
Output $/M: $1.20

Qwen3.8 Max (0902)Qwen

Coding: 62.4
Overall: 68.9
Output $/M: $6.00

vs boards won of 102 shared, 1 tied

GPT-5.6 Luna or Qwen3.8 Max (0902): which to pick

Pick Qwen3.8 Max (0902) for coding: it scores 62.4 on the Vibe Coding Index against 58.0 for GPT-5.6 Luna, though on 5 benchmarks against 6.

Pick GPT-5.6 Luna for output tokens 5.0x cheaper ($1.20 vs $6.00 per million) and a lower cost per solved task ($0.04 against $1.32).

Model AGPT-5.6 LunaOpenAI58.0/1006 independent benchmarks$0.04 per solved task Model BQwen3.8 Max (0902)Qwen62.4/1005 independent benchmarks$1.32 per solved task

1021812

Shared results: 102
GPT-5.6 Luna only: 18
Qwen3.8 Max (0902) only: 12
Like-for-like categories: 8 of 9

Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.

Which one for your work

Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.

Coding
Vibe Coding Index, six public coding benchmarks
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) scores 62.4 against 58.0.
6 and 5 benchmarks behind the scores
Intelligence
AA Intelligence Index
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 45.4 against 37.3.
Same board, both measured
Agentic work
AA Agentic Index
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 56.0 against 42.1.
Same board, both measured
Reasoning
GPQA Diamond
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 93.7% against 91.7%.
Same board, both measured
Knowledge
MMLU Pro
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 88.6% against 86.0%.
Same board, both measured
Web design
Design Arena: websites
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 1275 against 1255.
Same board, both measured
Long documents
Listed context window
GPT-5.6 Luna
GPT-5.6 Luna takes 1.05M tokens against 1M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-5.6 Luna
GPT-5.6 Luna $0.0008, Qwen3.8 Max (0902) $0.0050 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5.6 Luna
GPT-5.6 Luna $0.04, Qwen3.8 Max (0902) $1.32.
Published attempt cost and pass rate

At a glance

Coding index: GPT-5.6 Luna 58.0 from 6 benchmarks, Qwen3.8 Max (0902) 62.4 from 5 benchmarks.
Widest category: Agentic work, 14.4 points apart on average over 10 shared benchmarks.
Widest single gap: CyberBench (Vals AI): GPT-5.6 Luna 73.6% against Qwen3.8 Max (0902) 28.6%, 45.1 points apart on a 0 to 100 scale.
Output price: GPT-5.6 Luna $1.20 against Qwen3.8 Max (0902) $6.00 per million output tokens, 5.0x apart.
Context window: GPT-5.6 Luna takes 1.05M tokens, Qwen3.8 Max (0902) 1M.

Benchmark by benchmark

First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.

Boards won, where both have a result
Category	GPT-5.6 Luna	Qwen3.8 Max (0902)	Ties
Coding	9	9	1 tie
Intelligence	0	2
Agentic work	3	7
Reasoning	0	14
Math	3	3
Knowledge	4	7
Web design	0	8
General	4	28
All shared boards	23	78	1 tie

