Model comparison
GPT-5.6 Terra vs Qwen2.5 7B Instruct
GPT-5.6 Terra is the stronger model overall, scoring 59.2 to 29.0 on the Noometry Index. Qwen2.5 7B Instruct costs 15× less per token, which makes it the better buy when GPT-5.6 Terra's lead doesn't matter for your workload.
Last verified . 7 shared benchmarks.
Alibaba (Qwen)Summary
- They share 7 benchmarks with published results for both. GPT-5.6 Terra scores higher in 7 categories and Qwen2.5 7B Instruct in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Terra leads 81.6 to 12.6.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 99.7% for GPT-5.6 Terra and 2.5% for Qwen2.5 7B Instruct.
- Qwen2.5 7B Instruct is cheaper at $0.17 / $0.70 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.
- Qwen2.5 7B Instruct has downloadable open weights; the other is API-only.
Side by side
| GPT-5.6 Terra | Qwen2.5 7B Instruct | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 59.2 | 29.0 |
| Released | 2026-07-09 | 2024-09 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 131K |
| Max output | 128K | 8K |
| Input $ / M tokens | $2 | $0.17 |
| Output $ / M tokens | $12 | $0.70 |
| Results tracked | 52 | 15 |
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Category by category
Coding GPT-5.6 Terra leads
GPT-5.6 Terra: 57.7 (#19), Qwen2.5 7B Instruct: 36.5 (#208)
| Benchmark | GPT-5.6 Terra | Qwen2.5 7B Instruct |
|---|---|---|
| DeepSWE | 69.6% | — |
| FrontierCode | 41.3% | — |
| CursorBench | 41.3% | — |
| LMArena WebDev | 1522 | — |
| SciCode | 55% | — |
| WeirdML | 78.3% | — |
| BigCodeBench Instruct | — | 37.6% |
| LMArena Coding | 1484 | — |
| BigCodeBench Complete | — | 46.1% |
| ALE-Bench | 1,951 | — |
Agentic & Tool Use GPT-5.6 Terra leads
GPT-5.6 Terra: 40.1 (#25), Qwen2.5 7B Instruct: 23.8 (#124)
| Benchmark | GPT-5.6 Terra | Qwen2.5 7B Instruct |
|---|---|---|
| BALROG | 53.2% | 7.8% |
| APEX-Agents | 58.2% | — |
| GDP.pdf | 24.7% | — |
| Vending-Bench 2 | 7,343 | — |
Reasoning GPT-5.6 Terra leads
GPT-5.6 Terra: 60.7 (#21), Qwen2.5 7B Instruct: 14.8 (#322)
| Benchmark | GPT-5.6 Terra | Qwen2.5 7B Instruct |
|---|---|---|
| Chess Puzzles | 54% | 0% |
| DTBench | 93.3% | 47.7% |
| LMCA | 55% | 6.4% |
| Epoch Capabilities Index | 159.62 | 118.51 |
| 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% | — |
| LMArena Hard Prompts | 1468 | — |
| Mystery Game Puzzles | 35% | — |
| Surface Evolver Bench | 83.8% | — |
Math GPT-5.6 Terra leads
GPT-5.6 Terra: 81.6 (#12), Qwen2.5 7B Instruct: 12.6 (#306)
| Benchmark | GPT-5.6 Terra | Qwen2.5 7B Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 99.7% | 2.5% |
| FrontierMath (Tiers 1-3) | 86% | — |
| FrontierMath Tier 4 | 70.7% | — |
| ProofBench | 74% | — |
| Omni-MATH | — | 29.4% |
| LMArena Math | 1466 | — |
Knowledge GPT-5.6 Terra leads
GPT-5.6 Terra: 61.2 (#30), Qwen2.5 7B Instruct: 17.0 (#286)
| Benchmark | GPT-5.6 Terra | Qwen2.5 7B Instruct |
|---|---|---|
| GPQA Diamond | 93.3% | 35.5% |
| SimpleQA Verified | 43.2% | — |
| MMLU-Pro | — | 53.9% |
| GPQA (HELM) | — | 34.1% |
| LMArena Expert | 1492 | — |
| MMLU | — | 72.9% |
Multimodal Not comparable
GPT-5.6 Terra: 47.3 (#11), Qwen2.5 7B Instruct: —
| Benchmark | GPT-5.6 Terra | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Vision | 1271 | — |
| Blueprint-Bench 2 | 30.8% | — |
| Furniture Assembly | 54.2% | — |
| LMArena Document | 1472 | — |
Multilingual Not comparable
GPT-5.6 Terra: 54.4 (#44), Qwen2.5 7B Instruct: —
| Benchmark | GPT-5.6 Terra | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Non-English | 1439 | — |
| LMArena Chinese | 1513 | — |
| LMArena French | 1471 | — |
| LMArena German | 1460 | — |
| LMArena Japanese | 1457 | — |
| LMArena Korean | 1425 | — |
| LMArena Russian | 1450 | — |
| LMArena Spanish | 1448 | — |
Instruction Following GPT-5.6 Terra leads
GPT-5.6 Terra: 76.4 (#40), Qwen2.5 7B Instruct: 63.2 (#231)
| Benchmark | GPT-5.6 Terra | Qwen2.5 7B Instruct |
|---|---|---|
| IFEval | — | 74.1% |
| LMArena Instruction Following | 1454 | — |
Long Context Not comparable
GPT-5.6 Terra: 44.4 (#68), Qwen2.5 7B Instruct: —
| Benchmark | GPT-5.6 Terra | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Longer Query | 1451 | — |
Writing & Preference GPT-5.6 Terra leads
GPT-5.6 Terra: 70.2 (#23), Qwen2.5 7B Instruct: 48.8 (#195)
| Benchmark | GPT-5.6 Terra | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Text | 1447 | — |
| LMArena Creative Writing | 1410 | — |
| EQ-Bench Creative Writing | 1855 | — |
| WildBench | — | 73.1% |
| EQ-Bench 4 | 1234 | — |
| LMArena Multi-Turn | 1449 | — |
Frequently asked questions
Is GPT-5.6 Terra better than Qwen2.5 7B Instruct?
GPT-5.6 Terra is the stronger model overall, scoring 59.2 to 29.0 on the Noometry Index. Qwen2.5 7B Instruct costs 15× 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 Qwen2.5 7B Instruct?
Qwen2.5 7B Instruct is cheaper. It lists at $0.17 per million input tokens and $0.70 per million output tokens; GPT-5.6 Terra lists at $2 and $12.
Is GPT-5.6 Terra or Qwen2.5 7B Instruct better for coding?
GPT-5.6 Terra scores higher on coding benchmarks: 57.7 versus 36.5 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 Qwen2.5 7B Instruct share?
7 benchmarks have published results for both models. GPT-5.6 Terra has 52 scored results on Noometry and Qwen2.5 7B Instruct has 15.