GPT-5.3-Codex vs Qwen3.8 Max (0902)

Qwen3.8 Max (0902) leads GPT-5.3-Codex on coding, 62.4 to 60.0 on the Vibe Coding Index, and Qwen3.8 Max (0902) costs 2.3x less per output token ($6.00 vs $14.00 per million). Updated October 22, 2026; 25 benchmarks have a result for both.

Replace GPT-5.3-Codex Replace Qwen3.8 Max (0902)

GPT-5.3-CodexOpenAI

Coding: 60.0
Overall: No data
Output $/M: $14.00

Qwen3.8 Max (0902)Qwen

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

vs boards won of 25 shared

GPT-5.3-Codex 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 60.0 for GPT-5.3-Codex.

Pick GPT-5.3-Codex for a lower cost per solved task ($0.59 against $1.32) and work measured by Long context reasoning (AA-LCR), where it leads.

Model AGPT-5.3-CodexOpenAI60.0/1003 independent benchmarks$0.59 per solved task Model BQwen3.8 Max (0902)Qwen62.4/1005 independent benchmarks$1.32 per solved task

25988

Shared results: 25
GPT-5.3-Codex only: 9
Qwen3.8 Max (0902) only: 88
Like-for-like categories: 6 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 60.0.
3 and 5 benchmarks behind the scores
Intelligence
AA Intelligence Index
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 45.4 against 32.5.
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 1162.
Same board, both measured
Long documents
Listed context window
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) takes 1M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.8 Max (0902)
GPT-5.3-Codex $0.0088, Qwen3.8 Max (0902) $0.0050 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5.3-Codex
GPT-5.3-Codex $0.59, Qwen3.8 Max (0902) $1.32.
Published attempt cost and pass rate

At a glance

Coding index: GPT-5.3-Codex 60.0 from 3 benchmarks, Qwen3.8 Max (0902) 62.4 from 5 benchmarks.
Widest category: Agentic work, 24.6 points apart on average over 5 shared benchmarks.
Widest single gap: Design Arena: 3D: GPT-5.3-Codex 1007 against Qwen3.8 Max (0902) 1336, 41.1 points apart on a 0 to 100 scale.
Output price: GPT-5.3-Codex $14.00 against Qwen3.8 Max (0902) $6.00 per million output tokens, 2.3x apart.
Context window: GPT-5.3-Codex takes 400K 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-5.3-Codex	Qwen3.8 Max (0902)
Coding	0	8
Intelligence	0	2
Agentic work	0	5
Reasoning	0	2
Web design	0	7
General	1	0
All shared boards	1	24

CodingLike-for-likeSix of these boards feed the Vibe Coding Index08

Coding benchmarks for GPT-5.3-Codex and Qwen3.8 Max (0902)
Benchmark	GPT-5.3-Codex	Qwen3.8 Max (0902)	Leader
LiveBench coding	Not measured	60.3%	Only Qwen3.8 Max (0902) measured
LMArena WebDev	1407	1674	Qwen3.8 Max (0902)
AA Coding Index	Not measured	76.2	Only Qwen3.8 Max (0902) measured
LiveCodeBench (Vals AI)	87.3%	87.8%	Qwen3.8 Max (0902)
SWE-bench Verified (Vals AI)	78.0%	85.6%	Qwen3.8 Max (0902)
Terminal-Bench 4 (Vals AI)	Not measured	34.3%	Only Qwen3.8 Max (0902) measured
Vibe Code Bench (Vals AI)	61.8%	64.7%	Qwen3.8 Max (0902)
Code migration (Vals AI)	Not measured	24.0%	Only Qwen3.8 Max (0902) measured
ProgramBench (Vals AI)	Not measured	0.0%	Only Qwen3.8 Max (0902) measured
IOI (Vals AI)	53.8%	68.9%	Qwen3.8 Max (0902)
Terminal-Bench 2.1 (Vals AI)	Not measured	67.4%	Only Qwen3.8 Max (0902) measured
SWE-bench Multimodal (Vals AI)	Not measured	42.5%	Only Qwen3.8 Max (0902) measured
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)	Not measured	28.6%	Only Qwen3.8 Max (0902) measured
SciCode	Not measured	53.2%	Only Qwen3.8 Max (0902) measured
Terminal-Bench Hard (Artificial Analysis run)	53.0%	Not measured	Only GPT-5.3-Codex measured
Terminal-Bench	78.4%	Not measured	Only GPT-5.3-Codex measured
FrontierSWE	Not measured	17.8%	Only Qwen3.8 Max (0902) measured
WeirdML	79.3%	Not measured	Only GPT-5.3-Codex measured
ALE-Bench	1655.2	Not measured	Only GPT-5.3-Codex measured
LMArena Text: coding	Not measured	1502	Only Qwen3.8 Max (0902) measured
LMArena Text: software and IT	Not measured	1498	Only Qwen3.8 Max (0902) measured
LMArena WebDev: React	1395	1692	Qwen3.8 Max (0902)
LMArena WebDev: HTML	1422	1649	Qwen3.8 Max (0902)
LMArena WebDev: from an image	1443	1637	Qwen3.8 Max (0902)
Neither model has a result on the other 13 boards here

