GPT-5.2 Pro vs Qwen3 VL 30B A3B Instruct
Qwen3 VL 30B A3B Instruct costs 240.0x less per output token than GPT-5.2 Pro ($0.70 vs $168.00 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $168.00
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.70
GPT-5.2 Pro or Qwen3 VL 30B A3B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-5.2 Pro for a larger context window (400K against 262K tokens).
No benchmark has tested both yet. GPT-5.2 Pro has results on Epoch Capabilities Index, ARC-AGI and ARC-AGI-2; Qwen3 VL 30B A3B Instruct on LiveCodeBench (Artificial Analysis run), Terminal-Bench Hard (Artificial Analysis run) and AA Intelligence Index. 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 30B A3B Instruct has a AA Intelligence Index result, 7.9.
Directional only
Long documents
Listed context window
GPT-5.2 Pro
GPT-5.2 Pro takes 400K tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 VL 30B A3B Instruct
GPT-5.2 Pro $0.105, Qwen3 VL 30B A3B Instruct $0.0006 per turn.
List prices
At a glance
- Coding index
- GPT-5.2 Pro not benchmarked, Qwen3 VL 30B A3B 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 Pro $168.00 against Qwen3 VL 30B A3B Instruct $0.70 per million output tokens, 240.0x apart.
- Context window
- GPT-5.2 Pro takes 400K tokens, Qwen3 VL 30B A3B 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 Index2 boards measured
| Benchmark | GPT-5.2 Pro | Qwen3 VL 30B A3B Instruct | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | Not measured | 69.7% | Only Qwen3 VL 30B A3B Instruct measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 6.1% | Only Qwen3 VL 30B A3B Instruct measured |
| Neither model has a result on the other 37 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | GPT-5.2 Pro | Qwen3 VL 30B A3B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 7.9 | Only Qwen3 VL 30B A3B Instruct measured |
| Epoch Capabilities Index | 156.3 | Not measured | Only GPT-5.2 Pro measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-5.2 Pro | Qwen3 VL 30B A3B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 19.9% | Only Qwen3 VL 30B A3B Instruct 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 Pro | Qwen3 VL 30B A3B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 72.0% | Only Qwen3 VL 30B A3B Instruct measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 8.9% | Only Qwen3 VL 30B A3B Instruct measured |
| ARC-AGI | 90.5% | Not measured | Only GPT-5.2 Pro measured |
| ARC-AGI-2 | 54.2% | Not measured | Only GPT-5.2 Pro measured |
| SimpleBench | 57.4% | Not measured | Only GPT-5.2 Pro measured |
| Chess puzzles | 60.0% | Not measured | Only GPT-5.2 Pro 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 Pro | Qwen3 VL 30B A3B Instruct | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 82.3 | Only Qwen3 VL 30B A3B Instruct measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 82.3% | Only Qwen3 VL 30B A3B Instruct measured |
| FrontierMath, tiers 1 to 3 | 74.0% | Not measured | Only GPT-5.2 Pro measured |
| FrontierMath, tier 4 | 46.0% | Not measured | Only GPT-5.2 Pro measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-5.2 Pro | Qwen3 VL 30B A3B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 80.7% | Only Qwen3 VL 30B A3B Instruct measured |
| Neither model has a result on the other 17 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-5.2 Pro | Qwen3 VL 30B A3B Instruct | Leader |
|---|---|---|---|
| IFBench | Not measured | 45.1% | Only Qwen3 VL 30B A3B Instruct measured |
| Neither model has a result on the other 45 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 30B A3B Instruct costs less
Repository review
50K input + 3K output tokens
Qwen3 VL 30B A3B Instruct costs less
Agent loop
200K input + 10K output tokens
Qwen3 VL 30B A3B 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 Pro | Qwen3 VL 30B A3B Instruct |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | December 10, 2025 | October 6, 2025 |
| Context window | 400K | 262K |
| Input $/Mtok | $21.00 | $0.20 |
| Output $/Mtok | $168.00 | $0.70 |
| 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 Pro better than Qwen3 VL 30B A3B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-5.2 Pro for a larger context window (400K against 262K tokens).
Which is cheaper, GPT-5.2 Pro or Qwen3 VL 30B A3B Instruct?
GPT-5.2 Pro costs $21.00 per million input tokens and $168.00 per million output tokens; Qwen3 VL 30B A3B Instruct costs $0.20 and $0.70. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-5.2 Pro or Qwen3 VL 30B A3B Instruct?
GPT-5.2 Pro, with 400K tokens against 262K.
Has any benchmark tested both GPT-5.2 Pro and Qwen3 VL 30B A3B Instruct?
No benchmark has tested both yet. GPT-5.2 Pro has results on Epoch Capabilities Index, ARC-AGI and ARC-AGI-2; Qwen3 VL 30B A3B Instruct on LiveCodeBench (Artificial Analysis run), Terminal-Bench Hard (Artificial Analysis run) and AA Intelligence Index. Compare them on price and context instead. Data as of October 22, 2026.