GPT-5.1-Codex-Max vs Qwen3.5 397B A17B
Qwen3.5 397B A17B costs 4.3x less per output token than GPT-5.1-Codex-Max ($2.34 vs $10.00 per million). Updated October 22, 2026; 1 benchmarks have a result for both.
GPT-5.1-Codex-Max or Qwen3.5 397B A17B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-5.1-Codex-Max for a larger context window (400K against 262K tokens).
- Shared results
- 1
- GPT-5.1-Codex-Max only
- 5
- Qwen3.5 397B A17B only
- 64
- Like-for-like categories
- 1 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
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
No call
Only Qwen3.5 397B A17B has a AA Intelligence Index result, 21.4.
Directional only
Web design
Design Arena: websites
No call
Only Qwen3.5 397B A17B has a Design Arena: websites result, 1190.
Directional only
Long documents
Listed context window
GPT-5.1-Codex-Max
GPT-5.1-Codex-Max takes 400K tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.5 397B A17B
GPT-5.1-Codex-Max $0.0063, Qwen3.5 397B A17B $0.0016 per turn.
List prices
At a glance
- Coding index
- GPT-5.1-Codex-Max not benchmarked, Qwen3.5 397B A17B not benchmarked.
- Widest category
- Coding, 20.2 points apart on average over 1 shared benchmark.
- Widest single gap
- ALE-Bench: GPT-5.1-Codex-Max 1228.3 against Qwen3.5 397B A17B 621.9, 20.2 points apart on a 0 to 100 scale.
- Output price
- GPT-5.1-Codex-Max $10.00 against Qwen3.5 397B A17B $2.34 per million output tokens, 4.3x apart.
- Context window
- GPT-5.1-Codex-Max takes 400K tokens, Qwen3.5 397B A17B 262K.
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-Max | Bars | Qwen3.5 397B A17B | Ties |
|---|---|---|---|---|
| Coding | 1 | 0 | ||
| All shared boards | 1 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
| Benchmark | GPT-5.1-Codex-Max | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1400 | Only Qwen3.5 397B A17B measured |
| LiveCodeBench (Vals AI) | 83.6% | Not measured | Only GPT-5.1-Codex-Max measured |
| Vibe Code Bench (Vals AI) | 22.2% | Not measured | Only GPT-5.1-Codex-Max measured |
| SciCode | Not measured | 44.8% | Only Qwen3.5 397B A17B measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 40.9% | Only Qwen3.5 397B A17B measured |
| Terminal-Bench | 60.4% | Not measured | Only GPT-5.1-Codex-Max measured |
| ALE-Bench | 1228.3 | 621.9 | GPT-5.1-Codex-Max |
| LMArena Text: coding | Not measured | 1464 | Only Qwen3.5 397B A17B measured |
| LMArena Text: software and IT | Not measured | 1463 | Only Qwen3.5 397B A17B measured |
| LMArena WebDev: React | Not measured | 1388 | Only Qwen3.5 397B A17B measured |
| LMArena WebDev: HTML | Not measured | 1410 | Only Qwen3.5 397B A17B measured |
| Neither model has a result on the other 28 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 21.4 | Only Qwen3.5 397B A17B measured |
| Epoch Capabilities Index | Not measured | 146.8 | Only Qwen3.5 397B A17B measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 95.6% | Only Qwen3.5 397B A17B measured |
| APEX-Agents | Not measured | 24.9% | Only Qwen3.5 397B A17B measured |
| METR time horizons | 70.8% | Not measured | Only GPT-5.1-Codex-Max measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 89.3% | Only Qwen3.5 397B A17B measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 29.0% | Only Qwen3.5 397B A17B measured |
| GPQA Diamond (Epoch AI run) | Not measured | 86.4% | Only Qwen3.5 397B A17B measured |
| Mystery game puzzles | Not measured | 18.0% | Only Qwen3.5 397B A17B measured |
| LMArena Text: hard prompts | Not measured | 1448 | Only Qwen3.5 397B A17B measured |
| LMArena Text: expert | Not measured | 1462 | Only Qwen3.5 397B A17B measured |
| LMArena Text: hard prompts in English | Not measured | 1450 | Only Qwen3.5 397B A17B measured |
| LMArena Vision: diagrams | Not measured | 1277 | Only Qwen3.5 397B A17B measured |
| LMArena Vision: homework | Not measured | 1292 | Only Qwen3.5 397B A17B measured |
| Neither model has a result on the other 13 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 88.9% | Only Qwen3.5 397B A17B measured |
| FrontierMath, tiers 1 to 3 | Not measured | 31.2% | Only Qwen3.5 397B A17B measured |
| ProofBench | 9.0% | Not measured | Only GPT-5.1-Codex-Max measured |
| LMArena Text: math | Not measured | 1453 | Only Qwen3.5 397B A17B measured |
| LMArena Text: mathematical work | Not measured | 1454 | Only Qwen3.5 397B A17B measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| LMArena Text: science | Not measured | 1457 | Only Qwen3.5 397B A17B measured |
| LMArena Text: medicine and healthcare | Not measured | 1455 | Only Qwen3.5 397B A17B measured |
| LMArena Text: legal and government | Not measured | 1441 | Only Qwen3.5 397B A17B measured |
| LMArena Text: business and finance | Not measured | 1435 | Only Qwen3.5 397B A17B measured |
