GPT-5.2 Chat vs Qwen3 VL 235B A22B Instruct
Qwen3 VL 235B A22B Instruct costs 7.4x less per output token than GPT-5.2 Chat ($1.90 vs $14.00 per million). Updated October 22, 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
- $1.90
GPT-5.2 Chat or Qwen3 VL 235B A22B Instruct: 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 VL 235B A22B Instruct on LiveCodeBench (Artificial Analysis run), Terminal-Bench Hard (Artificial Analysis run) and LMArena Text: coding. 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 VL 235B A22B Instruct has a AA Intelligence Index result, 9.9.
Directional only
Long documents
Listed context window
Qwen3 VL 235B A22B Instruct
Qwen3 VL 235B A22B Instruct takes 262K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 VL 235B A22B Instruct
GPT-5.2 Chat $0.0088, Qwen3 VL 235B A22B Instruct $0.0012 per turn.
List prices
At a glance
- Coding index
- GPT-5.2 Chat not benchmarked, Qwen3 VL 235B A22B Instruct 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 VL 235B A22B Instruct $1.90 per million output tokens, 7.4x apart.
- Context window
- GPT-5.2 Chat takes 128K tokens, Qwen3 VL 235B A22B Instruct 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 Index4 boards measured
| Benchmark | GPT-5.2 Chat | Qwen3 VL 235B A22B Instruct | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | Not measured | 64.6% | Only Qwen3 VL 235B A22B Instruct measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 11.4% | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Text: coding | Not measured | 1438 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Text: software and IT | Not measured | 1442 | Only Qwen3 VL 235B A22B Instruct measured |
| Neither model has a result on the other 35 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-5.2 Chat | Qwen3 VL 235B A22B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 9.9 | Only Qwen3 VL 235B A22B Instruct 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 VL 235B A22B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 54.1% | Only Qwen3 VL 235B A22B Instruct measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index7 boards measured
| Benchmark | GPT-5.2 Chat | Qwen3 VL 235B A22B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 77.2% | Only Qwen3 VL 235B A22B Instruct measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 11.9% | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Text: hard prompts | Not measured | 1428 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Text: expert | Not measured | 1442 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Text: hard prompts in English | Not measured | 1439 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Vision: diagrams | Not measured | 1255 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Vision: homework | Not measured | 1277 | Only Qwen3 VL 235B A22B Instruct measured |
| Neither model has a result on the other 15 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | GPT-5.2 Chat | Qwen3 VL 235B A22B Instruct | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 88.3 | Only Qwen3 VL 235B A22B Instruct measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 88.3% | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Text: math | Not measured | 1425 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Text: mathematical work | Not measured | 1419 | Only Qwen3 VL 235B A22B Instruct 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 VL 235B A22B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 83.6% | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Text: science | Not measured | 1444 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Text: medicine and healthcare | Not measured | 1431 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Text: legal and government | Not measured | 1438 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Text: business and finance | Not measured | 1433 | Only Qwen3 VL 235B A22B Instruct measured |
| Neither model has a result on the other 13 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index27 boards measured
| Benchmark | GPT-5.2 Chat | Qwen3 VL 235B A22B Instruct | Leader |
|---|---|---|---|
| IFBench | Not measured | 56.5% | Only Qwen3 VL 235B A22B Instruct measured |
| Long context reasoning (AA-LCR) | Not measured | 63.7% | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Text | Not measured | 1420 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Text: instruction following | Not measured | 1406 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Text: creative writing | Not measured | 1366 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Vision | Not measured | 1247 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Text: English | Not measured | 1429 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Text: non-English | Not measured | 1405 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Text: Chinese | Not measured | 1463 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Text: German | Not measured | 1424 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Text: Japanese | Not measured | 1385 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Text: Korean | Not measured | 1394 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Text: French | Not measured | 1452 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Text: Spanish | Not measured | 1428 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Text: Russian | Not measured | 1408 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Text: Polish | Not measured | 1419 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Text: multi-turn | Not measured | 1428 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Text: longer queries | Not measured | 1419 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Text: writing and language | Not measured | 1392 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Text: entertainment and media | Not measured | 1368 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Vision: English | Not measured | 1256 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Vision: Chinese | Not measured | 1283 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Vision: reading text | Not measured | 1253 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Vision: humor | Not measured | 1209 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Vision: captioning | Not measured | 1281 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Vision: recognizing things | Not measured | 1249 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Vision: creative writing | Not measured | 1220 | Only Qwen3 VL 235B A22B Instruct measured |
| Neither model has a result on the other 19 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 VL 235B A22B Instruct costs less
Repository review
50K input + 3K output tokens
Qwen3 VL 235B A22B Instruct costs less
Agent loop
200K input + 10K output tokens
Qwen3 VL 235B A22B Instruct 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 VL 235B A22B Instruct |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | December 10, 2025 | September 23, 2025 |
| Context window | 128K | 262K |
| Input $/Mtok | $1.75 | $0.21 |
| Output $/Mtok | $14.00 | $1.90 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.2 Chat better than Qwen3 VL 235B A22B Instruct 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 VL 235B A22B Instruct?
GPT-5.2 Chat costs $1.75 per million input tokens and $14.00 per million output tokens; Qwen3 VL 235B A22B Instruct costs $0.21 and $1.90. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-5.2 Chat or Qwen3 VL 235B A22B Instruct?
Qwen3 VL 235B A22B Instruct, with 262K tokens against 128K.
Has any benchmark tested both GPT-5.2 Chat and Qwen3 VL 235B A22B Instruct?
No benchmark has tested both yet. GPT-5.2 Chat has none yet; Qwen3 VL 235B A22B Instruct on LiveCodeBench (Artificial Analysis run), Terminal-Bench Hard (Artificial Analysis run) and LMArena Text: coding. Compare them on price and context instead. Data as of October 22, 2026.