CodingLike-for-likeSix of these boards feed the Vibe Coding Index99

Coding benchmarks for GPT-5.6 Luna and Qwen3.8 Max (0902)
Benchmark	GPT-5.6 Luna	Qwen3.8 Max (0902)	Leader
LiveBench coding	73.6%	60.3%	GPT-5.6 Luna
LMArena WebDev	1518	1674	Qwen3.8 Max (0902)
AA Coding Index	71.4	76.2	Qwen3.8 Max (0902)
LiveCodeBench (Vals AI)	Not measured	87.8%	Only Qwen3.8 Max (0902) measured
SWE-bench Verified (Vals AI)	93.0%	85.6%	GPT-5.6 Luna
Terminal-Bench 4 (Vals AI)	11.6%	34.3%	Qwen3.8 Max (0902)
Vibe Code Bench (Vals AI)	77.1%	64.7%	GPT-5.6 Luna
Code migration (Vals AI)	44.5%	24.0%	GPT-5.6 Luna
ProgramBench (Vals AI)	0.0%	0.0%	Tie
IOI (Vals AI)	61.8%	68.9%	Qwen3.8 Max (0902)
Terminal-Bench 2.1 (Vals AI)	79.0%	67.4%	GPT-5.6 Luna
SWE-bench Multimodal (Vals AI)	Not measured	42.5%	Only Qwen3.8 Max (0902) measured
Vibe Code Bench 1-100 (Vals AI)	22.6%	12.8%	GPT-5.6 Luna
SkillsBench (Vals AI)	60.5%	42.0%	GPT-5.6 Luna
CyberBench (Vals AI)	73.6%	28.6%	GPT-5.6 Luna
SciCode	53.6%	53.2%	GPT-5.6 Luna
FrontierCode	39.8%	Not measured	Only GPT-5.6 Luna measured
FrontierSWE	Not measured	17.8%	Only Qwen3.8 Max (0902) measured
WeirdML	60.9%	Not measured	Only GPT-5.6 Luna measured
ALE-Bench	1667.4	Not measured	Only GPT-5.6 Luna measured
LMArena Text: coding	1466	1502	Qwen3.8 Max (0902)
LMArena Text: software and IT	1463	1498	Qwen3.8 Max (0902)
LMArena WebDev: React	1508	1692	Qwen3.8 Max (0902)
LMArena WebDev: HTML	1520	1649	Qwen3.8 Max (0902)
LMArena WebDev: from an image	1517	1637	Qwen3.8 Max (0902)
SciCode	6.2%	Not measured	Only GPT-5.6 Luna measured
Neither model has a result on the other 17 boards here

IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02

Intelligence benchmarks for GPT-5.6 Luna and Qwen3.8 Max (0902)
Benchmark	GPT-5.6 Luna	Qwen3.8 Max (0902)	Leader
AA Intelligence Index	37.3	45.4	Qwen3.8 Max (0902)
Epoch Capabilities Index	156.5	157.4	Qwen3.8 Max (0902)

Agentic workLike-for-likePublic boards, shown as published, kept out of the index37

Agentic work benchmarks for GPT-5.6 Luna and Qwen3.8 Max (0902)
Benchmark	GPT-5.6 Luna	Qwen3.8 Max (0902)	Leader
AA Agentic Index	42.1	56.0	Qwen3.8 Max (0902)
Design Arena: agents building web apps	Not measured	1300	Only Qwen3.8 Max (0902) measured
Design Arena: agents building full-stack apps	Not measured	1305	Only Qwen3.8 Max (0902) measured
Design Arena: agents building mobile apps	Not measured	1224	Only Qwen3.8 Max (0902) measured
Design Arena: agents building Android apps	Not measured	1190	Only Qwen3.8 Max (0902) measured
Design Arena: agents building Godot games	Not measured	1234	Only Qwen3.8 Max (0902) measured
Design Arena: PowerPoint slides	Not measured	1232	Only Qwen3.8 Max (0902) measured
Design Arena: spreadsheets	Not measured	993	Only Qwen3.8 Max (0902) measured
LiveBench research	51.0%	69.9%	Qwen3.8 Max (0902)
LiveBench browser	68.0%	32.0%	GPT-5.6 Luna
LiveBench long agentic	30.4%	42.3%	Qwen3.8 Max (0902)
SREBench (Vals AI)	1.5%	Not measured	Only GPT-5.6 Luna measured
Finance Agent (Vals AI)	55.0%	50.6%	GPT-5.6 Luna
Tax Agent Bench (Vals AI)	24.8%	32.1%	Qwen3.8 Max (0902)
Legal Research Bench (Vals AI)	36.5%	47.6%	Qwen3.8 Max (0902)
Excel Modeling Benchmark (Vals AI)	67.1%	60.1%	GPT-5.6 Luna
BioMysteryBench (Vals AI)	61.5%	Not measured	Only GPT-5.6 Luna measured
Terminal-Bench-Science (Vals AI)	0.0%	12.9%	Qwen3.8 Max (0902)
Vals Science Index	25.3%	Not measured	Only GPT-5.6 Luna measured
APEX-Agents	43.0%	63.3%	Qwen3.8 Max (0902)
BALROG	45.6%	Not measured	Only GPT-5.6 Luna measured
Vending-Bench 2	$4,095	Not measured	Only GPT-5.6 Luna measured
LMArena Search	Not measured	1261	Only Qwen3.8 Max (0902) measured
Neither model has a result on the other 19 boards here