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

Intelligence benchmarks for GPT-5.3-Codex and Qwen3.8 Max (0902)
Benchmark	GPT-5.3-Codex	Qwen3.8 Max (0902)	Leader
AA Intelligence Index	32.5	45.4	Qwen3.8 Max (0902)
Epoch Capabilities Index	157.1	157.4	Qwen3.8 Max (0902)

Agentic workLike-for-likePublic boards, shown as published, kept out of the index05

Agentic work benchmarks for GPT-5.3-Codex and Qwen3.8 Max (0902)
Benchmark	GPT-5.3-Codex	Qwen3.8 Max (0902)	Leader
AA Agentic Index	Not measured	56.0	Only Qwen3.8 Max (0902) measured
Design Arena: agents building web apps	1032	1300	Qwen3.8 Max (0902)
Design Arena: agents building full-stack apps	983	1305	Qwen3.8 Max (0902)
Design Arena: agents building mobile apps	1053	1224	Qwen3.8 Max (0902)
Design Arena: agents building Android apps	1084	1196	Qwen3.8 Max (0902)
Design Arena: agents building Godot games	1124	1234	Qwen3.8 Max (0902)
Design Arena: PowerPoint slides	Not measured	1233	Only Qwen3.8 Max (0902) measured
Design Arena: spreadsheets	Not measured	967	Only Qwen3.8 Max (0902) measured
LiveBench research	Not measured	69.9%	Only Qwen3.8 Max (0902) measured
LiveBench browser	Not measured	32.0%	Only Qwen3.8 Max (0902) measured
LiveBench long agentic	Not measured	42.3%	Only Qwen3.8 Max (0902) measured
Finance Agent (Vals AI)	Not measured	50.6%	Only Qwen3.8 Max (0902) measured
Tax Agent Bench (Vals AI)	Not measured	32.1%	Only Qwen3.8 Max (0902) measured
Legal Research Bench (Vals AI)	Not measured	47.6%	Only Qwen3.8 Max (0902) measured
Excel Modeling Benchmark (Vals AI)	Not measured	60.1%	Only Qwen3.8 Max (0902) measured
Terminal-Bench-Science (Vals AI)	Not measured	12.9%	Only Qwen3.8 Max (0902) measured
τ²-Bench Telecom	86.0%	Not measured	Only GPT-5.3-Codex measured
APEX-Agents	Not measured	63.3%	Only Qwen3.8 Max (0902) measured
METR time horizons	74.5%	Not measured	Only GPT-5.3-Codex measured
Vending-Bench 2	$5,940	Not measured	Only GPT-5.3-Codex measured
LMArena Search	Not measured	1261	Only Qwen3.8 Max (0902) measured
Neither model has a result on the other 20 boards here

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

Reasoning benchmarks for GPT-5.3-Codex and Qwen3.8 Max (0902)
Benchmark	GPT-5.3-Codex	Qwen3.8 Max (0902)	Leader
GPQA Diamond	Not measured	93.7%	Only Qwen3.8 Max (0902) measured
LiveBench reasoning	Not measured	88.2%	Only Qwen3.8 Max (0902) measured
MysteryMechanism (Vals AI)	Not measured	23.9%	Only Qwen3.8 Max (0902) measured
MMMU Pro (Vals AI)	Not measured	88.0%	Only Qwen3.8 Max (0902) measured
GPQA Diamond (Artificial Analysis run)	91.5%	92.8%	Qwen3.8 Max (0902)
Humanity's Last Exam (Artificial Analysis run)	42.5%	43.1%	Qwen3.8 Max (0902)
GPQA Diamond (Epoch AI run)	Not measured	92.7%	Only Qwen3.8 Max (0902) measured
Chess puzzles	Not measured	46.0%	Only Qwen3.8 Max (0902) measured
Mystery game puzzles	Not measured	38.0%	Only Qwen3.8 Max (0902) measured
LMArena Text: hard prompts	Not measured	1496	Only Qwen3.8 Max (0902) measured
LMArena Text: expert	Not measured	1507	Only Qwen3.8 Max (0902) measured
LMArena Text: hard prompts in English	Not measured	1503	Only Qwen3.8 Max (0902) measured
LMArena Vision: diagrams	Not measured	1323	Only Qwen3.8 Max (0902) measured
LMArena Vision: homework	Not measured	1336	Only Qwen3.8 Max (0902) measured
Neither model has a result on the other 8 boards here

MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared

Math benchmarks for GPT-5.3-Codex and Qwen3.8 Max (0902)
Benchmark	GPT-5.3-Codex	Qwen3.8 Max (0902)	Leader
OTIS Mock AIME	Not measured	100.0%	Only Qwen3.8 Max (0902) measured
FrontierMath, tiers 1 to 3	Not measured	74.7%	Only Qwen3.8 Max (0902) measured
FrontierMath, tier 4	Not measured	46.3%	Only Qwen3.8 Max (0902) measured
ProofBench	Not measured	58.0%	Only Qwen3.8 Max (0902) measured
LMArena Text: math	Not measured	1501	Only Qwen3.8 Max (0902) measured
LMArena Text: mathematical work	Not measured	1491	Only Qwen3.8 Max (0902) measured
Neither model has a result on the other 7 boards here

KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared

Knowledge benchmarks for GPT-5.3-Codex and Qwen3.8 Max (0902)
Benchmark	GPT-5.3-Codex	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)	Not measured	40.7%	Only Qwen3.8 Max (0902) measured
SimpleQA Verified	Not measured	47.3%	Only Qwen3.8 Max (0902) measured
LMArena Text: science	Not measured	1512	Only Qwen3.8 Max (0902) measured
LMArena Text: medicine and healthcare	Not measured	1503	Only Qwen3.8 Max (0902) measured
LMArena Text: legal and government	Not measured	1492	Only Qwen3.8 Max (0902) measured
LMArena Text: business and finance	Not measured	1480	Only Qwen3.8 Max (0902) measured
Neither model has a result on the other 7 boards here

Web designLike-for-likePublic boards, shown as published, kept out of the index07

Web design benchmarks for GPT-5.3-Codex and Qwen3.8 Max (0902)
Benchmark	GPT-5.3-Codex	Qwen3.8 Max (0902)	Leader
Design Arena: websites	1162	1275	Qwen3.8 Max (0902)
Design Arena: UI components	1134	1338	Qwen3.8 Max (0902)
Design Arena: game dev	1161	1312	Qwen3.8 Max (0902)
Design Arena: data viz	1164	1281	Qwen3.8 Max (0902)
Design Arena: 3D	1007	1336	Qwen3.8 Max (0902)
Design Arena: all code categories	1144	1293	Qwen3.8 Max (0902)
Design Arena: SVG	1129	Not measured	Only GPT-5.3-Codex measured
Design Arena: ASCII art	1152	1200	Qwen3.8 Max (0902)
LiveBench design	Not measured	78.6%	Only Qwen3.8 Max (0902) measured

