Model comparison
GPT-5.1 vs Qwen2.5 7B Instruct
GPT-5.1 is the stronger model overall, scoring 48.8 to 28.6 on the Noometry Index. Qwen2.5 7B Instruct costs 11× less per token, which makes it the better buy when GPT-5.1's lead doesn't matter for your workload.
Last verified . 11 shared benchmarks.
Alibaba (Qwen)Summary
- They share 11 benchmarks with published results for both. GPT-5.1 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-5.1 leads 50.3 to 11.8.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 88.6% for GPT-5.1 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 $1.25 / $10 for GPT-5.1.
- GPT-5.1 accepts more context: 400K tokens versus 131K.
- Qwen2.5 7B Instruct has downloadable open weights; the other is API-only.
Side by side
| GPT-5.1 | Qwen2.5 7B Instruct | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 48.8 | 28.6 |
| Released | 2025-11-13 | 2024-09 |
| Weights | Proprietary | Open |
| Context window | 400K | 131K |
| Max output | 128K | 8K |
| Input $ / M tokens | $1.25 | $0.17 |
| Output $ / M tokens | $10 | $0.70 |
| Results tracked | 63 | 15 |
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Category by category
Coding GPT-5.1 leads
GPT-5.1: 46.9 (#59), Qwen2.5 7B Instruct: 35.9 (#204)
| Benchmark | GPT-5.1 | Qwen2.5 7B Instruct |
|---|---|---|
| SWE-bench Verified | 68% | — |
| SWE-bench Verified (bash only) | 66% | — |
| LMArena WebDev | 1395 | — |
| SciCode | 43.3% | — |
| GSO | 13.7% | — |
| WeirdML | 60.8% | — |
| BigCodeBench Instruct | — | 37.6% |
| LiveBench Coding | 72.5% | — |
| LMArena Coding | 1454 | — |
| BigCodeBench Complete | — | 46.1% |
| ALE-Bench | 1,192 | — |
Agentic & Tool Use GPT-5.1 leads
GPT-5.1: 32.5 (#55), Qwen2.5 7B Instruct: 23.5 (#117)
| Benchmark | GPT-5.1 | Qwen2.5 7B Instruct |
|---|---|---|
| Terminal-Bench | 47.6% | — |
| DeepResearch Bench | 42.8% | — |
| BALROG | — | 7.8% |
| LMArena Search | 1199 | — |
| Vending-Bench 2 | 1,473 | — |
Reasoning GPT-5.1 leads
GPT-5.1: 39.9 (#50), Qwen2.5 7B Instruct: 14.3 (#317)
| Benchmark | GPT-5.1 | Qwen2.5 7B Instruct |
|---|---|---|
| Chess Puzzles | 32% | 0% |
| DTBench | 90.1% | 47.7% |
| LMCA | 43.9% | 6.4% |
| Epoch Capabilities Index | 149.66 | 118.44 |
| ARC-AGI-2 | 17.6% | — |
| SimpleBench | 53.2% | — |
| ARC-AGI-1 | 72.8% | — |
| CritPt | 4.9% | — |
| EnigmaEval | 11.2% | — |
| LiveBench Reasoning | 95.8% | — |
| LMArena Hard Prompts | 1457 | — |
| Mystery Game Puzzles | 19% | — |
| LiveBench Data Analysis | 72.1% | — |
| ForecastBench | 58.1 | — |
| LiveBench | 78.8% | — |
Math GPT-5.1 leads
GPT-5.1: 50.3 (#45), Qwen2.5 7B Instruct: 11.8 (#300)
| Benchmark | GPT-5.1 | Qwen2.5 7B Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 88.6% | 2.5% |
| Omni-MATH | 46.4% | 29.4% |
| LiveBench Math | 94.5% | — |
| LMArena Math | 1446 | — |
| FrontierMath (Feb 2025 set) | 31% | — |
| FrontierMath Tier 4 (v1) | 12.5% | — |
Knowledge GPT-5.1 leads
GPT-5.1: 50.5 (#65), Qwen2.5 7B Instruct: 17.0 (#281)
| Benchmark | GPT-5.1 | Qwen2.5 7B Instruct |
|---|---|---|
| GPQA Diamond | 87.6% | 35.5% |
| MMLU-Pro | 57.9% | 53.9% |
| GPQA (HELM) | 44.2% | 34.1% |
| Humanity's Last Exam | 23.7% | — |
| SimpleQA Verified | 48% | — |
| Vectara Hallucination Rate | 10.9% | — |
| LMArena Expert | 1470 | — |
| MMLU | — | 72.9% |
Multimodal Not comparable
GPT-5.1: 44.0 (#17), Qwen2.5 7B Instruct: —
| Benchmark | GPT-5.1 | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Vision | 1250 | — |
| VPCT | 58.7% | — |
| LMArena Document | 1403 | — |
Multilingual Not comparable
GPT-5.1: 54.0 (#51), Qwen2.5 7B Instruct: —
| Benchmark | GPT-5.1 | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Non-English | 1431 | — |
| LMArena Chinese | 1495 | — |
| LMArena French | 1451 | — |
| LMArena German | 1439 | — |
| LMArena Japanese | 1453 | — |
| LMArena Korean | 1401 | — |
| LMArena Russian | 1435 | — |
| LMArena Spanish | 1430 | — |
Instruction Following GPT-5.1 leads
GPT-5.1: 84.0 (#1), Qwen2.5 7B Instruct: 63.4 (#226)
| Benchmark | GPT-5.1 | Qwen2.5 7B Instruct |
|---|---|---|
| IFEval | 93.5% | 74.1% |
| LiveBench Instruction Following | 93.3% | — |
| LMArena Instruction Following | 1442 | — |
Long Context Not comparable
GPT-5.1: 47.6 (#14), Qwen2.5 7B Instruct: —
| Benchmark | GPT-5.1 | Qwen2.5 7B Instruct |
|---|---|---|
| CL-bench | 23.7% | — |
| CL-bench Life | 17.3% | — |
| LMArena Longer Query | 1448 | — |
Writing & Preference GPT-5.1 leads
GPT-5.1: 64.7 (#53), Qwen2.5 7B Instruct: 48.8 (#194)
| Benchmark | GPT-5.1 | Qwen2.5 7B Instruct |
|---|---|---|
| WildBench | 86.3% | 73.1% |
| LMArena Text | 1443 | — |
| LMArena Creative Writing | 1427 | — |
| LMArena Multi-Turn | 1449 | — |
| LiveBench Language | 80.2% | — |
Frequently asked questions
Is GPT-5.1 better than Qwen2.5 7B Instruct?
GPT-5.1 is the stronger model overall, scoring 48.8 to 28.6 on the Noometry Index. Qwen2.5 7B Instruct costs 11× less per token, which makes it the better buy when GPT-5.1's lead doesn't matter for your workload.
Which is cheaper, GPT-5.1 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-5.1 lists at $1.25 and $10.
Is GPT-5.1 or Qwen2.5 7B Instruct better for coding?
GPT-5.1 scores higher on coding benchmarks: 46.9 versus 35.9 in the Noometry coding category.
Which has the bigger context window?
GPT-5.1 does, with 400K tokens against 131K.
How many benchmarks do GPT-5.1 and Qwen2.5 7B Instruct share?
11 benchmarks have published results for both models. GPT-5.1 has 63 scored results on Noometry and Qwen2.5 7B Instruct has 15.