GPT-6 Luna vs Qwen3.8 Max (0902)

Qwen3.8 Max (0902) leads GPT-6 Luna on coding, 62.4 to 57.3 on the Vibe Coding Index, and GPT-6 Luna costs 12.0x less per output token ($0.50 vs $6.00 per million). Updated October 22, 2026; 91 benchmarks have a result for both.

Replace GPT-6 Luna Replace Qwen3.8 Max (0902)

GPT-6 LunaOpenAI

Coding: 57.3
Overall: 59.7
Output $/M: $0.50

Qwen3.8 Max (0902)Qwen

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

vs boards won of 91 shared

GPT-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 57.3 for GPT-6 Luna.

Pick GPT-6 Luna for output tokens 12.0x cheaper ($0.50 vs $6.00 per million) and a larger context window (1.05M against 1M tokens).

Model AGPT-6 LunaOpenAI57.3/1005 independent benchmarksNo cost per solved task Model BQwen3.8 Max (0902)Qwen62.4/1005 independent benchmarks$1.32 per solved task

911323

Shared results: 91
GPT-6 Luna only: 13
Qwen3.8 Max (0902) only: 23
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 57.3.
5 benchmarks behind each score
Intelligence
AA Intelligence Index
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 45.4 against 38.1.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Qwen3.8 Max (0902) has a AA Agentic Index result, 56.0.
Directional only
Reasoning
GPQA Diamond
No call
Only Qwen3.8 Max (0902) has a GPQA Diamond result, 93.7%.
Directional only
Knowledge
MMLU Pro
No call
Only Qwen3.8 Max (0902) has a MMLU Pro result, 88.6%.
Directional only
Web design
Design Arena: websites
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 1275 against 1271.
Same board, both measured
General
Vals Index
No call
Only GPT-6 Luna has a Vals Index result, 52.3%.
Directional only
Long documents
Listed context window
GPT-6 Luna
GPT-6 Luna takes 1.05M tokens against 1M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-6 Luna
GPT-6 Luna $0.0003, Qwen3.8 Max (0902) $0.0050 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Qwen3.8 Max (0902) has the number, $1.32.
Directional only

At a glance

Coding index: GPT-6 Luna 57.3 from 5 benchmarks, Qwen3.8 Max (0902) 62.4 from 5 benchmarks.
Widest category: Agentic work, 15.2 points apart on average over 12 shared benchmarks.
Widest single gap: CyberBench (Vals AI): GPT-6 Luna 76.3% against Qwen3.8 Max (0902) 28.6%, 47.7 points apart on a 0 to 100 scale.
Output price: GPT-6 Luna $0.50 against Qwen3.8 Max (0902) $6.00 per million output tokens, 12.0x apart.
Context window: GPT-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-6 Luna	Qwen3.8 Max (0902)
Coding	7	9
Intelligence	0	2
Agentic work	3	9
Reasoning	0	11
Math	3	3
Knowledge	1	5
Web design	1	7
General	2	28
All shared boards	17	74

CodingLike-for-likeSix of these boards feed the Vibe Coding Index79

Coding benchmarks for GPT-6 Luna and Qwen3.8 Max (0902)
Benchmark	GPT-6 Luna	Qwen3.8 Max (0902)	Leader
LiveBench coding	84.5%	60.3%	GPT-6 Luna
LMArena WebDev	1578	1674	Qwen3.8 Max (0902)
AA Coding Index	Not measured	76.2	Only Qwen3.8 Max (0902) measured
LiveCodeBench (Vals AI)	Not measured	87.8%	Only Qwen3.8 Max (0902) measured
SWE-bench Verified (Vals AI)	Not measured	85.6%	Only Qwen3.8 Max (0902) measured
Terminal-Bench 4 (Vals AI)	13.6%	34.3%	Qwen3.8 Max (0902)
Vibe Code Bench (Vals AI)	81.7%	64.7%	GPT-6 Luna
Code migration (Vals AI)	42.5%	24.0%	GPT-6 Luna
ProgramBench (Vals AI)	0.5%	0.0%	GPT-6 Luna
IOI (Vals AI)	55.6%	68.9%	Qwen3.8 Max (0902)
Terminal-Bench 2.1 (Vals AI)	73.0%	67.4%	GPT-6 Luna
SWE-bench Multimodal (Vals AI)	37.5%	42.5%	Qwen3.8 Max (0902)
Vibe Code Bench 1-100 (Vals AI)	Not measured	12.8%	Only Qwen3.8 Max (0902) measured
SkillsBench (Vals AI)	Not measured	42.0%	Only Qwen3.8 Max (0902) measured
CyberBench (Vals AI)	76.3%	28.6%	GPT-6 Luna
SciCode	54.6%	53.2%	GPT-6 Luna
FrontierCode	42.4%	Not measured	Only GPT-6 Luna measured
FrontierSWE	Not measured	17.8%	Only Qwen3.8 Max (0902) measured
ALE-Bench	1576.9	Not measured	Only GPT-6 Luna measured
LMArena Text: coding	1437	1502	Qwen3.8 Max (0902)
LMArena Text: software and IT	1423	1498	Qwen3.8 Max (0902)
LMArena WebDev: React	1573	1692	Qwen3.8 Max (0902)
LMArena WebDev: HTML	1595	1649	Qwen3.8 Max (0902)
LMArena WebDev: from an image	1571	1637	Qwen3.8 Max (0902)
Neither model has a result on the other 19 boards here