GeneralLike-for-likePublic boards, shown as published, kept out of the index10

General benchmarks for GPT-5.3-Codex and Qwen3.8 Max (0902)
Benchmark	GPT-5.3-Codex	Qwen3.8 Max (0902)	Leader
LiveBench data analysis	Not measured	78.4%	Only Qwen3.8 Max (0902) measured
Public Benefits Bench (Vals AI)	Not measured	67.1%	Only Qwen3.8 Max (0902) measured
SAGE (Vals AI)	Not measured	51.2%	Only Qwen3.8 Max (0902) measured
MedScribe (Vals AI)	Not measured	85.0%	Only Qwen3.8 Max (0902) measured
Vals Multimodal Index	Not measured	65.4%	Only Qwen3.8 Max (0902) measured
IFBench	75.4%	Not measured	Only GPT-5.3-Codex measured
Long context reasoning (AA-LCR)	83.3%	80.3%	GPT-5.3-Codex
DTBench	Not measured	92.0%	Only Qwen3.8 Max (0902) measured
LMCA	Not measured	46.2%	Only Qwen3.8 Max (0902) measured
Furniture assembly	Not measured	20.0%	Only Qwen3.8 Max (0902) measured
LMArena Text	Not measured	1483	Only Qwen3.8 Max (0902) measured
LMArena Text: instruction following	Not measured	1481	Only Qwen3.8 Max (0902) measured
LMArena Text: creative writing	Not measured	1477	Only Qwen3.8 Max (0902) measured
LMArena Vision	Not measured	1313	Only Qwen3.8 Max (0902) measured
LMArena Text: English	Not measured	1491	Only Qwen3.8 Max (0902) measured
LMArena Text: non-English	Not measured	1473	Only Qwen3.8 Max (0902) measured
LMArena Text: Chinese	Not measured	1539	Only Qwen3.8 Max (0902) measured
LMArena Text: German	Not measured	1478	Only Qwen3.8 Max (0902) measured
LMArena Text: Japanese	Not measured	1473	Only Qwen3.8 Max (0902) measured
LMArena Text: Korean	Not measured	1460	Only Qwen3.8 Max (0902) measured
LMArena Text: French	Not measured	1498	Only Qwen3.8 Max (0902) measured
LMArena Text: Spanish	Not measured	1489	Only Qwen3.8 Max (0902) measured
LMArena Text: Russian	Not measured	1482	Only Qwen3.8 Max (0902) measured
LMArena Text: Polish	Not measured	1471	Only Qwen3.8 Max (0902) measured
LMArena Text: multi-turn	Not measured	1489	Only Qwen3.8 Max (0902) measured
LMArena Text: longer queries	Not measured	1488	Only Qwen3.8 Max (0902) measured
LMArena Text: writing and language	Not measured	1477	Only Qwen3.8 Max (0902) measured
LMArena Text: entertainment and media	Not measured	1464	Only Qwen3.8 Max (0902) measured
LMArena Vision: English	Not measured	1311	Only Qwen3.8 Max (0902) measured
LMArena Vision: Chinese	Not measured	1358	Only Qwen3.8 Max (0902) measured
LMArena Vision: reading text	Not measured	1322	Only Qwen3.8 Max (0902) measured
LMArena Vision: humor	Not measured	1304	Only Qwen3.8 Max (0902) measured
LMArena Vision: creative writing	Not measured	1312	Only Qwen3.8 Max (0902) 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-5.3-Codex and Qwen3.8 Max (0902)
Benchmark	GPT-5.3-Codex	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-5.3-Codex$0.0088Fits in one request

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

Qwen3.8 Max (0902) costs less

Repository review

50K input + 3K output tokens

GPT-5.3-Codex$0.130Fits in one request

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

Qwen3.8 Max (0902) costs less

Agent loop

200K input + 10K output tokens

GPT-5.3-Codex$0.490Fits in one request

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

Qwen3.8 Max (0902) 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-5.3-Codex and Qwen3.8 Max (0902)
Specification	OpenAI GPT-5.3-Codex	Qwen3.8 Max (0902)
Maker	OpenAI	Qwen
Weights	No public weights listed	No public weights listed
Released	February 24, 2026	September 3, 2026
Context window	400K	1M
Input $/Mtok	$1.75	$2.00
Output $/Mtok	$14.00	$6.00
Coding index	60.0	62.4
Independent measurements behind it	3	5
Cost per solved task	$0.59	$1.32
Source	OpenRouter listing	OpenRouter listing

Questions

Is GPT-5.3-Codex 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 60.0 for GPT-5.3-Codex. Pick GPT-5.3-Codex for a lower cost per solved task ($0.59 against $1.32) and work measured by Long context reasoning (AA-LCR), where it leads.

Which is cheaper, GPT-5.3-Codex or Qwen3.8 Max (0902)?

GPT-5.3-Codex costs $1.75 per million input tokens and $14.00 per million output tokens; Qwen3.8 Max (0902) costs $2.00 and $6.00. Per solved SWE-bench Verified task, GPT-5.3-Codex costs $0.59 and Qwen3.8 Max (0902) $1.32.

Which has the bigger context window, GPT-5.3-Codex or Qwen3.8 Max (0902)?

Qwen3.8 Max (0902), with 1M tokens against 400K.

How many benchmarks is the GPT-5.3-Codex vs Qwen3.8 Max (0902) comparison based on?

25 benchmarks have a result for both models as of October 22, 2026. GPT-5.3-Codex's coding index rests on 3 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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<p style="font:13px/1.4 sans-serif;margin:6px 0 0">Source: <a href="https://llmbenchmark.ai/compare/gpt-5-3-codex-vs-qwen3-8-max-0902">LLMBenchmark.ai, GPT-5.3-Codex vs Qwen3.8 Max (0902)</a></p>

GPT-5.3-Codex 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