GPT-5.3-Codex vs Qwen3.7 Plus
Updated October 21, 2026. 17 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick GPT-5.3-Codex for coding: it scores 60.0 on the Vibe Coding Index against 33.2 for Qwen3.7 Plus.
Pick Qwen3.7 Plus when output tokens cost 10.9x less, or when the context window is larger, 1M against 400K tokens.
- Shared results
- 17
- GPT-5.3-Codex only
- 17
- Qwen3.7 Plus only
- 58
- Like-for-like categories
- 6 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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
GPT-5.3-Codex
GPT-5.3-Codex scores 60.0 against 33.2.
3 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-5.3-Codex
GPT-5.3-Codex leads, 32.5 against 25.2.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Qwen3.7 Plus has a AA Agentic Index result, 17.5.
Directional only
Web design
Design Arena: websites
Qwen3.7 Plus
Qwen3.7 Plus leads, 1269 against 1163.
Same board, both measured
Long documents
Listed context window
Qwen3.7 Plus
Qwen3.7 Plus takes 1M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.7 Plus
GPT-5.3-Codex $0.0088, Qwen3.7 Plus $0.0010 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.3-Codex has the number, $0.59.
Directional only
At a glance
- Coding index
- GPT-5.3-Codex 60.0 from 3 benchmarks, Qwen3.7 Plus 33.2 from 2 benchmarks.
- Widest category
- Web design, 13.8 points apart on average over 8 shared benchmarks.
- Widest single gap
- Design Arena: 3D: GPT-5.3-Codex 1008 against Qwen3.7 Plus 1239, 28.9 points apart on a 0 to 100 scale.
- Output price
- GPT-5.3-Codex $14.00 against Qwen3.7 Plus $1.28 per million output tokens, 10.9x apart.
- Context window
- GPT-5.3-Codex takes 400K tokens, Qwen3.7 Plus 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-5.3-Codex | Bars | Qwen3.7 Plus | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 2 | 0 | ||
| Web design | 1 | 7 | ||
| General | 1 | 1 | ||
| All shared boards | 8 | 9 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | GPT-5.3-Codex | Qwen3.7 Plus | Leader |
|---|---|---|---|
| LMArena WebDev | 1407 | Not measured | Only GPT-5.3-Codex measured |
| AA Coding Index | Not measured | 55.9 | Only Qwen3.7 Plus measured |
| LiveCodeBench (Vals AI) | 87.3% | Not measured | Only GPT-5.3-Codex measured |
| SWE-bench Verified (Vals AI) | 78.0% | Not measured | Only GPT-5.3-Codex measured |
| Vibe Code Bench (Vals AI) | 61.8% | 46.4% | GPT-5.3-Codex |
| Code migration (Vals AI) | Not measured | 12.9% | Only Qwen3.7 Plus measured |
| IOI (Vals AI) | 53.8% | Not measured | Only GPT-5.3-Codex measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 52.8% | Only Qwen3.7 Plus measured |
| SkillsBench (Vals AI) | Not measured | 54.3% | Only Qwen3.7 Plus measured |
| SciCode | Not measured | 46.1% | Only Qwen3.7 Plus measured |
| Terminal-Bench Hard (Artificial Analysis run) | 53.0% | 47.0% | GPT-5.3-Codex |
| Terminal-Bench | 78.4% | Not measured | Only GPT-5.3-Codex measured |
| FrontierCode | Not measured | 10.2% | Only Qwen3.7 Plus 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 | 1473 | Only Qwen3.7 Plus measured |
| LMArena Text: software and IT | Not measured | 1473 | Only Qwen3.7 Plus measured |
| LMArena WebDev: React | 1395 | Not measured | Only GPT-5.3-Codex measured |
| LMArena WebDev: HTML | 1422 | Not measured | Only GPT-5.3-Codex measured |
| LMArena WebDev: from an image | 1443 | Not measured | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 18 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.3-Codex | Qwen3.7 Plus | Leader |
|---|---|---|---|
| AA Intelligence Index | 32.5 | 25.2 | GPT-5.3-Codex |
| Epoch Capabilities Index | 157.1 | 147.0 | GPT-5.3-Codex |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-5.3-Codex | Qwen3.7 Plus | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 17.5 | Only Qwen3.7 Plus measured |