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

Intelligence benchmarks for GPT-6 Luna and Qwen3.8 Max (0902)
Benchmark	GPT-6 Luna	Qwen3.8 Max (0902)	Leader
AA Intelligence Index	38.1	45.4	Qwen3.8 Max (0902)
Epoch Capabilities Index	156.8	157.4	Qwen3.8 Max (0902)

Agentic workLike-for-likePublic boards, shown as published, kept out of the index39

Agentic work benchmarks for GPT-6 Luna and Qwen3.8 Max (0902)
Benchmark	GPT-6 Luna	Qwen3.8 Max (0902)	Leader
AA Agentic Index	Not measured	56.0	Only Qwen3.8 Max (0902) measured
Design Arena: agents building web apps	1191	1300	Qwen3.8 Max (0902)
Design Arena: agents building full-stack apps	1158	1305	Qwen3.8 Max (0902)
Design Arena: agents building mobile apps	1131	1224	Qwen3.8 Max (0902)
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	43.5%	69.9%	Qwen3.8 Max (0902)
LiveBench browser	72.0%	32.0%	GPT-6 Luna
LiveBench long agentic	48.1%	42.3%	GPT-6 Luna
SREBench (Vals AI)	2.7%	Not measured	Only GPT-6 Luna measured
Finance Agent (Vals AI)	49.9%	50.6%	Qwen3.8 Max (0902)
Tax Agent Bench (Vals AI)	19.9%	32.1%	Qwen3.8 Max (0902)
Legal Research Bench (Vals AI)	30.3%	47.6%	Qwen3.8 Max (0902)
Harvey's Legal Agent Benchmark (Vals AI)	4.2%	Not measured	Only GPT-6 Luna measured
Excel Modeling Benchmark (Vals AI)	68.5%	60.1%	GPT-6 Luna
BioMysteryBench (Vals AI)	61.5%	Not measured	Only GPT-6 Luna measured
Terminal-Bench-Science (Vals AI)	4.3%	12.9%	Qwen3.8 Max (0902)
Vals Science Index	28.4%	Not measured	Only GPT-6 Luna measured
APEX-Agents	44.3%	63.3%	Qwen3.8 Max (0902)
SpireClimb	0.0%	Not measured	Only GPT-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 index011

Reasoning benchmarks for GPT-6 Luna and Qwen3.8 Max (0902)
Benchmark	GPT-6 Luna	Qwen3.8 Max (0902)	Leader
GPQA Diamond	Not measured	93.7%	Only Qwen3.8 Max (0902) measured
LiveBench reasoning	81.8%	88.2%	Qwen3.8 Max (0902)
MysteryMechanism (Vals AI)	19.4%	23.9%	Qwen3.8 Max (0902)
MMMU Pro (Vals AI)	Not measured	88.0%	Only Qwen3.8 Max (0902) measured
GPQA Diamond (Artificial Analysis run)	Not measured	92.8%	Only Qwen3.8 Max (0902) measured
Humanity's Last Exam (Artificial Analysis run)	38.5%	43.1%	Qwen3.8 Max (0902)
GPQA Diamond (Epoch AI run)	90.5%	92.7%	Qwen3.8 Max (0902)
ARC-AGI	86.7%	Not measured	Only GPT-6 Luna measured
ARC-AGI-2	59.3%	Not measured	Only GPT-6 Luna measured
Chess puzzles	36.0%	46.0%	Qwen3.8 Max (0902)
Mystery game puzzles	7.0%	38.0%	Qwen3.8 Max (0902)
LMArena Text: hard prompts	1410	1496	Qwen3.8 Max (0902)
LMArena Text: expert	1442	1507	Qwen3.8 Max (0902)
LMArena Text: hard prompts in English	1410	1503	Qwen3.8 Max (0902)
LMArena Vision: diagrams	1245	1323	Qwen3.8 Max (0902)
LMArena Vision: homework	1261	1336	Qwen3.8 Max (0902)
Neither model has a result on the other 7 boards here

