GPT-5.4 Nano vs Qwen3.8 Max (0902)

Qwen3.8 Max (0902) leads GPT-5.4 Nano on coding, 67.1 to 44.8 on the Vibe Coding Index, and GPT-5.4 Nano costs 4.8x less per output token ($1.25 vs $6.00 per million). Updated October 23, 2026; 74 benchmarks have a result for both.

Replace GPT-5.4 Nano Replace Qwen3.8 Max (0902)

GPT-5.4 NanoOpenAI

Coding: 44.8
Overall: 45.8
Output $/M: $1.25

Qwen3.8 Max (0902)Qwen

Coding: 67.1
Overall: 70.1
Output $/M: $6.00

vs boards won of 74 shared

GPT-5.4 Nano or Qwen3.8 Max (0902): which to pick

Pick Qwen3.8 Max (0902) for coding: it scores 67.1 on the Vibe Coding Index against 44.8 for GPT-5.4 Nano.

Pick GPT-5.4 Nano for output tokens 4.8x cheaper ($1.25 vs $6.00 per million) and a lower cost per solved task ($0.14 against $1.32).

Model AGPT-5.4 NanoOpenAI44.8/1002 independent benchmarks$0.14 per solved task Model BQwen3.8 Max (0902)Qwen67.1/1005 independent benchmarks$1.32 per solved task

741142

Shared results: 74
GPT-5.4 Nano only: 11
Qwen3.8 Max (0902) only: 42
Like-for-like categories: 7 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 67.1 against 44.8.
2 and 5 benchmarks behind the scores
Intelligence
AA Intelligence Index
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 45.4 against 20.7.
Same board, both measured
Agentic work
AA Agentic Index
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 56.0 against 16.0.
Same board, both measured
Reasoning
GPQA Diamond
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 93.7% against 77.5%.
Same board, both measured
Math
AIME
No call
Only GPT-5.4 Nano has a AIME result, 88.8%.
Directional only
Knowledge
MMLU Pro
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 88.6% against 77.2%.
Same board, both measured
Web design
Design Arena: websites
No call
Only Qwen3.8 Max (0902) has a Design Arena: websites result, 1276.
Directional only
General
Vals Index
No call
Only Qwen3.8 Max (0902) has a Vals Index result, 59.1%.
Directional only
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
GPT-5.4 Nano
GPT-5.4 Nano $0.0008, Qwen3.8 Max (0902) $0.0050 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5.4 Nano
GPT-5.4 Nano $0.14, Qwen3.8 Max (0902) $1.32.
Published attempt cost and pass rate

At a glance

Coding index: GPT-5.4 Nano 44.8 from 2 benchmarks, Qwen3.8 Max (0902) 67.1 from 5 benchmarks.
Widest category: Agentic work, 34.6 points apart on average over 5 shared benchmarks.
Widest single gap: ProofBench: GPT-5.4 Nano 5.0% against Qwen3.8 Max (0902) 58.0%, 53.0 points apart on a 0 to 100 scale.
Output price: GPT-5.4 Nano $1.25 against Qwen3.8 Max (0902) $6.00 per million output tokens, 4.8x apart.
Context window: GPT-5.4 Nano 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-5.4 Nano	Qwen3.8 Max (0902)
Coding	0	9
Intelligence	0	2
Agentic work	0	5
Reasoning	0	12
Math	0	6
Knowledge	1	10
General	0	29
All shared boards	1	73

