Model comparison
GPT-5.6 Luna vs Qwen2.5 7B Instruct
GPT-5.6 Luna is the stronger model overall, scoring 54.6 to 29.0 on the Noometry Index.
Last verified . 7 shared benchmarks.
Alibaba (Qwen)Summary
- They share 7 benchmarks with published results for both. GPT-5.6 Luna 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 Luna leads 77.7 to 12.6.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 98.3% for GPT-5.6 Luna 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 $0.20 / $1.20 for GPT-5.6 Luna.
- GPT-5.6 Luna 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 Luna | Qwen2.5 7B Instruct | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 54.6 | 29.0 |
| Released | 2026-07-09 | 2024-09 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 131K |
| Max output | 128K | 8K |
| Input $ / M tokens | $0.20 | $0.17 |
| Output $ / M tokens | $1.20 | $0.70 |
| Results tracked | 52 | 15 |
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Category by category
Coding GPT-5.6 Luna leads
GPT-5.6 Luna: 54.5 (#28), Qwen2.5 7B Instruct: 36.5 (#208)
| Benchmark | GPT-5.6 Luna | Qwen2.5 7B Instruct |
|---|---|---|
| DeepSWE | 67.2% | — |
| FrontierCode | 39.8% | — |
| CursorBench | 35.9% | — |
| LMArena WebDev | 1519 | — |
| SciCode | 53.6% | — |
| WeirdML | 60.9% | — |
| BigCodeBench Instruct | — | 37.6% |
| LMArena Coding | 1466 | — |
| BigCodeBench Complete | — | 46.1% |
| ALE-Bench | 1,667 | — |
Agentic & Tool Use GPT-5.6 Luna leads
GPT-5.6 Luna: 34.4 (#45), Qwen2.5 7B Instruct: 23.8 (#124)
| Benchmark | GPT-5.6 Luna | Qwen2.5 7B Instruct |
|---|---|---|
| BALROG | 45.6% | 7.8% |
| APEX-Agents | 43% | — |
| GDP.pdf | 22.7% | — |
| Vending-Bench 2 | 4,095 | — |
Reasoning GPT-5.6 Luna leads
GPT-5.6 Luna: 47.6 (#43), Qwen2.5 7B Instruct: 14.8 (#322)
| Benchmark | GPT-5.6 Luna | Qwen2.5 7B Instruct |
|---|---|---|
| Chess Puzzles | 40% | 0% |
| DTBench | 89.1% | 47.7% |
| LMCA | 48.5% | 6.4% |
| Epoch Capabilities Index | 156.39 | 118.51 |
| ARC-AGI-2 | 59.5% | — |
| SimpleBench | 46.8% | — |
| Kagi LLM Benchmark | 49.1% | — |
| NYT Connections (extended) | 69.4% | — |
| ARC-AGI-1 | 88% | — |
| CritPt | 20.6% | — |
| LMArena Hard Prompts | 1451 | — |
| Mystery Game Puzzles | 21% | — |
| Surface Evolver Bench | 61.9% | — |
Math GPT-5.6 Luna leads
GPT-5.6 Luna: 77.7 (#14), Qwen2.5 7B Instruct: 12.6 (#306)
| Benchmark | GPT-5.6 Luna | Qwen2.5 7B Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 98.3% | 2.5% |
| FrontierMath (Tiers 1-3) | 82.1% | — |
| FrontierMath Tier 4 | 61% | — |
| ProofBench | 60% | — |
| Omni-MATH | — | 29.4% |
| LMArena Math | 1458 | — |
Knowledge GPT-5.6 Luna leads
GPT-5.6 Luna: 58.5 (#34), Qwen2.5 7B Instruct: 17.0 (#286)
| Benchmark | GPT-5.6 Luna | Qwen2.5 7B Instruct |
|---|---|---|
| GPQA Diamond | 91.6% | 35.5% |
| SimpleQA Verified | 41% | — |
| MMLU-Pro | — | 53.9% |
| GPQA (HELM) | — | 34.1% |
| LMArena Expert | 1478 | — |
| MMLU | — | 72.9% |
Multimodal Not comparable
GPT-5.6 Luna: 42.7 (#28), Qwen2.5 7B Instruct: —
| Benchmark | GPT-5.6 Luna | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Vision | 1258 | — |
| Blueprint-Bench 2 | 22.6% | — |
| Furniture Assembly | 42.5% | — |
| LMArena Document | 1457 | — |
Multilingual Not comparable
GPT-5.6 Luna: 52.8 (#78), Qwen2.5 7B Instruct: —
| Benchmark | GPT-5.6 Luna | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Non-English | 1417 | — |
| LMArena Chinese | 1470 | — |
| LMArena French | 1456 | — |
| LMArena German | 1454 | — |
| LMArena Japanese | 1411 | — |
| LMArena Korean | 1415 | — |
| LMArena Russian | 1428 | — |
| LMArena Spanish | 1448 | — |
Instruction Following GPT-5.6 Luna leads
GPT-5.6 Luna: 75.6 (#57), Qwen2.5 7B Instruct: 63.2 (#231)
| Benchmark | GPT-5.6 Luna | Qwen2.5 7B Instruct |
|---|---|---|
| IFEval | — | 74.1% |
| LMArena Instruction Following | 1437 | — |
Long Context Not comparable
GPT-5.6 Luna: 43.9 (#82), Qwen2.5 7B Instruct: —
| Benchmark | GPT-5.6 Luna | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Longer Query | 1436 | — |
Writing & Preference GPT-5.6 Luna leads
GPT-5.6 Luna: 68.0 (#29), Qwen2.5 7B Instruct: 48.8 (#195)
| Benchmark | GPT-5.6 Luna | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Text | 1431 | — |
| LMArena Creative Writing | 1396 | — |
| EQ-Bench Creative Writing | 1829 | — |
| WildBench | — | 73.1% |
| EQ-Bench 4 | 1156 | — |
| LMArena Multi-Turn | 1434 | — |
Frequently asked questions
Is GPT-5.6 Luna better than Qwen2.5 7B Instruct?
GPT-5.6 Luna is the stronger model overall, scoring 54.6 to 29.0 on the Noometry Index.
Which is cheaper, GPT-5.6 Luna 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 Luna lists at $0.20 and $1.20.
Is GPT-5.6 Luna or Qwen2.5 7B Instruct better for coding?
GPT-5.6 Luna scores higher on coding benchmarks: 54.5 versus 36.5 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Luna does, with 1.05M tokens against 131K.
How many benchmarks do GPT-5.6 Luna and Qwen2.5 7B Instruct share?
7 benchmarks have published results for both models. GPT-5.6 Luna has 52 scored results on Noometry and Qwen2.5 7B Instruct has 15.