ReasoningLike-for-likePublic boards, shown as published, kept out of the index014

Reasoning benchmarks for GPT-5.6 Luna and Qwen3.8 Max (0902)
Benchmark	GPT-5.6 Luna	Qwen3.8 Max (0902)	Leader
GPQA Diamond	91.7%	93.7%	Qwen3.8 Max (0902)
LiveBench reasoning	85.6%	88.2%	Qwen3.8 Max (0902)
MysteryMechanism (Vals AI)	14.4%	23.9%	Qwen3.8 Max (0902)
MMMU Pro (Vals AI)	85.0%	88.0%	Qwen3.8 Max (0902)
GPQA Diamond (Artificial Analysis run)	91.1%	92.8%	Qwen3.8 Max (0902)
Humanity's Last Exam (Artificial Analysis run)	39.5%	43.1%	Qwen3.8 Max (0902)
GPQA Diamond (Epoch AI run)	91.6%	92.7%	Qwen3.8 Max (0902)
ARC-AGI	88.0%	Not measured	Only GPT-5.6 Luna measured
ARC-AGI-2	59.5%	Not measured	Only GPT-5.6 Luna measured
SimpleBench	46.8%	Not measured	Only GPT-5.6 Luna measured
Chess puzzles	43.0%	46.0%	Qwen3.8 Max (0902)
Mystery game puzzles	21.0%	38.0%	Qwen3.8 Max (0902)
LMArena Text: hard prompts	1451	1496	Qwen3.8 Max (0902)
LMArena Text: expert	1478	1507	Qwen3.8 Max (0902)
LMArena Text: hard prompts in English	1460	1503	Qwen3.8 Max (0902)
LMArena Vision: diagrams	1273	1323	Qwen3.8 Max (0902)
LMArena Vision: homework	1282	1336	Qwen3.8 Max (0902)
Kagi LLM Benchmark	49.1%	Not measured	Only GPT-5.6 Luna measured
Neither model has a result on the other 5 boards here

MathLike-for-likePublic boards, shown as published, kept out of the index33

Math benchmarks for GPT-5.6 Luna and Qwen3.8 Max (0902)
Benchmark	GPT-5.6 Luna	Qwen3.8 Max (0902)	Leader
OTIS Mock AIME	98.3%	100.0%	Qwen3.8 Max (0902)
FrontierMath, tiers 1 to 3	82.1%	74.7%	GPT-5.6 Luna
FrontierMath, tier 4	61.0%	46.3%	GPT-5.6 Luna
ProofBench	60.0%	58.0%	GPT-5.6 Luna
LMArena Text: math	1459	1501	Qwen3.8 Max (0902)
LMArena Text: mathematical work	1470	1491	Qwen3.8 Max (0902)
MathArena	Not measured	51.6%	Only Qwen3.8 Max (0902) measured
Neither model has a result on the other 7 boards here

KnowledgeLike-for-likePublic boards, shown as published, kept out of the index47

Knowledge benchmarks for GPT-5.6 Luna and Qwen3.8 Max (0902)
Benchmark	GPT-5.6 Luna	Qwen3.8 Max (0902)	Leader
MMLU Pro	86.0%	88.6%	Qwen3.8 Max (0902)
LegalBench (Vals AI)	84.0%	83.6%	GPT-5.6 Luna
CorpFin (Vals AI)	64.2%	65.8%	Qwen3.8 Max (0902)
TaxEval (Vals AI)	76.2%	75.5%	GPT-5.6 Luna
MortgageTax (Vals AI)	67.3%	64.0%	GPT-5.6 Luna
MedCode (Vals AI)	42.4%	40.7%	GPT-5.6 Luna
SimpleQA Verified	41.0%	47.3%	Qwen3.8 Max (0902)
LMArena Text: science	1433	1512	Qwen3.8 Max (0902)
LMArena Text: medicine and healthcare	1399	1503	Qwen3.8 Max (0902)
LMArena Text: legal and government	1446	1492	Qwen3.8 Max (0902)
LMArena Text: business and finance	1439	1480	Qwen3.8 Max (0902)
FACTS Benchmark Suite	35.0%	Not measured	Only GPT-5.6 Luna measured
Neither model has a result on the other 8 boards here