| Design Arena: agents building web apps | 1032 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building full-stack apps | 983 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building mobile apps | 1053 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building Android apps | 1084 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building Godot games | 1124 | Not measured | Only GPT-5.3-Codex measured |
| Finance Agent (Vals AI) | Not measured | 38.2% | Only Qwen3.7 Plus measured |
| Tax Agent Bench (Vals AI) | Not measured | 10.5% | Only Qwen3.7 Plus measured |
| Legal Research Bench (Vals AI) | Not measured | 16.4% | Only Qwen3.7 Plus measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 49.3% | Only Qwen3.7 Plus measured |
| τ²-Bench Telecom | 86.0% | 93.0% | Qwen3.7 Plus |
| METR time horizons | 74.5% | Not measured | Only GPT-5.3-Codex measured |
| OSWorld 2.0 | Not measured | 2.8% | Only Qwen3.7 Plus measured |
| Vending-Bench 2 | $5,940 | Not measured | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 25 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.3-Codex | Qwen3.7 Plus | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 91.5% | 90.0% | GPT-5.3-Codex |
| Humanity's Last Exam (Artificial Analysis run) | 42.5% | 35.6% | GPT-5.3-Codex |
| GPQA Diamond (Epoch AI run) | Not measured | 87.9% | Only Qwen3.7 Plus measured |
| Mystery game puzzles | Not measured | 17.0% | Only Qwen3.7 Plus measured |
| LMArena Text: hard prompts | Not measured | 1460 | Only Qwen3.7 Plus measured |
| LMArena Text: expert | Not measured | 1466 | Only Qwen3.7 Plus measured |
| LMArena Text: hard prompts in English | Not measured | 1460 | Only Qwen3.7 Plus measured |
| LMArena Vision: diagrams | Not measured | 1292 | Only Qwen3.7 Plus measured |
| LMArena Vision: homework | Not measured | 1295 | Only Qwen3.7 Plus measured |
| Neither model has a result on the other 12 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GPT-5.3-Codex | Qwen3.7 Plus | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 93.3% | Only Qwen3.7 Plus measured |
| FrontierMath, tiers 1 to 3 | Not measured | 34.4% | Only Qwen3.7 Plus measured |
| LMArena Text: math | Not measured | 1467 | Only Qwen3.7 Plus measured |
| LMArena Text: mathematical work | Not measured | 1470 | Only Qwen3.7 Plus measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GPT-5.3-Codex | Qwen3.7 Plus | Leader |
|---|---|---|---|
| MortgageTax (Vals AI) | Not measured | 66.2% | Only Qwen3.7 Plus measured |
| LMArena Text: science | Not measured | 1464 | Only Qwen3.7 Plus measured |
| LMArena Text: medicine and healthcare | Not measured | 1464 | Only Qwen3.7 Plus measured |
| LMArena Text: legal and government | Not measured | 1451 | Only Qwen3.7 Plus measured |
| LMArena Text: business and finance | Not measured | 1435 | Only Qwen3.7 Plus measured |
| Neither model has a result on the other 12 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index17
| Benchmark | GPT-5.3-Codex | Qwen3.7 Plus | Leader |
|---|---|---|---|
| Design Arena: websites | 1163 | 1269 | Qwen3.7 Plus |
| Design Arena: UI components | 1137 | 1261 | Qwen3.7 Plus |
| Design Arena: game dev | 1161 | 1262 | Qwen3.7 Plus |
| Design Arena: data viz | 1163 | 1275 | Qwen3.7 Plus |
| Design Arena: 3D | 1008 | 1239 | Qwen3.7 Plus |
| Design Arena: all code categories | 1145 | 1269 | Qwen3.7 Plus |
| Design Arena: SVG | 1129 | 1208 | Qwen3.7 Plus |
| Design Arena: ASCII art | 1151 | 1144 | GPT-5.3-Codex |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | GPT-5.3-Codex | Qwen3.7 Plus | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 39.3% | Only Qwen3.7 Plus measured |
| Vals Multimodal Index | Not measured | 53.9% | Only Qwen3.7 Plus measured |
| IFBench | 75.4% | 78.0% | Qwen3.7 Plus |
