GPT-5.1-Codex vs Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads GPT-5.1-Codex on coding, 62.4 to 15.3 on the Vibe Coding Index, and Qwen3.8 Max (0902) costs 1.7x less per output token ($6.00 vs $10.00 per million). Updated October 21, 2026; 16 benchmarks have a result for both.
vs boards won of 16 shared
GPT-5.1-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 15.3 for GPT-5.1-Codex.
Pick GPT-5.1-Codex for a lower cost per solved task ($0.892 against $1.32).
Model AGPT-5.1-CodexOpenAI15.3/1002 independent benchmarks$0.892 per solved taskModel BQwen3.8 Max (0902)Qwen62.4/1005 independent benchmarks$1.32 per solved task
- Shared results
- 16
- GPT-5.1-Codex only
- 10
- Qwen3.8 Max (0902) only
- 97
- 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 15.3.
2 and 5 benchmarks behind the scores
Intelligence
AA Intelligence Index
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 45.4 against 23.7.
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-5.1-Codex $0.0063, Qwen3.8 Max (0902) $0.0050 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5.1-Codex
GPT-5.1-Codex $0.892, Qwen3.8 Max (0902) $1.32.
Published attempt cost and pass rate
At a glance
- Coding index
- GPT-5.1-Codex 15.3 from 2 benchmarks, Qwen3.8 Max (0902) 62.4 from 5 benchmarks.
- Widest category
- Coding, 36.1 points apart on average over 4 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): GPT-5.1-Codex 13.1% against Qwen3.8 Max (0902) 64.7%, 51.6 points apart on a 0 to 100 scale.
- Output price
- GPT-5.1-Codex $10.00 against Qwen3.8 Max (0902) $6.00 per million output tokens, 1.7x apart.
- Context window
- GPT-5.1-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.
| Category | GPT-5.1-Codex | Bars | Qwen3.8 Max (0902) | Ties |
|---|---|---|---|---|
| Coding | 0 | 4 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 3 | ||
| Reasoning | 0 | 2 | ||
| Web design | 0 | 5 | ||
| General | 0 | 1 | ||
| All shared boards | 0 | 16 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
| Benchmark | GPT-5.1-Codex | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| SWE-bench Verified | 66.0% | Not measured | Only GPT-5.1-Codex measured |
| LiveBench coding | Not measured | 60.3% | Only Qwen3.8 Max (0902) measured |
| LMArena WebDev | 1337 | 1674 | Qwen3.8 Max (0902) |
| AA Coding Index | Not measured | 76.2 | Only Qwen3.8 Max (0902) measured |
| LiveCodeBench (Vals AI) | 85.5% | 87.8% | Qwen3.8 Max (0902) |
| SWE-bench Verified (Vals AI) | Not measured | 85.6% | Only Qwen3.8 Max (0902) measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 34.3% | Only Qwen3.8 Max (0902) measured |
| Vibe Code Bench (Vals AI) | 13.1% | 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) | Not measured | 68.9% | Only Qwen3.8 Max (0902) measured |
| 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 |
| LiveCodeBench (Artificial Analysis run) | 84.9% | Not measured | Only GPT-5.1-Codex measured |
| SciCode | Not measured | 53.2% | Only Qwen3.8 Max (0902) measured |
| Terminal-Bench Hard (Artificial Analysis run) | 34.8% | Not measured | Only GPT-5.1-Codex measured |
| Terminal-Bench | 57.8% | Not measured | Only GPT-5.1-Codex measured |
| FrontierSWE | Not measured | 17.8% | Only Qwen3.8 Max (0902) measured |
| ALE-Bench | 1244.9 | Not measured | Only GPT-5.1-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 | Not measured | 1692 | Only Qwen3.8 Max (0902) measured |
| LMArena WebDev: HTML | 1346 | 1649 | Qwen3.8 Max (0902) |
| LMArena WebDev: from an image | Not measured | 1637 | Only Qwen3.8 Max (0902) measured |
| Neither model has a result on the other 12 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-5.1-Codex | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| AA Intelligence Index | 23.7 | 45.4 | Qwen3.8 Max (0902) |
| Epoch Capabilities Index | Not measured | 157.4 | Only Qwen3.8 Max (0902) measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | GPT-5.1-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 | 1018 | 1300 | Qwen3.8 Max (0902) |