Web designLike-for-likePublic boards, shown as published, kept out of the index08

Web design benchmarks for GPT-5.6 Luna and Qwen3.8 Max (0902)
Benchmark	GPT-5.6 Luna	Qwen3.8 Max (0902)	Leader
Design Arena: websites	1255	1275	Qwen3.8 Max (0902)
Design Arena: UI components	1248	1336	Qwen3.8 Max (0902)
Design Arena: game dev	1195	1312	Qwen3.8 Max (0902)
Design Arena: data viz	1212	1282	Qwen3.8 Max (0902)
Design Arena: 3D	1121	1336	Qwen3.8 Max (0902)
Design Arena: all code categories	1230	1292	Qwen3.8 Max (0902)
Design Arena: SVG	1193	Not measured	Only GPT-5.6 Luna measured
Design Arena: ASCII art	1142	1200	Qwen3.8 Max (0902)
LiveBench design	76.0%	78.6%	Qwen3.8 Max (0902)

GeneralLike-for-likePublic boards, shown as published, kept out of the index428

General benchmarks for GPT-5.6 Luna and Qwen3.8 Max (0902)
Benchmark	GPT-5.6 Luna	Qwen3.8 Max (0902)	Leader
LiveBench data analysis	78.0%	78.4%	Qwen3.8 Max (0902)
Public Benefits Bench (Vals AI)	61.2%	67.1%	Qwen3.8 Max (0902)
SAGE (Vals AI)	44.2%	51.2%	Qwen3.8 Max (0902)
MedScribe (Vals AI)	84.4%	85.0%	Qwen3.8 Max (0902)
Vals Multimodal Index	69.1%	65.4%	GPT-5.6 Luna
Long context reasoning (AA-LCR)	83.7%	80.3%	GPT-5.6 Luna
DTBench	89.1%	92.0%	Qwen3.8 Max (0902)
LMCA	48.5%	46.2%	GPT-5.6 Luna
Surface Evolver Bench	61.9%	Not measured	Only GPT-5.6 Luna measured
Furniture assembly	42.5%	20.0%	GPT-5.6 Luna
LMArena Text	1431	1483	Qwen3.8 Max (0902)
LMArena Text: instruction following	1437	1481	Qwen3.8 Max (0902)
LMArena Text: creative writing	1396	1477	Qwen3.8 Max (0902)
LMArena Vision	1257	1313	Qwen3.8 Max (0902)
LMArena Text: English	1441	1491	Qwen3.8 Max (0902)
LMArena Text: non-English	1417	1473	Qwen3.8 Max (0902)
LMArena Text: Chinese	1470	1539	Qwen3.8 Max (0902)
LMArena Text: German	1455	1478	Qwen3.8 Max (0902)
LMArena Text: Japanese	1415	1473	Qwen3.8 Max (0902)
LMArena Text: Korean	1415	1460	Qwen3.8 Max (0902)
LMArena Text: French	1458	1498	Qwen3.8 Max (0902)
LMArena Text: Spanish	1446	1489	Qwen3.8 Max (0902)
LMArena Text: Russian	1427	1482	Qwen3.8 Max (0902)
LMArena Text: Polish	1405	1471	Qwen3.8 Max (0902)
LMArena Text: multi-turn	1434	1489	Qwen3.8 Max (0902)
LMArena Text: longer queries	1436	1488	Qwen3.8 Max (0902)
LMArena Text: writing and language	1419	1477	Qwen3.8 Max (0902)
LMArena Text: entertainment and media	1402	1464	Qwen3.8 Max (0902)
LMArena Vision: English	1263	1311	Qwen3.8 Max (0902)
LMArena Vision: Chinese	1294	1358	Qwen3.8 Max (0902)
LMArena Vision: reading text	1264	1322	Qwen3.8 Max (0902)
LMArena Vision: humor	1190	1304	Qwen3.8 Max (0902)
LMArena Vision: creative writing	1257	1312	Qwen3.8 Max (0902)
LMArena Document	1457	Not measured	Only GPT-5.6 Luna measured
EQ-Bench 4	1156	Not measured	Only GPT-5.6 Luna measured
Neither model has a result on the other 13 boards here

Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared

Image generation benchmarks for GPT-5.6 Luna and Qwen3.8 Max (0902)
Benchmark	GPT-5.6 Luna	Qwen3.8 Max (0902)	Leader
Neither model has a result on 5 boards here

What each workload costs

Three fixed token mixes at each model's listed price, so per-token rates become a bill.