| Long context reasoning (AA-LCR) | 83.3% | 73.0% | GPT-5.3-Codex |
| DTBench | Not measured | 84.0% | Only Qwen3.7 Plus measured |
| LMCA | Not measured | 37.6% | Only Qwen3.7 Plus measured |
| LMArena Text | Not measured | 1454 | Only Qwen3.7 Plus measured |
| LMArena Text: instruction following | Not measured | 1439 | Only Qwen3.7 Plus measured |
| LMArena Text: creative writing | Not measured | 1438 | Only Qwen3.7 Plus measured |
| LMArena Vision | Not measured | 1278 | Only Qwen3.7 Plus measured |
| LMArena Text: English | Not measured | 1458 | Only Qwen3.7 Plus measured |
| LMArena Text: non-English | Not measured | 1445 | Only Qwen3.7 Plus measured |
| LMArena Text: Chinese | Not measured | 1509 | Only Qwen3.7 Plus measured |
| LMArena Text: German | Not measured | 1471 | Only Qwen3.7 Plus measured |
| LMArena Text: Japanese | Not measured | 1412 | Only Qwen3.7 Plus measured |
| LMArena Text: Korean | Not measured | 1413 | Only Qwen3.7 Plus measured |
| LMArena Text: French | Not measured | 1472 | Only Qwen3.7 Plus measured |
| LMArena Text: Spanish | Not measured | 1457 | Only Qwen3.7 Plus measured |
| LMArena Text: Russian | Not measured | 1457 | Only Qwen3.7 Plus measured |
| LMArena Text: Polish | Not measured | 1449 | Only Qwen3.7 Plus measured |
| LMArena Text: multi-turn | Not measured | 1459 | Only Qwen3.7 Plus measured |
| LMArena Text: longer queries | Not measured | 1454 | Only Qwen3.7 Plus measured |
| LMArena Text: writing and language | Not measured | 1437 | Only Qwen3.7 Plus measured |
| LMArena Text: entertainment and media | Not measured | 1422 | Only Qwen3.7 Plus measured |
| LMArena Vision: English | Not measured | 1280 | Only Qwen3.7 Plus measured |
| LMArena Vision: Chinese | Not measured | 1332 | Only Qwen3.7 Plus measured |
| LMArena Vision: reading text | Not measured | 1288 | Only Qwen3.7 Plus measured |
| LMArena Vision: humor | Not measured | 1261 | Only Qwen3.7 Plus measured |
| LMArena Vision: creative writing | Not measured | 1258 | Only Qwen3.7 Plus measured |
| LMArena Document | Not measured | 1444 | Only Qwen3.7 Plus measured |
| Neither model has a result on the other 16 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.3-Codex | Qwen3.7 Plus | 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
Qwen3.7 Plus costs less
Repository review
50K input + 3K output tokens
Qwen3.7 Plus costs less
Agent loop
200K input + 10K output tokens
Qwen3.7 Plus 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-5.3-Codex | Qwen3.7 Plus |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | No public weights listed |
| Released | February 24, 2026 | June 3, 2026 |
| Context window | 400K | 1M |
| Input $/Mtok | $1.75 | $0.32 |
| Output $/Mtok | $14.00 | $1.28 |
| Coding index | 60.0 | 33.2 |
| Independent measurements behind it | 3 | 2 |
| Cost per solved task | $0.59 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.3-Codex better than Qwen3.7 Plus for coding?
Pick GPT-5.3-Codex for coding: it scores 60.0 on the Vibe Coding Index against 33.2 for Qwen3.7 Plus. Pick Qwen3.7 Plus when output tokens cost 10.9x less, or when the context window is larger, 1M against 400K tokens.
Which is cheaper, GPT-5.3-Codex or Qwen3.7 Plus?
GPT-5.3-Codex costs $1.75 per million input tokens and $14.00 per million output tokens; Qwen3.7 Plus costs $0.32 and $1.28. Cost per solved task is known for GPT-5.3-Codex only.
Which has the bigger context window, GPT-5.3-Codex or Qwen3.7 Plus?
Qwen3.7 Plus, with 1M tokens against 400K.
How many benchmarks is the GPT-5.3-Codex vs Qwen3.7 Plus comparison based on?
17 benchmarks have a result for both models as of October 21, 2026. GPT-5.3-Codex's coding index rests on 3 independent benchmarks and Qwen3.7 Plus's on 2. Where only one model has a result, the page says so instead of naming a winner.