CodingLike-for-likeSix of these boards feed the Vibe Coding Index09

Coding benchmarks for GPT-5.4 Nano and Qwen3.8 Max (0902)
Benchmark	GPT-5.4 Nano	Qwen3.8 Max (0902)	Leader
LiveBench coding	Not measured	60.3%	Only Qwen3.8 Max (0902) measured
LMArena WebDev	Not measured	1673	Only Qwen3.8 Max (0902) measured
AA Coding Index	56.1	76.2	Qwen3.8 Max (0902)
LiveCodeBench (Vals AI)	84.0%	87.8%	Qwen3.8 Max (0902)
SWE-bench Verified (Vals AI)	69.8%	85.6%	Qwen3.8 Max (0902)
Terminal-Bench 4 (Vals AI)	Not measured	38.9%	Only Qwen3.8 Max (0902) measured
Vibe Code Bench (Vals AI)	26.1%	77.1%	Qwen3.8 Max (0902)
Code migration (Vals AI)	14.5%	24.0%	Qwen3.8 Max (0902)
ProgramBench (Vals AI)	Not measured	0.0%	Only Qwen3.8 Max (0902) measured
IOI (Vals AI)	Not measured	68.9%	Only Qwen3.8 Max (0902) measured
Terminal-Bench 2.1 (Vals AI)	41.6%	67.4%	Qwen3.8 Max (0902)
SWE-bench Multimodal (Vals AI)	Not measured	42.5%	Only Qwen3.8 Max (0902) measured
Vibe Code Bench 1-100 (Vals AI)	Not measured	24.8%	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	47.2%	53.2%	Qwen3.8 Max (0902)
Terminal-Bench Hard (Artificial Analysis run)	42.4%	Not measured	Only GPT-5.4 Nano measured
FrontierSWE	Not measured	17.8%	Only Qwen3.8 Max (0902) measured
WeirdML	49.2%	Not measured	Only GPT-5.4 Nano measured
ALE-Bench	1004.5	Not measured	Only GPT-5.4 Nano measured
LMArena Text: coding	1405	1503	Qwen3.8 Max (0902)
LMArena Text: software and IT	1403	1498	Qwen3.8 Max (0902)
LMArena WebDev: React	Not measured	1690	Only Qwen3.8 Max (0902) measured
LMArena WebDev: HTML	Not measured	1652	Only Qwen3.8 Max (0902) measured
LMArena WebDev: from an image	Not measured	1637	Only Qwen3.8 Max (0902) measured
Neither model has a result on the other 18 boards here

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

Intelligence benchmarks for GPT-5.4 Nano and Qwen3.8 Max (0902)
Benchmark	GPT-5.4 Nano	Qwen3.8 Max (0902)	Leader
AA Intelligence Index	20.7	45.4	Qwen3.8 Max (0902)
Epoch Capabilities Index	146.0	157.4	Qwen3.8 Max (0902)

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

Agentic work benchmarks for GPT-5.4 Nano and Qwen3.8 Max (0902)
Benchmark	GPT-5.4 Nano	Qwen3.8 Max (0902)	Leader
AA Agentic Index	16.0	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	1194	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	1226	Only Qwen3.8 Max (0902) measured
Design Arena: spreadsheets	Not measured	995	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)	38.2%	57.9%	Qwen3.8 Max (0902)
Tax Agent Bench (Vals AI)	5.0%	49.7%	Qwen3.8 Max (0902)
Legal Research Bench (Vals AI)	6.3%	47.6%	Qwen3.8 Max (0902)
Harvey's Legal Agent Benchmark (Vals AI)	Not measured	15.4%	Only Qwen3.8 Max (0902) measured
Excel Modeling Benchmark (Vals AI)	44.8%	72.0%	Qwen3.8 Max (0902)
Terminal-Bench-Science (Vals AI)	Not measured	12.9%	Only Qwen3.8 Max (0902) measured
τ²-Bench Telecom	76.0%	Not measured	Only GPT-5.4 Nano measured
APEX-Agents	Not measured	63.3%	Only Qwen3.8 Max (0902) measured
LMArena Search	Not measured	1261	Only Qwen3.8 Max (0902) measured
Neither model has a result on the other 22 boards here

ReasoningLike-for-likePublic boards, shown as published, kept out of the index012

Reasoning benchmarks for GPT-5.4 Nano and Qwen3.8 Max (0902)
Benchmark	GPT-5.4 Nano	Qwen3.8 Max (0902)	Leader
GPQA Diamond	77.5%	93.7%	Qwen3.8 Max (0902)
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)	73.6%	88.0%	Qwen3.8 Max (0902)
GPQA Diamond (Artificial Analysis run)	81.7%	92.8%	Qwen3.8 Max (0902)
Humanity's Last Exam (Artificial Analysis run)	28.3%	43.1%	Qwen3.8 Max (0902)
GPQA Diamond (Epoch AI run)	78.5%	92.7%	Qwen3.8 Max (0902)
ARC-AGI	51.5%	Not measured	Only GPT-5.4 Nano measured
ARC-AGI-2	5.7%	Not measured	Only GPT-5.4 Nano measured
Chess puzzles	45.0%	46.0%	Qwen3.8 Max (0902)
Mystery game puzzles	9.0%	38.0%	Qwen3.8 Max (0902)
LMArena Text: hard prompts	1381	1497	Qwen3.8 Max (0902)
LMArena Text: expert	1396	1507	Qwen3.8 Max (0902)
LMArena Text: hard prompts in English	1387	1504	Qwen3.8 Max (0902)
LMArena Vision: diagrams	1210	1323	Qwen3.8 Max (0902)
LMArena Vision: homework	1225	1336	Qwen3.8 Max (0902)
Kagi LLM Benchmark	39.7%	Not measured	Only GPT-5.4 Nano measured
Neither model has a result on the other 6 boards here