| Neither model has a result on the other 14 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1190 | Only Qwen3.5 397B A17B measured |
| Design Arena: UI components | Not measured | 1160 | Only Qwen3.5 397B A17B measured |
| Design Arena: game dev | Not measured | 1140 | Only Qwen3.5 397B A17B measured |
| Design Arena: data viz | Not measured | 1175 | Only Qwen3.5 397B A17B measured |
| Design Arena: 3D | Not measured | 1160 | Only Qwen3.5 397B A17B measured |
| Design Arena: all code categories | Not measured | 1179 | Only Qwen3.5 397B A17B measured |
| Design Arena: SVG | Not measured | 1139 | Only Qwen3.5 397B A17B measured |
| Neither model has a result on the other 2 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index29 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| IFBench | Not measured | 78.8% | Only Qwen3.5 397B A17B measured |
| Long context reasoning (AA-LCR) | Not measured | 77.3% | Only Qwen3.5 397B A17B measured |
| DTBench | Not measured | 87.5% | Only Qwen3.5 397B A17B measured |
| LMCA | Not measured | 37.9% | Only Qwen3.5 397B A17B measured |
| LMArena Text | Not measured | 1437 | Only Qwen3.5 397B A17B measured |
| LMArena Text: instruction following | Not measured | 1424 | Only Qwen3.5 397B A17B measured |
| LMArena Text: creative writing | Not measured | 1402 | Only Qwen3.5 397B A17B measured |
| LMArena Vision | Not measured | 1262 | Only Qwen3.5 397B A17B measured |
| LMArena Text: English | Not measured | 1443 | Only Qwen3.5 397B A17B measured |
| LMArena Text: non-English | Not measured | 1430 | Only Qwen3.5 397B A17B measured |
| LMArena Text: Chinese | Not measured | 1498 | Only Qwen3.5 397B A17B measured |
| LMArena Text: German | Not measured | 1445 | Only Qwen3.5 397B A17B measured |
| LMArena Text: Japanese | Not measured | 1429 | Only Qwen3.5 397B A17B measured |
| LMArena Text: Korean | Not measured | 1384 | Only Qwen3.5 397B A17B measured |
| LMArena Text: French | Not measured | 1462 | Only Qwen3.5 397B A17B measured |
| LMArena Text: Spanish | Not measured | 1441 | Only Qwen3.5 397B A17B measured |
| LMArena Text: Russian | Not measured | 1430 | Only Qwen3.5 397B A17B measured |
| LMArena Text: Polish | Not measured | 1447 | Only Qwen3.5 397B A17B measured |
| LMArena Text: multi-turn | Not measured | 1446 | Only Qwen3.5 397B A17B measured |
| LMArena Text: longer queries | Not measured | 1441 | Only Qwen3.5 397B A17B measured |
| LMArena Text: writing and language | Not measured | 1415 | Only Qwen3.5 397B A17B measured |
| LMArena Text: entertainment and media | Not measured | 1399 | Only Qwen3.5 397B A17B measured |
| LMArena Vision: English | Not measured | 1268 | Only Qwen3.5 397B A17B measured |
| LMArena Vision: Chinese | Not measured | 1314 | Only Qwen3.5 397B A17B measured |
| LMArena Vision: reading text | Not measured | 1271 | Only Qwen3.5 397B A17B measured |
| LMArena Vision: humor | Not measured | 1262 | Only Qwen3.5 397B A17B measured |
| LMArena Vision: captioning | Not measured | 1230 | Only Qwen3.5 397B A17B measured |
| LMArena Vision: recognizing things | Not measured | 1265 | Only Qwen3.5 397B A17B measured |
| LMArena Vision: creative writing | Not measured | 1260 | Only Qwen3.5 397B A17B measured |
| Neither model has a result on the other 17 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Qwen3.5 397B A17B | 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
Qwen3.5 397B A17B costs less
Repository review
50K input + 3K output tokens
Qwen3.5 397B A17B costs less
Agent loop
200K input + 10K output tokens
Qwen3.5 397B A17B 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-Max | Qwen3.5 397B A17B |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | December 4, 2025 | February 16, 2026 |
| Context window | 400K | 262K |
| Input $/Mtok | $1.25 | $0.39 |
| Output $/Mtok | $10.00 | $2.34 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.1-Codex-Max better than Qwen3.5 397B A17B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-5.1-Codex-Max for a larger context window (400K against 262K tokens).
Which is cheaper, GPT-5.1-Codex-Max or Qwen3.5 397B A17B?
GPT-5.1-Codex-Max costs $1.25 per million input tokens and $10.00 per million output tokens; Qwen3.5 397B A17B costs $0.39 and $2.34. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-5.1-Codex-Max or Qwen3.5 397B A17B?
GPT-5.1-Codex-Max, with 400K tokens against 262K.
How many benchmarks is the GPT-5.1-Codex-Max vs Qwen3.5 397B A17B comparison based on?
1 benchmarks have a result for both models as of October 22, 2026. GPT-5.1-Codex-Max's coding index rests on 1 independent benchmark and Qwen3.5 397B A17B's on 1. Where only one model has a result, the page says so instead of naming a winner.