Model comparison
GPT-5.6 Terra vs Qwen3-VL 235B-A22B
GPT-5.6 Terra is the stronger model overall, scoring 59.2 to 43.2 on the Noometry Index. Qwen3-VL 235B-A22B costs 3.7× less per token, which makes it the better buy when GPT-5.6 Terra's lead doesn't matter for your workload.
Last verified . 18 shared benchmarks.
Alibaba (Qwen)Summary
- They share 18 benchmarks with published results for both. GPT-5.6 Terra scores higher in 9 categories and Qwen3-VL 235B-A22B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Terra leads 81.6 to 39.0.
- Qwen3-VL 235B-A22B is cheaper at $0.70 / $2.80 per million input/output tokens, against $2 / $12 for GPT-5.6 Terra.
- GPT-5.6 Terra accepts more context: 1.05M tokens versus 131K.
- Qwen3-VL 235B-A22B has downloadable open weights; the other is API-only.
Side by side
| GPT-5.6 Terra | Qwen3-VL 235B-A22B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 59.2 | 43.2 |
| Released | 2026-07-09 | 2025-04 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 131K |
| Max output | 128K | 33K |
| Input $ / M tokens | $2 | $0.70 |
| Output $ / M tokens | $12 | $2.80 |
| Results tracked | 52 | 18 |
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Category by category
Coding GPT-5.6 Terra leads
GPT-5.6 Terra: 57.7 (#19), Qwen3-VL 235B-A22B: 42.4 (#100)
| Benchmark | GPT-5.6 Terra | Qwen3-VL 235B-A22B |
|---|---|---|
| LMArena Coding | 1484 | 1439 |
| DeepSWE | 69.6% | — |
| FrontierCode | 41.3% | — |
| CursorBench | 41.3% | — |
| LMArena WebDev | 1522 | — |
| SciCode | 55% | — |
| WeirdML | 78.3% | — |
| ALE-Bench | 1,951 | — |
Agentic & Tool Use Not comparable
GPT-5.6 Terra: 40.1 (#25), Qwen3-VL 235B-A22B: —
| Benchmark | GPT-5.6 Terra | Qwen3-VL 235B-A22B |
|---|---|---|
| APEX-Agents | 58.2% | — |
| BALROG | 53.2% | — |
| GDP.pdf | 24.7% | — |
| Vending-Bench 2 | 7,343 | — |
Reasoning GPT-5.6 Terra leads
GPT-5.6 Terra: 60.7 (#21), Qwen3-VL 235B-A22B: 29.3 (#92)
| Benchmark | GPT-5.6 Terra | Qwen3-VL 235B-A22B |
|---|---|---|
| LMArena Hard Prompts | 1468 | 1428 |
| ARC-AGI-2 | 83.9% | — |
| SimpleBench | 48.9% | — |
| Kagi LLM Benchmark | 51.3% | — |
| NYT Connections (extended) | 78.4% | — |
| ARC-AGI-1 | 96.5% | — |
| CritPt | 30% | — |
| Chess Puzzles | 54% | — |
| Mystery Game Puzzles | 35% | — |
| DTBench | 93.3% | — |
| LMCA | 55% | — |
| Surface Evolver Bench | 83.8% | — |
| Epoch Capabilities Index | 159.62 | — |
Math GPT-5.6 Terra leads
GPT-5.6 Terra: 81.6 (#12), Qwen3-VL 235B-A22B: 39.0 (#118)
| Benchmark | GPT-5.6 Terra | Qwen3-VL 235B-A22B |
|---|---|---|
| LMArena Math | 1466 | 1426 |
| FrontierMath (Tiers 1-3) | 86% | — |
| FrontierMath Tier 4 | 70.7% | — |
| OTIS Mock AIME 2024-2025 | 99.7% | — |
| ProofBench | 74% | — |
Knowledge GPT-5.6 Terra leads
GPT-5.6 Terra: 61.2 (#30), Qwen3-VL 235B-A22B: 40.3 (#121)
| Benchmark | GPT-5.6 Terra | Qwen3-VL 235B-A22B |
|---|---|---|
| LMArena Expert | 1492 | 1442 |
| GPQA Diamond | 93.3% | — |
| SimpleQA Verified | 43.2% | — |
Multimodal GPT-5.6 Terra leads
GPT-5.6 Terra: 47.3 (#11), Qwen3-VL 235B-A22B: 39.8 (#55)
| Benchmark | GPT-5.6 Terra | Qwen3-VL 235B-A22B |
|---|---|---|
| LMArena Vision | 1271 | 1247 |
| Blueprint-Bench 2 | 30.8% | — |
| Furniture Assembly | 54.2% | — |
| LMArena Document | 1472 | — |
Multilingual GPT-5.6 Terra leads
GPT-5.6 Terra: 54.4 (#44), Qwen3-VL 235B-A22B: 51.9 (#97)
| Benchmark | GPT-5.6 Terra | Qwen3-VL 235B-A22B |
|---|---|---|
| LMArena Non-English | 1439 | 1405 |
| LMArena Chinese | 1513 | 1463 |
| LMArena French | 1471 | 1452 |
| LMArena German | 1460 | 1424 |
| LMArena Japanese | 1457 | 1385 |
| LMArena Korean | 1425 | 1394 |
| LMArena Russian | 1450 | 1408 |
| LMArena Spanish | 1448 | 1428 |
Instruction Following GPT-5.6 Terra leads
GPT-5.6 Terra: 76.4 (#40), Qwen3-VL 235B-A22B: 74.2 (#101)
| Benchmark | GPT-5.6 Terra | Qwen3-VL 235B-A22B |
|---|---|---|
| LMArena Instruction Following | 1454 | 1406 |
Long Context GPT-5.6 Terra leads
GPT-5.6 Terra: 44.4 (#68), Qwen3-VL 235B-A22B: 43.4 (#98)
| Benchmark | GPT-5.6 Terra | Qwen3-VL 235B-A22B |
|---|---|---|
| LMArena Longer Query | 1451 | 1420 |
Writing & Preference GPT-5.6 Terra leads
GPT-5.6 Terra: 70.2 (#23), Qwen3-VL 235B-A22B: 60.2 (#99)
| Benchmark | GPT-5.6 Terra | Qwen3-VL 235B-A22B |
|---|---|---|
| LMArena Text | 1447 | 1420 |
| LMArena Creative Writing | 1410 | 1366 |
| LMArena Multi-Turn | 1449 | 1428 |
| EQ-Bench Creative Writing | 1855 | — |
| EQ-Bench 4 | 1234 | — |
Frequently asked questions
Is GPT-5.6 Terra better than Qwen3-VL 235B-A22B?
GPT-5.6 Terra is the stronger model overall, scoring 59.2 to 43.2 on the Noometry Index. Qwen3-VL 235B-A22B costs 3.7× less per token, which makes it the better buy when GPT-5.6 Terra's lead doesn't matter for your workload.
Which is cheaper, GPT-5.6 Terra or Qwen3-VL 235B-A22B?
Qwen3-VL 235B-A22B is cheaper. It lists at $0.70 per million input tokens and $2.80 per million output tokens; GPT-5.6 Terra lists at $2 and $12.
Is GPT-5.6 Terra or Qwen3-VL 235B-A22B better for coding?
GPT-5.6 Terra scores higher on coding benchmarks: 57.7 versus 42.4 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Terra does, with 1.05M tokens against 131K.
How many benchmarks do GPT-5.6 Terra and Qwen3-VL 235B-A22B share?
18 benchmarks have published results for both models. GPT-5.6 Terra has 52 scored results on Noometry and Qwen3-VL 235B-A22B has 18.