MathLike-for-likePublic boards, shown as published, kept out of the index06

Math benchmarks for GPT-5.4 Nano and Qwen3.8 Max (0902)
Benchmark	GPT-5.4 Nano	Qwen3.8 Max (0902)	Leader
AIME	88.8%	Not measured	Only GPT-5.4 Nano measured
OTIS Mock AIME	87.8%	100.0%	Qwen3.8 Max (0902)
FrontierMath, tiers 1 to 3	44.9%	74.7%	Qwen3.8 Max (0902)
FrontierMath, tier 4	12.2%	46.3%	Qwen3.8 Max (0902)
ProofBench	5.0%	58.0%	Qwen3.8 Max (0902)
LMArena Text: math	1406	1501	Qwen3.8 Max (0902)
LMArena Text: mathematical work	1409	1491	Qwen3.8 Max (0902)
MathArena	Not measured	51.6%	Only Qwen3.8 Max (0902) measured
Neither model has a result on the other 6 boards here

KnowledgeLike-for-likePublic boards, shown as published, kept out of the index110

Knowledge benchmarks for GPT-5.4 Nano and Qwen3.8 Max (0902)
Benchmark	GPT-5.4 Nano	Qwen3.8 Max (0902)	Leader
MMLU Pro	77.2%	88.6%	Qwen3.8 Max (0902)
LegalBench (Vals AI)	77.9%	83.6%	Qwen3.8 Max (0902)
CaseLaw (Vals AI)	51.9%	Not measured	Only GPT-5.4 Nano measured
CorpFin (Vals AI)	61.2%	65.8%	Qwen3.8 Max (0902)
TaxEval (Vals AI)	67.4%	75.5%	Qwen3.8 Max (0902)
MortgageTax (Vals AI)	59.1%	64.0%	Qwen3.8 Max (0902)
MedCode (Vals AI)	41.0%	40.7%	GPT-5.4 Nano
SimpleQA Verified	11.7%	47.3%	Qwen3.8 Max (0902)
LMArena Text: science	1374	1512	Qwen3.8 Max (0902)
LMArena Text: medicine and healthcare	1369	1503	Qwen3.8 Max (0902)
LMArena Text: legal and government	1368	1494	Qwen3.8 Max (0902)
LMArena Text: business and finance	1366	1481	Qwen3.8 Max (0902)
Neither model has a result on the other 8 boards here

Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared

Web design benchmarks for GPT-5.4 Nano and Qwen3.8 Max (0902)
Benchmark	GPT-5.4 Nano	Qwen3.8 Max (0902)	Leader
Design Arena: websites	Not measured	1276	Only Qwen3.8 Max (0902) measured
Design Arena: UI components	Not measured	1336	Only Qwen3.8 Max (0902) measured
Design Arena: game dev	Not measured	1313	Only Qwen3.8 Max (0902) measured
Design Arena: data viz	Not measured	1282	Only Qwen3.8 Max (0902) measured
Design Arena: 3D	Not measured	1338	Only Qwen3.8 Max (0902) measured
Design Arena: all code categories	Not measured	1293	Only Qwen3.8 Max (0902) measured
Design Arena: ASCII art	Not measured	1200	Only Qwen3.8 Max (0902) measured
LiveBench design	Not measured	78.6%	Only Qwen3.8 Max (0902) measured
Neither model has a result on the other 1 board here