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

Math benchmarks for GPT-6 Luna and Qwen3.8 Max (0902)
Benchmark	GPT-6 Luna	Qwen3.8 Max (0902)	Leader
OTIS Mock AIME	98.9%	100.0%	Qwen3.8 Max (0902)
FrontierMath, tiers 1 to 3	78.9%	74.7%	GPT-6 Luna
FrontierMath, tier 4	56.1%	46.3%	GPT-6 Luna
ProofBench	64.0%	58.0%	GPT-6 Luna
LMArena Text: math	1409	1501	Qwen3.8 Max (0902)
LMArena Text: mathematical work	1427	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 index15

Knowledge benchmarks for GPT-6 Luna and Qwen3.8 Max (0902)
Benchmark	GPT-6 Luna	Qwen3.8 Max (0902)	Leader
MMLU Pro	Not measured	88.6%	Only Qwen3.8 Max (0902) measured
LegalBench (Vals AI)	Not measured	83.6%	Only Qwen3.8 Max (0902) measured
CorpFin (Vals AI)	Not measured	65.8%	Only Qwen3.8 Max (0902) measured
TaxEval (Vals AI)	Not measured	75.5%	Only Qwen3.8 Max (0902) measured
MortgageTax (Vals AI)	Not measured	64.0%	Only Qwen3.8 Max (0902) measured
MedCode (Vals AI)	44.7%	40.7%	GPT-6 Luna
SimpleQA Verified	41.4%	47.3%	Qwen3.8 Max (0902)
LMArena Text: science	1386	1512	Qwen3.8 Max (0902)
LMArena Text: medicine and healthcare	1352	1503	Qwen3.8 Max (0902)
LMArena Text: legal and government	1400	1492	Qwen3.8 Max (0902)
LMArena Text: business and finance	1388	1480	Qwen3.8 Max (0902)
FACTS Benchmark Suite	63.3%	Not measured	Only GPT-6 Luna measured
PRBench Legal	42.9%	Not measured	Only GPT-6 Luna measured
Neither model has a result on the other 7 boards here

Web designLike-for-likePublic boards, shown as published, kept out of the index17

Web design benchmarks for GPT-6 Luna and Qwen3.8 Max (0902)
Benchmark	GPT-6 Luna	Qwen3.8 Max (0902)	Leader
Design Arena: websites	1271	1275	Qwen3.8 Max (0902)
Design Arena: UI components	1295	1336	Qwen3.8 Max (0902)
Design Arena: game dev	1258	1312	Qwen3.8 Max (0902)
Design Arena: data viz	1217	1282	Qwen3.8 Max (0902)
Design Arena: 3D	1218	1336	Qwen3.8 Max (0902)
Design Arena: all code categories	1270	1292	Qwen3.8 Max (0902)
Design Arena: SVG	1253	Not measured	Only GPT-6 Luna measured
Design Arena: ASCII art	1153	1200	Qwen3.8 Max (0902)
LiveBench design	82.5%	78.6%	GPT-6 Luna

