GPT-5.3-Codex vs Qwen3 Coder 480B A35B
Updated October 21, 2026. 18 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick Qwen3 Coder 480B A35B for coding: it scores 50.0 on the Vibe Coding Index against 29.5 for GPT-5.3-Codex.
Pick GPT-5.3-Codex when the context window is larger, 400K against 262K tokens, or when what matters is LMArena WebDev or Terminal-Bench Hard (Artificial Analysis run), where it leads.
- Shared results
- 18
- GPT-5.3-Codex only
- 16
- Qwen3 Coder 480B A35B only
- 34
- 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, four public coding benchmarks
Qwen3 Coder 480B A35B
Qwen3 Coder 480B A35B scores 50.0 against 29.5.
1 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-5.3-Codex
GPT-5.3-Codex leads, 32.5 against 11.9.
Same board, both measured
Web design
Design Arena: websites
GPT-5.3-Codex
GPT-5.3-Codex leads, 1163 against 1159.
Same board, both measured
Long documents
Listed context window
GPT-5.3-Codex
GPT-5.3-Codex takes 400K tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 Coder 480B A35B
GPT-5.3-Codex $0.0088, Qwen3 Coder 480B A35B $0.0008 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 29.5 from 1 benchmark, Qwen3 Coder 480B A35B 50.0 from 2 benchmarks.
- Widest category
- Agentic work, 42.4 points apart on average over 1 shared benchmark.
- Widest single gap
- Terminal-Bench: GPT-5.3-Codex 78.4% against Qwen3 Coder 480B A35B 27.2%, 51.2 points apart on a 0 to 100 scale.
- Output price
- GPT-5.3-Codex $14.00 against Qwen3 Coder 480B A35B $1.00 per million output tokens, 14.0x apart.
- Context window
- GPT-5.3-Codex takes 400K tokens, Qwen3 Coder 480B A35B 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-5.3-Codex | Bars | Qwen3 Coder 480B A35B | Ties |
|---|---|---|---|---|
| Coding | 7 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| Web design | 5 | 0 | ||
| General | 2 | 0 | ||
| All shared boards | 18 | 0 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index70
| Benchmark | GPT-5.3-Codex | Qwen3 Coder 480B A35B | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 69.6% | Only Qwen3 Coder 480B A35B measured |
| LMArena WebDev | 1407 | 1275 | GPT-5.3-Codex |
| 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% | Not measured | Only GPT-5.3-Codex measured |
| IOI (Vals AI) | 53.8% | Not measured | Only GPT-5.3-Codex measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 58.5% | Only Qwen3 Coder 480B A35B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 53.0% | 18.9% | GPT-5.3-Codex |
| Terminal-Bench | 78.4% | 27.2% | GPT-5.3-Codex |
| GSO-Bench | Not measured | 4.9% | Only Qwen3 Coder 480B A35B measured |
| WeirdML | 79.3% | 41.2% | GPT-5.3-Codex |
| ALE-Bench | 1655.2 | 461.4 | GPT-5.3-Codex |
| LMArena Text: coding | Not measured | 1412 | Only Qwen3 Coder 480B A35B measured |
| LMArena Text: software and IT | Not measured | 1393 | Only Qwen3 Coder 480B A35B measured |
| LMArena WebDev: React | 1395 | 1257 | GPT-5.3-Codex |
| LMArena WebDev: HTML | 1422 | 1287 | GPT-5.3-Codex |
| LMArena WebDev: from an image | 1443 | Not measured | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 21 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.3-Codex | Qwen3 Coder 480B A35B | Leader |
|---|---|---|---|
| AA Intelligence Index | 32.5 | 11.9 | GPT-5.3-Codex |
| Epoch Capabilities Index | 157.1 | Not measured | Only GPT-5.3-Codex measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.3-Codex | Qwen3 Coder 480B A35B | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1033 | 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 | 1054 | 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 |
| τ²-Bench Telecom | 86.0% | 43.6% | GPT-5.3-Codex |
| 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 |
| Neither model has a result on the other 31 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.3-Codex | Qwen3 Coder 480B A35B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 91.5% | 61.8% | GPT-5.3-Codex |
| Humanity's Last Exam (Artificial Analysis run) | 42.5% | 4.5% | GPT-5.3-Codex |
| LMArena Text: hard prompts | Not measured | 1372 | Only Qwen3 Coder 480B A35B measured |
| LMArena Text: expert | Not measured | 1338 | Only Qwen3 Coder 480B A35B measured |
| LMArena Text: hard prompts in English | Not measured | 1380 | Only Qwen3 Coder 480B A35B measured |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GPT-5.3-Codex | Qwen3 Coder 480B A35B | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 39.3 | Only Qwen3 Coder 480B A35B measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 39.3% | Only Qwen3 Coder 480B A35B measured |
| LMArena Text: math | Not measured | 1365 | Only Qwen3 Coder 480B A35B measured |