Chat turn

1K input + 500 output tokens

GPT-5.6 Luna$0.0008Fits in one request

Qwen3.8 Max (0902)$0.0050Fits in one request

GPT-5.6 Luna costs less

Repository review

50K input + 3K output tokens

GPT-5.6 Luna$0.014Fits in one request

Qwen3.8 Max (0902)$0.118Fits in one request

GPT-5.6 Luna costs less

Agent loop

200K input + 10K output tokens

GPT-5.6 Luna$0.052Fits in one request

Qwen3.8 Max (0902)$0.460Fits in one request

GPT-5.6 Luna costs less

Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.

Specification differences

The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.

Specifications of GPT-5.6 Luna and Qwen3.8 Max (0902)
Specification	OpenAI GPT-5.6 Luna	Qwen3.8 Max (0902)
Maker	OpenAI	Qwen
Weights	No public weights listed	No public weights listed
Released	July 9, 2026	September 3, 2026
Context window	1.05M	1M
Input $/Mtok	$0.20	$2.00
Output $/Mtok	$1.20	$6.00
Coding index	58.0	62.4
Independent measurements behind it	6	5
Cost per solved task	$0.04	$1.32
Source	OpenRouter listing	OpenRouter listing

Questions

Is GPT-5.6 Luna better than Qwen3.8 Max (0902) for coding?

Pick Qwen3.8 Max (0902) for coding: it scores 62.4 on the Vibe Coding Index against 58.0 for GPT-5.6 Luna, though on 5 benchmarks against 6. Pick GPT-5.6 Luna for output tokens 5.0x cheaper ($1.20 vs $6.00 per million) and a lower cost per solved task ($0.04 against $1.32).

Which is cheaper, GPT-5.6 Luna or Qwen3.8 Max (0902)?

GPT-5.6 Luna costs $0.20 per million input tokens and $1.20 per million output tokens; Qwen3.8 Max (0902) costs $2.00 and $6.00. Per solved SWE-bench Verified task, GPT-5.6 Luna costs $0.04 and Qwen3.8 Max (0902) $1.32.

Which has the bigger context window, GPT-5.6 Luna or Qwen3.8 Max (0902)?

GPT-5.6 Luna, with 1.05M tokens against 1M.

How many benchmarks is the GPT-5.6 Luna vs Qwen3.8 Max (0902) comparison based on?

102 benchmarks have a result for both models as of October 22, 2026. GPT-5.6 Luna's coding index rests on 6 independent benchmarks and Qwen3.8 Max (0902)'s on 5. Where only one model has a result, the page says so instead of naming a winner.

Compare next

Embed this comparison on your site

Free under CC BY 4.0, while the source link stays visible. Preview the frame · Terms and other embeds

<iframe src="https://llmbenchmark.ai/embed/compare/gpt-5-6-luna-vs-qwen3-8-max-0902" title="GPT-5.6 Luna vs Qwen3.8 Max (0902), LLMBenchmark.ai" width="100%" height="360" style="border:0;max-width:100%" loading="lazy"></iframe>
<p style="font:13px/1.4 sans-serif;margin:6px 0 0">Source: <a href="https://llmbenchmark.ai/compare/gpt-5-6-luna-vs-qwen3-8-max-0902">LLMBenchmark.ai, GPT-5.6 Luna vs Qwen3.8 Max (0902)</a></p>

GPT-5.6 Luna or Qwen3.8 Max (0902): which to pick

Which one for your work

Coding

Intelligence

Agentic work

Reasoning

Knowledge

Web design

Long documents

Everyday cost

Cost per solved task

At a glance

Benchmark by benchmark

What each workload costs

Chat turn

Repository review

Agent loop

Specification differences

Questions

Compare next