GPT-5.2 Chat vs Qwen3 Coder 480B A35B
Qwen3 Coder 480B A35B costs 9.3x less per output token than GPT-5.2 Chat ($1.50 vs $14.00 per million). Updated October 21, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $14.00
- Coding
- No data
- Overall
- No data
- Output $/M
- $1.50
GPT-5.2 Chat or Qwen3 Coder 480B A35B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
No benchmark has tested both yet. GPT-5.2 Chat has none yet; Qwen3 Coder 480B A35B on SWE-bench Verified, LMArena WebDev and LiveCodeBench (Artificial Analysis run). Compare them on price and context instead.
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 Coder 480B A35B has a AA Intelligence Index result, 11.9.
Directional only
Web design
Design Arena: websites
No call
Only Qwen3 Coder 480B A35B has a Design Arena: websites result, 1159.
Directional only
Long documents
Listed context window
Qwen3 Coder 480B A35B
Qwen3 Coder 480B A35B takes 262K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 Coder 480B A35B
GPT-5.2 Chat $0.0088, Qwen3 Coder 480B A35B $0.0010 per turn.
List prices
At a glance
- Coding index
- GPT-5.2 Chat not benchmarked, Qwen3 Coder 480B A35B not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- GPT-5.2 Chat $14.00 against Qwen3 Coder 480B A35B $1.50 per million output tokens, 9.3x apart.
- Context window
- GPT-5.2 Chat takes 128K tokens, Qwen3 Coder 480B A35B 262K.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index12 boards measured
| Benchmark | GPT-5.2 Chat | Qwen3 Coder 480B A35B | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 69.6% | Only Qwen3 Coder 480B A35B measured |
| LMArena WebDev | Not measured | 1275 | Only Qwen3 Coder 480B A35B measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 58.5% | Only Qwen3 Coder 480B A35B measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 18.9% | Only Qwen3 Coder 480B A35B measured |
| Terminal-Bench | Not measured | 27.2% | Only Qwen3 Coder 480B A35B measured |
| GSO-Bench | Not measured | 4.9% | Only Qwen3 Coder 480B A35B measured |
| WeirdML | Not measured | 41.2% | Only Qwen3 Coder 480B A35B measured |
| ALE-Bench | Not measured | 461.4 | Only Qwen3 Coder 480B A35B measured |
| 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 | Not measured | 1257 | Only Qwen3 Coder 480B A35B measured |
| LMArena WebDev: HTML | Not measured | 1287 | Only Qwen3 Coder 480B A35B measured |
| Neither model has a result on the other 27 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-5.2 Chat | Qwen3 Coder 480B A35B | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 11.9 | Only Qwen3 Coder 480B A35B measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-5.2 Chat | Qwen3 Coder 480B A35B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 43.6% | Only Qwen3 Coder 480B A35B measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
| Benchmark | GPT-5.2 Chat | Qwen3 Coder 480B A35B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 61.8% | Only Qwen3 Coder 480B A35B measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 4.5% | Only Qwen3 Coder 480B A35B measured |
| 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 |
| Kagi LLM Benchmark | Not measured | 49.5% | 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 measured
| Benchmark | GPT-5.2 Chat | 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 measured
| Benchmark | GPT-5.2 Chat | 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 13 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | GPT-5.2 Chat | Qwen3 Coder 480B A35B | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1159 | Only Qwen3 Coder 480B A35B measured |
| Design Arena: UI components | Not measured | 1113 | Only Qwen3 Coder 480B A35B measured |
| Design Arena: game dev | Not measured | 1099 | Only Qwen3 Coder 480B A35B measured |
| Design Arena: data viz | Not measured | 1084 | Only Qwen3 Coder 480B A35B measured |
| Design Arena: all code categories | Not measured | 1143 | Only Qwen3 Coder 480B A35B measured |
| Neither model has a result on the other 4 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index19 boards measured
| Benchmark | GPT-5.2 Chat | Qwen3 Coder 480B A35B | Leader |
|---|---|---|---|
| IFBench | Not measured | 40.5% | Only Qwen3 Coder 480B A35B measured |
| Long context reasoning (AA-LCR) | Not measured | 45.7% | Only Qwen3 Coder 480B A35B measured |
| 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 | |||
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-5.2 Chat | Qwen3 Coder 480B A35B |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | December 10, 2025 | July 23, 2025 |
| Context window | 128K | 262K |
| Input $/Mtok | $1.75 | $0.30 |
| Output $/Mtok | $14.00 | $1.50 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.2 Chat better than Qwen3 Coder 480B A35B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, GPT-5.2 Chat or Qwen3 Coder 480B A35B?
GPT-5.2 Chat costs $1.75 per million input tokens and $14.00 per million output tokens; Qwen3 Coder 480B A35B costs $0.30 and $1.50. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-5.2 Chat or Qwen3 Coder 480B A35B?
Qwen3 Coder 480B A35B, with 262K tokens against 128K.
Has any benchmark tested both GPT-5.2 Chat and Qwen3 Coder 480B A35B?
No benchmark has tested both yet. GPT-5.2 Chat has none yet; Qwen3 Coder 480B A35B on SWE-bench Verified, LMArena WebDev and LiveCodeBench (Artificial Analysis run). Compare them on price and context instead. Data as of October 21, 2026.