Model comparison
GPT-5 Nano vs Qwen2.5 7B Instruct
GPT-5 Nano is the stronger model overall, scoring 33.4 to 28.6 on the Noometry Index.
Last verified . 11 shared benchmarks.
Alibaba (Qwen)Summary
- They share 11 benchmarks with published results for both. GPT-5 Nano scores higher in 5 categories and Qwen2.5 7B Instruct in 2 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GPT-5 Nano leads 35.8 to 17.0.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 81.1% for GPT-5 Nano and 2.5% for Qwen2.5 7B Instruct.
- GPT-5 Nano is cheaper at $0.05 / $0.40 per million input/output tokens, against $0.17 / $0.70 for Qwen2.5 7B Instruct.
- GPT-5 Nano accepts more context: 400K tokens versus 131K.
- Qwen2.5 7B Instruct has downloadable open weights; the other is API-only.
Side by side
| GPT-5 Nano | Qwen2.5 7B Instruct | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 33.4 | 28.6 |
| Released | 2025-08-07 | 2024-09 |
| Weights | Proprietary | Open |
| Context window | 400K | 131K |
| Max output | 128K | 8K |
| Input $ / M tokens | $0.05 | $0.17 |
| Output $ / M tokens | $0.40 | $0.70 |
| Results tracked | 48 | 15 |
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Category by category
Coding Qwen2.5 7B Instruct leads
GPT-5 Nano: 33.3 (#248), Qwen2.5 7B Instruct: 35.9 (#204)
| Benchmark | GPT-5 Nano | Qwen2.5 7B Instruct |
|---|---|---|
| SWE-bench Verified (bash only) | 34.8% | — |
| WeirdML | 38.1% | — |
| BigCodeBench Instruct | — | 37.6% |
| LMArena Coding | 1351 | — |
| BigCodeBench Complete | — | 46.1% |
| ALE-Bench | 718.67 | — |
Agentic & Tool Use GPT-5 Nano leads
GPT-5 Nano: 25.6 (#99), Qwen2.5 7B Instruct: 23.5 (#117)
| Benchmark | GPT-5 Nano | Qwen2.5 7B Instruct |
|---|---|---|
| Terminal-Bench | 21.8% | — |
| Berkeley Function Calling Leaderboard | 51.5% | — |
| BALROG | — | 7.8% |
Reasoning GPT-5 Nano leads
GPT-5 Nano: 16.4 (#291), Qwen2.5 7B Instruct: 14.3 (#317)
| Benchmark | GPT-5 Nano | Qwen2.5 7B Instruct |
|---|---|---|
| Chess Puzzles | 27% | 0% |
| DTBench | 62.7% | 47.7% |
| LMCA | 7.9% | 6.4% |
| Epoch Capabilities Index | 139.39 | 118.44 |
| ARC-AGI-2 | 2.6% | — |
| Kagi LLM Benchmark | 62.2% | — |
| ARC-AGI-1 | 20.7% | — |
| LMArena Hard Prompts | 1329 | — |
| Mystery Game Puzzles | 9% | — |
| ForecastBench | 59.1 | — |
Math GPT-5 Nano leads
GPT-5 Nano: 29.1 (#221), Qwen2.5 7B Instruct: 11.8 (#300)
| Benchmark | GPT-5 Nano | Qwen2.5 7B Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 81.1% | 2.5% |
| Omni-MATH | 54.6% | 29.4% |
| FrontierMath (Tiers 1-3) | 20% | — |
| FrontierMath Tier 4 | 2.4% | — |
| ProofBench | 12% | — |
| LMArena Math | 1318 | — |
| MATH Level 5 | 95.2% | — |
| FrontierMath (Feb 2025 set) | 8.3% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge GPT-5 Nano leads
GPT-5 Nano: 35.8 (#166), Qwen2.5 7B Instruct: 17.0 (#281)
| Benchmark | GPT-5 Nano | Qwen2.5 7B Instruct |
|---|---|---|
| GPQA Diamond | 69.4% | 35.5% |
| MMLU-Pro | 77.8% | 53.9% |
| GPQA (HELM) | 67.9% | 34.1% |
| SimpleQA Verified | 11.7% | — |
| Vectara Hallucination Rate | 10.5% | — |
| LMArena Expert | 1321 | — |
| MMLU | — | 72.9% |
Multimodal Not comparable
GPT-5 Nano: 30.7 (#102), Qwen2.5 7B Instruct: —
| Benchmark | GPT-5 Nano | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Vision | 1159 | — |
| VPCT | 37.2% | — |
Multilingual Not comparable
GPT-5 Nano: 45.5 (#167), Qwen2.5 7B Instruct: —
| Benchmark | GPT-5 Nano | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Non-English | 1313 | — |
| LMArena Chinese | 1355 | — |
| LMArena German | 1329 | — |
| LMArena Korean | 1269 | — |
| LMArena Russian | 1296 | — |
| LMArena Spanish | 1360 | — |
Instruction Following GPT-5 Nano leads
GPT-5 Nano: 75.2 (#72), Qwen2.5 7B Instruct: 63.4 (#226)
| Benchmark | GPT-5 Nano | Qwen2.5 7B Instruct |
|---|---|---|
| IFEval | 93.2% | 74.1% |
| LMArena Instruction Following | 1306 | — |
Long Context Not comparable
GPT-5 Nano: 31.3 (#275), Qwen2.5 7B Instruct: —
| Benchmark | GPT-5 Nano | Qwen2.5 7B Instruct |
|---|---|---|
| Fiction.LiveBench | 44.4% | — |
| LMArena Longer Query | 1313 | — |
Writing & Preference Qwen2.5 7B Instruct leads
GPT-5 Nano: 39.4 (#244), Qwen2.5 7B Instruct: 48.8 (#194)
| Benchmark | GPT-5 Nano | Qwen2.5 7B Instruct |
|---|---|---|
| WildBench | 80.6% | 73.1% |
| LMArena Text | 1320 | — |
| LMArena Creative Writing | 1250 | — |
| EQ-Bench Creative Writing | 705 | — |
| LMArena Multi-Turn | 1311 | — |
Frequently asked questions
Is GPT-5 Nano better than Qwen2.5 7B Instruct?
GPT-5 Nano is the stronger model overall, scoring 33.4 to 28.6 on the Noometry Index.
Which is cheaper, GPT-5 Nano or Qwen2.5 7B Instruct?
GPT-5 Nano is cheaper. It lists at $0.05 per million input tokens and $0.40 per million output tokens; Qwen2.5 7B Instruct lists at $0.17 and $0.70.
Is GPT-5 Nano or Qwen2.5 7B Instruct better for coding?
Qwen2.5 7B Instruct scores higher on coding benchmarks: 35.9 versus 33.3 in the Noometry coding category.
Which has the bigger context window?
GPT-5 Nano does, with 400K tokens against 131K.
How many benchmarks do GPT-5 Nano and Qwen2.5 7B Instruct share?
11 benchmarks have published results for both models. GPT-5 Nano has 48 scored results on Noometry and Qwen2.5 7B Instruct has 15.