GeneralLike-for-likePublic boards, shown as published, kept out of the index029

General benchmarks for GPT-5.4 Nano and Qwen3.8 Max (0902)
Benchmark	GPT-5.4 Nano	Qwen3.8 Max (0902)	Leader
Vals Index	Not measured	59.1%	Only Qwen3.8 Max (0902) measured
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)	38.1%	51.2%	Qwen3.8 Max (0902)
MedScribe (Vals AI)	77.1%	85.0%	Qwen3.8 Max (0902)
Vals Multimodal Index	47.6%	65.4%	Qwen3.8 Max (0902)
IFBench	75.9%	Not measured	Only GPT-5.4 Nano measured
Long context reasoning (AA-LCR)	76.7%	80.3%	Qwen3.8 Max (0902)
DTBench	80.3%	92.0%	Qwen3.8 Max (0902)
LMCA	36.9%	46.2%	Qwen3.8 Max (0902)
Furniture assembly	Not measured	20.0%	Only Qwen3.8 Max (0902) measured
LMArena Text	1372	1484	Qwen3.8 Max (0902)
LMArena Text: instruction following	1362	1481	Qwen3.8 Max (0902)
LMArena Text: creative writing	1314	1477	Qwen3.8 Max (0902)
LMArena Vision	1196	1313	Qwen3.8 Max (0902)
LMArena Text: English	1381	1492	Qwen3.8 Max (0902)
LMArena Text: non-English	1360	1473	Qwen3.8 Max (0902)
LMArena Text: Chinese	1391	1539	Qwen3.8 Max (0902)
LMArena Text: German	1368	1476	Qwen3.8 Max (0902)
LMArena Text: Japanese	1343	1473	Qwen3.8 Max (0902)
LMArena Text: Korean	1320	1462	Qwen3.8 Max (0902)
LMArena Text: French	1395	1502	Qwen3.8 Max (0902)
LMArena Text: Spanish	1371	1487	Qwen3.8 Max (0902)
LMArena Text: Russian	1362	1482	Qwen3.8 Max (0902)
LMArena Text: Polish	1366	1471	Qwen3.8 Max (0902)
LMArena Text: multi-turn	1382	1490	Qwen3.8 Max (0902)
LMArena Text: longer queries	1366	1489	Qwen3.8 Max (0902)
LMArena Text: writing and language	1338	1477	Qwen3.8 Max (0902)
LMArena Text: entertainment and media	1324	1464	Qwen3.8 Max (0902)
LMArena Vision: English	1202	1311	Qwen3.8 Max (0902)
LMArena Vision: Chinese	1232	1358	Qwen3.8 Max (0902)
LMArena Vision: reading text	1204	1322	Qwen3.8 Max (0902)
LMArena Vision: humor	1161	1304	Qwen3.8 Max (0902)
LMArena Vision: recognizing things	1141	Not measured	Only GPT-5.4 Nano measured
LMArena Vision: creative writing	1159	1312	Qwen3.8 Max (0902)
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-5.4 Nano and Qwen3.8 Max (0902)
Benchmark	GPT-5.4 Nano	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-5.4 Nano$0.0008Fits in one request

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

GPT-5.4 Nano costs less

Repository review

50K input + 3K output tokens

GPT-5.4 Nano$0.014Fits in one request

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

GPT-5.4 Nano costs less

Agent loop

200K input + 10K output tokens

GPT-5.4 Nano$0.052Fits in one request

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

GPT-5.4 Nano 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-5.4 Nano and Qwen3.8 Max (0902)
Specification	OpenAI GPT-5.4 Nano	Qwen3.8 Max (0902)
Maker	OpenAI	Qwen
Weights	No public weights listed	No public weights listed
Released	March 17, 2026	September 3, 2026
Context window	400K	1M
Input $/Mtok	$0.20	$2.00
Output $/Mtok	$1.25	$6.00
Coding index	44.8	67.1
Independent measurements behind it	2	5
Cost per solved task	$0.14	$1.32
Source	OpenRouter listing	OpenRouter listing

Questions

Is GPT-5.4 Nano better than Qwen3.8 Max (0902) for coding?

Pick Qwen3.8 Max (0902) for coding: it scores 67.1 on the Vibe Coding Index against 44.8 for GPT-5.4 Nano. Pick GPT-5.4 Nano for output tokens 4.8x cheaper ($1.25 vs $6.00 per million) and a lower cost per solved task ($0.14 against $1.32).

Which is cheaper, GPT-5.4 Nano or Qwen3.8 Max (0902)?

GPT-5.4 Nano costs $0.20 per million input tokens and $1.25 per million output tokens; Qwen3.8 Max (0902) costs $2.00 and $6.00. Per solved SWE-bench Verified task, GPT-5.4 Nano costs $0.14 and Qwen3.8 Max (0902) $1.32.

Which has the bigger context window, GPT-5.4 Nano or Qwen3.8 Max (0902)?

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

How many benchmarks is the GPT-5.4 Nano vs Qwen3.8 Max (0902) comparison based on?

74 benchmarks have a result for both models as of October 23, 2026. GPT-5.4 Nano's coding index rests on 2 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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GPT-5.4 Nano or Qwen3.8 Max (0902): which to pick

Which one for your work

Coding

Intelligence

Agentic work

Reasoning

Math

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