Model comparison
GPT-5.6 Terra vs Qwen Max
GPT-5.6 Terra is the stronger model overall, scoring 59.1 to 34.4 on the Noometry Index. Qwen Max costs 1.6× less per token, which makes it the better buy when GPT-5.6 Terra's lead doesn't matter for your workload.
Last verified . 19 shared benchmarks.
Alibaba (Qwen)Summary
- They share 19 benchmarks with published results for both. GPT-5.6 Terra scores higher in 8 categories and Qwen Max in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Terra leads 81.5 to 21.0.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 99.7% for GPT-5.6 Terra and 16.1% for Qwen Max.
- Qwen Max is cheaper at $1.60 / $6.40 per million input/output tokens, against $2 / $12 for GPT-5.6 Terra.
- GPT-5.6 Terra accepts more context: 1.05M tokens versus 33K.
Side by side
| GPT-5.6 Terra | Qwen Max | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 59.1 | 34.4 |
| Released | 2026-07-09 | 2024-04-03 |
| Weights | Proprietary | Proprietary |
| Context window | 1.05M | 33K |
| Max output | 128K | 8K |
| Input $ / M tokens | $2 | $1.60 |
| Output $ / M tokens | $12 | $6.40 |
| Results tracked | 52 | 23 |
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Category by category
Coding GPT-5.6 Terra leads
GPT-5.6 Terra: 57.1 (#16), Qwen Max: 30.3 (#288)
| Benchmark | GPT-5.6 Terra | Qwen Max |
|---|---|---|
| LMArena Coding | 1484 | 1288 |
| DeepSWE | 69.6% | — |
| FrontierCode | 41.3% | — |
| Aider Polyglot | — | 21.8% |
| CursorBench | 41.3% | — |
| LMArena WebDev | 1520 | — |
| SciCode | 55% | — |
| WeirdML | 78.3% | — |
| ALE-Bench | 1,951 | — |
Agentic & Tool Use Not comparable
GPT-5.6 Terra: 40.2 (#23), Qwen Max: —
| Benchmark | GPT-5.6 Terra | Qwen Max |
|---|---|---|
| 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: 60.6 (#19), Qwen Max: 24.1 (#154)
| Benchmark | GPT-5.6 Terra | Qwen Max |
|---|---|---|
| LMArena Hard Prompts | 1467 | 1270 |
| 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 | 159.31 | — |
Math GPT-5.6 Terra leads
GPT-5.6 Terra: 81.5 (#10), Qwen Max: 21.0 (#272)
| Benchmark | GPT-5.6 Terra | Qwen Max |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 99.7% | 16.1% |
| LMArena Math | 1463 | 1275 |
| FrontierMath (Tiers 1-3) | 86% | — |
| FrontierMath Tier 4 | 70.7% | — |
| ProofBench | 74% | — |
| MATH Level 5 | — | 67.2% |
| FrontierMath (Feb 2025 set) | — | 1% |
Knowledge GPT-5.6 Terra leads
GPT-5.6 Terra: 61.3 (#27), Qwen Max: 30.3 (#222)
| Benchmark | GPT-5.6 Terra | Qwen Max |
|---|---|---|
| GPQA Diamond | 93.3% | 56.1% |
| LMArena Expert | 1492 | 1248 |
| SimpleQA Verified | 43.2% | — |
Multimodal Not comparable
GPT-5.6 Terra: 47.5 (#10), Qwen Max: —
| Benchmark | GPT-5.6 Terra | Qwen Max |
|---|---|---|
| LMArena Vision | 1271 | — |
| Blueprint-Bench 2 | 30.8% | — |
| Furniture Assembly | 54.2% | — |
| LMArena Document | 1472 | — |
Multilingual GPT-5.6 Terra leads
GPT-5.6 Terra: 54.5 (#42), Qwen Max: 42.0 (#197)
| Benchmark | GPT-5.6 Terra | Qwen Max |
|---|---|---|
| LMArena Non-English | 1439 | 1263 |
| LMArena Chinese | 1514 | 1254 |
| LMArena French | 1476 | 1329 |
| LMArena German | 1462 | 1254 |
| LMArena Japanese | 1446 | 1205 |
| LMArena Korean | 1420 | 1141 |
| LMArena Russian | 1450 | 1274 |
| LMArena Spanish | 1455 | 1290 |
Instruction Following GPT-5.6 Terra leads
GPT-5.6 Terra: 76.6 (#38), Qwen Max: 66.7 (#203)
| Benchmark | GPT-5.6 Terra | Qwen Max |
|---|---|---|
| LMArena Instruction Following | 1453 | 1262 |
Long Context GPT-5.6 Terra leads
GPT-5.6 Terra: 44.4 (#64), Qwen Max: 39.4 (#175)
| Benchmark | GPT-5.6 Terra | Qwen Max |
|---|---|---|
| LMArena Longer Query | 1452 | 1289 |
| Fiction.LiveBench | — | 66.7% |
Writing & Preference GPT-5.6 Terra leads
GPT-5.6 Terra: 70.4 (#21), Qwen Max: 48.0 (#200)
| Benchmark | GPT-5.6 Terra | Qwen Max |
|---|---|---|
| LMArena Text | 1446 | 1282 |
| LMArena Creative Writing | 1411 | 1248 |
| LMArena Multi-Turn | 1451 | 1277 |
| EQ-Bench Creative Writing | 1855 | — |
| EQ-Bench 4 | 1234 | — |
Frequently asked questions
Is GPT-5.6 Terra better than Qwen Max?
GPT-5.6 Terra is the stronger model overall, scoring 59.1 to 34.4 on the Noometry Index. Qwen Max costs 1.6× 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 Qwen Max?
Qwen Max is cheaper. It lists at $1.60 per million input tokens and $6.40 per million output tokens; GPT-5.6 Terra lists at $2 and $12.
Is GPT-5.6 Terra or Qwen Max better for coding?
GPT-5.6 Terra scores higher on coding benchmarks: 57.1 versus 30.3 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Terra does, with 1.05M tokens against 33K.
How many benchmarks do GPT-5.6 Terra and Qwen Max share?
19 benchmarks have published results for both models. GPT-5.6 Terra has 52 scored results on Noometry and Qwen Max has 23.