GeneralLike-for-likePublic boards, shown as published, kept out of the index228

General benchmarks for GPT-6 Luna and Qwen3.8 Max (0902)
Benchmark	GPT-6 Luna	Qwen3.8 Max (0902)	Leader
Vals Index	52.3%	Not measured	Only GPT-6 Luna measured
LiveBench data analysis	73.4%	78.4%	Qwen3.8 Max (0902)
Public Benefits Bench (Vals AI)	57.6%	67.1%	Qwen3.8 Max (0902)
SAGE (Vals AI)	48.1%	51.2%	Qwen3.8 Max (0902)
MedScribe (Vals AI)	83.7%	85.0%	Qwen3.8 Max (0902)
Vals Multimodal Index	Not measured	65.4%	Only Qwen3.8 Max (0902) measured
Long context reasoning (AA-LCR)	83.3%	80.3%	GPT-6 Luna
DTBench	90.1%	92.0%	Qwen3.8 Max (0902)
LMCA	44.5%	46.2%	Qwen3.8 Max (0902)
Furniture assembly	44.2%	20.0%	GPT-6 Luna
LMArena Text	1389	1483	Qwen3.8 Max (0902)
LMArena Text: instruction following	1408	1481	Qwen3.8 Max (0902)
LMArena Text: creative writing	1363	1477	Qwen3.8 Max (0902)
LMArena Vision	1216	1313	Qwen3.8 Max (0902)
LMArena Text: English	1393	1491	Qwen3.8 Max (0902)
LMArena Text: non-English	1381	1473	Qwen3.8 Max (0902)
LMArena Text: Chinese	1427	1539	Qwen3.8 Max (0902)
LMArena Text: German	1376	1478	Qwen3.8 Max (0902)
LMArena Text: Japanese	1369	1473	Qwen3.8 Max (0902)
LMArena Text: Korean	1359	1460	Qwen3.8 Max (0902)
LMArena Text: French	1408	1498	Qwen3.8 Max (0902)
LMArena Text: Spanish	1391	1489	Qwen3.8 Max (0902)
LMArena Text: Russian	1392	1482	Qwen3.8 Max (0902)
LMArena Text: Polish	1383	1471	Qwen3.8 Max (0902)
LMArena Text: multi-turn	1392	1489	Qwen3.8 Max (0902)
LMArena Text: longer queries	1408	1488	Qwen3.8 Max (0902)
LMArena Text: writing and language	1384	1477	Qwen3.8 Max (0902)
LMArena Text: entertainment and media	1364	1464	Qwen3.8 Max (0902)
LMArena Vision: English	1204	1311	Qwen3.8 Max (0902)
LMArena Vision: Chinese	1266	1358	Qwen3.8 Max (0902)
LMArena Vision: reading text	1225	1322	Qwen3.8 Max (0902)
LMArena Vision: humor	Not measured	1304	Only Qwen3.8 Max (0902) measured
LMArena Vision: creative writing	1205	1312	Qwen3.8 Max (0902)
Neither model has a result on the other 15 boards here

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

Image generation benchmarks for GPT-6 Luna and Qwen3.8 Max (0902)
Benchmark	GPT-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-6 Luna$0.0003Fits in one request

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

GPT-6 Luna costs less

Repository review

50K input + 3K output tokens

GPT-6 Luna$0.0065Fits in one request

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

GPT-6 Luna costs less

Agent loop

200K input + 10K output tokens

GPT-6 Luna$0.025Fits in one request

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

GPT-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-6 Luna and Qwen3.8 Max (0902)
Specification	OpenAI GPT-6 Luna	Qwen3.8 Max (0902)
Maker	OpenAI	Qwen
Weights	No public weights listed	No public weights listed
Released	September 22, 2026	September 3, 2026
Context window	1.05M	1M
Input $/Mtok	$0.10	$2.00
Output $/Mtok	$0.50	$6.00
Coding index	57.3	62.4
Independent measurements behind it	5	5
Cost per solved task	Not computable	$1.32
Source	OpenRouter listing	OpenRouter listing

Questions

Is GPT-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 57.3 for GPT-6 Luna. Pick GPT-6 Luna for output tokens 12.0x cheaper ($0.50 vs $6.00 per million) and a larger context window (1.05M against 1M tokens).

Which is cheaper, GPT-6 Luna or Qwen3.8 Max (0902)?

GPT-6 Luna costs $0.10 per million input tokens and $0.50 per million output tokens; Qwen3.8 Max (0902) costs $2.00 and $6.00. Cost per solved task is known for Qwen3.8 Max (0902) only.

Which has the bigger context window, GPT-6 Luna or Qwen3.8 Max (0902)?

GPT-6 Luna, with 1.05M tokens against 1M.

How many benchmarks is the GPT-6 Luna vs Qwen3.8 Max (0902) comparison based on?

91 benchmarks have a result for both models as of October 22, 2026. GPT-6 Luna's coding index rests on 5 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.

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<iframe src="https://llmbenchmark.ai/embed/compare/gpt-6-luna-vs-qwen3-8-max-0902" title="GPT-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-6-luna-vs-qwen3-8-max-0902">LLMBenchmark.ai, GPT-6 Luna vs Qwen3.8 Max (0902)</a></p>

GPT-6 Luna or Qwen3.8 Max (0902): which to pick

Which one for your work

Coding

Intelligence

Agentic work

Reasoning

Knowledge

Web design

General

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