| Design Arena: agents building full-stack apps | 1086 | 1305 | Qwen3.8 Max (0902) |
| Design Arena: agents building mobile apps | 1191 | 1224 | Qwen3.8 Max (0902) |
| Design Arena: agents building Android apps | Not measured | 1202 | 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 | 1258 | Only Qwen3.8 Max (0902) measured |
| Design Arena: spreadsheets | Not measured | 937 | 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 | 83.0% | Not measured | Only GPT-5.1-Codex 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 index02
| Benchmark | GPT-5.1-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) | 86.0% | 92.8% | Qwen3.8 Max (0902) |
| Humanity's Last Exam (Artificial Analysis run) | 25.7% | 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 index8 boards, none shared
| Benchmark | GPT-5.1-Codex | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| AA Math Index | 95.7 | Not measured | Only GPT-5.1-Codex measured |
| AIME 2025 (Artificial Analysis run) | 95.7% | Not measured | Only GPT-5.1-Codex measured |
| 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 5 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index12 boards, none shared
| Benchmark | GPT-5.1-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 |
| MMLU Pro (Artificial Analysis run) | 86.0% | Not measured | Only GPT-5.1-Codex 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 6 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | GPT-5.1-Codex | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| Design Arena: websites | 1162 | 1275 | Qwen3.8 Max (0902) |
| Design Arena: UI components | 1237 | 1340 | Qwen3.8 Max (0902) |
| Design Arena: game dev | 1138 | 1313 | Qwen3.8 Max (0902) |
| Design Arena: data viz | 1213 | 1282 | Qwen3.8 Max (0902) |
| Design Arena: 3D | Not measured | 1337 | Only Qwen3.8 Max (0902) measured |
| Design Arena: all code categories | 1152 | 1293 | Qwen3.8 Max (0902) |
| 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 index01
| Benchmark | GPT-5.1-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 | 70.0% | Not measured | Only GPT-5.1-Codex measured |
| Long context reasoning (AA-LCR) | 69.3% | 80.3% | Qwen3.8 Max (0902) |
| 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
| Benchmark | GPT-5.1-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-5.1-Codex$0.0063Fits 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-5.1-Codex$0.092Fits in one request
Qwen3.8 Max (0902)$0.118Fits in one request
GPT-5.1-Codex costs less
Agent loop
200K input + 10K output tokens
GPT-5.1-Codex$0.350Fits in one request
Qwen3.8 Max (0902)$0.460Fits in one request
GPT-5.1-Codex 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.
| Specification | OpenAI GPT-5.1-Codex | Qwen3.8 Max (0902) |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | No public weights listed |
| Released | November 13, 2025 | September 3, 2026 |
| Context window | 400K | 1M |
| Input $/Mtok | $1.25 | $2.00 |
| Output $/Mtok | $10.00 | $6.00 |
| Coding index | 15.3 | 62.4 |
| Independent measurements behind it | 2 | 5 |
| Cost per solved task | $0.892 | $1.32 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.1-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 15.3 for GPT-5.1-Codex. Pick GPT-5.1-Codex for a lower cost per solved task ($0.892 against $1.32).
Which is cheaper, GPT-5.1-Codex or Qwen3.8 Max (0902)?
GPT-5.1-Codex costs $1.25 per million input tokens and $10.00 per million output tokens; Qwen3.8 Max (0902) costs $2.00 and $6.00. Per solved SWE-bench Verified task, GPT-5.1-Codex costs $0.892 and Qwen3.8 Max (0902) $1.32.
Which has the bigger context window, GPT-5.1-Codex or Qwen3.8 Max (0902)?
Qwen3.8 Max (0902), with 1M tokens against 400K.
How many benchmarks is the GPT-5.1-Codex vs Qwen3.8 Max (0902) comparison based on?
16 benchmarks have a result for both models as of October 21, 2026. GPT-5.1-Codex'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.