| LMArena Text: mathematical work | Not measured | 1367 | Only Qwen3 Coder 480B A35B 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 Coder 480B A35B | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 78.8% | Only Qwen3 Coder 480B A35B measured |
| LMArena Text: science | Not measured | 1347 | Only Qwen3 Coder 480B A35B measured |
| LMArena Text: medicine and healthcare | Not measured | 1347 | Only Qwen3 Coder 480B A35B measured |
| LMArena Text: legal and government | Not measured | 1358 | Only Qwen3 Coder 480B A35B measured |
| LMArena Text: business and finance | Not measured | 1343 | Only Qwen3 Coder 480B A35B measured |
| Neither model has a result on the other 12 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GPT-5.3-Codex | Qwen3 Coder 480B A35B | Leader |
|---|---|---|---|
| Design Arena: websites | 1163 | 1159 | GPT-5.3-Codex |
| Design Arena: UI components | 1137 | 1112 | GPT-5.3-Codex |
| Design Arena: game dev | 1162 | 1099 | GPT-5.3-Codex |
| Design Arena: data viz | 1164 | 1084 | GPT-5.3-Codex |
| Design Arena: 3D | 1008 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: all code categories | 1145 | 1143 | GPT-5.3-Codex |
| Design Arena: SVG | 1129 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: ASCII art | 1151 | Not measured | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.3-Codex | Qwen3 Coder 480B A35B | Leader |
|---|---|---|---|
| IFBench | 75.4% | 40.5% | GPT-5.3-Codex |
| Long context reasoning (AA-LCR) | 83.3% | 45.7% | GPT-5.3-Codex |
| LMArena Text | Not measured | 1357 | Only Qwen3 Coder 480B A35B measured |
| LMArena Text: instruction following | Not measured | 1355 | Only Qwen3 Coder 480B A35B measured |
| LMArena Text: creative writing | Not measured | 1333 | Only Qwen3 Coder 480B A35B measured |
| LMArena Text: English | Not measured | 1359 | Only Qwen3 Coder 480B A35B measured |
| LMArena Text: non-English | Not measured | 1346 | Only Qwen3 Coder 480B A35B measured |
| LMArena Text: Chinese | Not measured | 1357 | Only Qwen3 Coder 480B A35B measured |
| LMArena Text: German | Not measured | 1325 | Only Qwen3 Coder 480B A35B measured |
| LMArena Text: Japanese | Not measured | 1311 | Only Qwen3 Coder 480B A35B measured |
| LMArena Text: Korean | Not measured | 1305 | Only Qwen3 Coder 480B A35B measured |
| LMArena Text: French | Not measured | 1398 | Only Qwen3 Coder 480B A35B measured |
| LMArena Text: Spanish | Not measured | 1360 | Only Qwen3 Coder 480B A35B measured |
| LMArena Text: Russian | Not measured | 1366 | Only Qwen3 Coder 480B A35B measured |
| LMArena Text: Polish | Not measured | 1344 | Only Qwen3 Coder 480B A35B measured |
| LMArena Text: multi-turn | Not measured | 1366 | Only Qwen3 Coder 480B A35B measured |
| LMArena Text: longer queries | Not measured | 1378 | Only Qwen3 Coder 480B A35B measured |
| LMArena Text: writing and language | Not measured | 1340 | Only Qwen3 Coder 480B A35B measured |
| LMArena Text: entertainment and media | Not measured | 1317 | Only Qwen3 Coder 480B A35B measured |
| Neither model has a result on the other 27 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.3-Codex | Qwen3 Coder 480B A35B | 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 Coder 480B A35B costs less
Repository review
50K input + 3K output tokens
Qwen3 Coder 480B A35B costs less
Agent loop
200K input + 10K output tokens
Qwen3 Coder 480B A35B 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 Coder 480B A35B |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | February 24, 2026 | July 23, 2025 |
| Context window | 400K | 262K |
| Input $/Mtok | $1.75 | $0.30 |
| Output $/Mtok | $14.00 | $1.00 |
| Coding index | 29.5 | 50.0 |
| Independent measurements behind it | 1 | 2 |
| Cost per solved task | $0.59 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.3-Codex better than Qwen3 Coder 480B A35B for coding?
Pick Qwen3 Coder 480B A35B for coding: it scores 50.0 on the Vibe Coding Index against 29.5 for GPT-5.3-Codex. Pick GPT-5.3-Codex when the context window is larger, 400K against 262K tokens, or when what matters is LMArena WebDev or Terminal-Bench Hard (Artificial Analysis run), where it leads.
Which is cheaper, GPT-5.3-Codex or Qwen3 Coder 480B A35B?
GPT-5.3-Codex costs $1.75 per million input tokens and $14.00 per million output tokens; Qwen3 Coder 480B A35B costs $0.30 and $1.00. Cost per solved task is known for GPT-5.3-Codex only.
Which has the bigger context window, GPT-5.3-Codex or Qwen3 Coder 480B A35B?
GPT-5.3-Codex, with 400K tokens against 262K.
How many benchmarks is the GPT-5.3-Codex vs Qwen3 Coder 480B A35B comparison based on?
18 benchmarks have a result for both models as of October 21, 2026. GPT-5.3-Codex's coding index rests on 1 independent benchmark and Qwen3 Coder 480B A35B's on 2. Where only one model has a result, the page says so instead of naming a winner.