Model comparison
GPT-4o vs Qwen2.5 7B Instruct
GPT-4o and Qwen2.5 7B Instruct score almost the same on the Noometry Index (28.6 vs 29.0), so choose on price, context window or the category you care about most.
Last verified . 15 shared benchmarks.
Alibaba (Qwen)Summary
- They share 15 benchmarks with published results for both. GPT-4o scores higher in 3 categories and Qwen2.5 7B Instruct in 4 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GPT-4o leads 28.8 to 17.0.
- The biggest single-benchmark swing is BALROG: 32.3% for GPT-4o and 7.8% for Qwen2.5 7B Instruct.
- Qwen2.5 7B Instruct is cheaper at $0.17 / $0.70 per million input/output tokens, against $2.50 / $10 for GPT-4o.
- Qwen2.5 7B Instruct accepts more context: 131K tokens versus 128K.
- Qwen2.5 7B Instruct has downloadable open weights; the other is API-only.
Side by side
| GPT-4o | Qwen2.5 7B Instruct | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 28.6 | 29.0 |
| Released | 2024-05-13 | 2024-09 |
| Weights | Proprietary | Open |
| Context window | 128K | 131K |
| Max output | 16K | 8K |
| Input $ / M tokens | $2.50 | $0.17 |
| Output $ / M tokens | $10 | $0.70 |
| Results tracked | 72 | 15 |
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Category by category
Coding Qwen2.5 7B Instruct leads
GPT-4o: 24.8 (#328), Qwen2.5 7B Instruct: 36.5 (#208)
| Benchmark | GPT-4o | Qwen2.5 7B Instruct |
|---|---|---|
| BigCodeBench Instruct | 51.1% | 37.6% |
| BigCodeBench Complete | 61.1% | 46.1% |
| SWE-bench Verified | 31% | — |
| SWE-bench Verified (bash only) | 21.6% | — |
| Aider Polyglot | 45.3% | — |
| GSO | 0% | — |
| WeirdML | 25.1% | — |
| LiveBench Coding | 51.4% | — |
| LMArena Coding | 1297 | — |
| CadEval | 26% | — |
| HumanEval+ | 87.2% | — |
| MBPP+ | 72.2% | — |
Agentic & Tool Use Qwen2.5 7B Instruct leads
GPT-4o: 21.0 (#141), Qwen2.5 7B Instruct: 23.8 (#124)
| Benchmark | GPT-4o | Qwen2.5 7B Instruct |
|---|---|---|
| BALROG | 32.3% | 7.8% |
| GDPval | 9.9% | — |
| TheAgentCompany | 8.6% | — |
| Cybench | 12.5% | — |
| LMArena Search | 1006 | — |
| METR Time Horizons | 40.8% | — |
Reasoning Qwen2.5 7B Instruct leads
GPT-4o: 9.4 (#343), Qwen2.5 7B Instruct: 14.8 (#322)
| Benchmark | GPT-4o | Qwen2.5 7B Instruct |
|---|---|---|
| Chess Puzzles | 13% | 0% |
| DTBench | 64.5% | 47.7% |
| LMCA | 16.6% | 6.4% |
| Epoch Capabilities Index | 128.97 | 118.51 |
| ARC-AGI-2 | 0% | — |
| SimpleBench | 17.8% | — |
| ARC-AGI-1 | 4.5% | — |
| CritPt | 0% | — |
| EnigmaEval | 0.8% | — |
| LiveBench Reasoning | 55.8% | — |
| LMArena Hard Prompts | 1281 | — |
| LiveBench Data Analysis | 60.9% | — |
| ForecastBench | 57.7 | — |
| LiveBench | 55.3% | — |
Math Qwen2.5 7B Instruct leads
GPT-4o: 10.6 (#312), Qwen2.5 7B Instruct: 12.6 (#306)
| Benchmark | GPT-4o | Qwen2.5 7B Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 6.4% | 2.5% |
| Omni-MATH | 29.3% | 29.4% |
| FrontierMath (Tiers 1-3) | 0.4% | — |
| LiveBench Math | 49.5% | — |
| LMArena Math | 1285 | — |
| MATH Level 5 | 53.3% | — |
| FrontierMath (Feb 2025 set) | 0.3% | — |
Knowledge GPT-4o leads
GPT-4o: 28.8 (#242), Qwen2.5 7B Instruct: 17.0 (#286)
| Benchmark | GPT-4o | Qwen2.5 7B Instruct |
|---|---|---|
| GPQA Diamond | 49.2% | 35.5% |
| MMLU-Pro | 71.3% | 53.9% |
| GPQA (HELM) | 52% | 34.1% |
| MMLU | 88.1% | 72.9% |
| Humanity's Last Exam | 2.7% | — |
| SimpleQA Verified | 26% | — |
| Confabulations | 15.3% | — |
| Vectara Hallucination Rate | 9.6% | — |
| LMArena Expert | 1250 | — |
Multimodal Not comparable
GPT-4o: 34.5 (#91), Qwen2.5 7B Instruct: —
| Benchmark | GPT-4o | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Vision | 1137 | — |
| Video-MME | 71.9% | — |
| GeoBench | 71% | — |
| VPCT | 40% | — |
| ScienceQA | 88.5% | — |
Multilingual Not comparable
GPT-4o: 43.2 (#186), Qwen2.5 7B Instruct: —
| Benchmark | GPT-4o | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Non-English | 1283 | — |
| LMArena Chinese | 1277 | — |
| LMArena French | 1304 | — |
| LMArena German | 1282 | — |
| LMArena Japanese | 1257 | — |
| LMArena Korean | 1234 | — |
| LMArena Russian | 1286 | — |
| LMArena Spanish | 1292 | — |
Instruction Following GPT-4o leads
GPT-4o: 66.6 (#207), Qwen2.5 7B Instruct: 63.2 (#231)
| Benchmark | GPT-4o | Qwen2.5 7B Instruct |
|---|---|---|
| IFEval | 81.7% | 74.1% |
| LiveBench Instruction Following | 68.6% | — |
| LMArena Instruction Following | 1278 | — |
Long Context Not comparable
GPT-4o: 39.4 (#179), Qwen2.5 7B Instruct: —
| Benchmark | GPT-4o | Qwen2.5 7B Instruct |
|---|---|---|
| Fiction.LiveBench | 66.7% | — |
| LMArena Longer Query | 1289 | — |
Writing & Preference GPT-4o leads
GPT-4o: 52.6 (#166), Qwen2.5 7B Instruct: 48.8 (#195)
| Benchmark | GPT-4o | Qwen2.5 7B Instruct |
|---|---|---|
| WildBench | 82.8% | 73.1% |
| LMArena Text | 1300 | — |
| LMArena Creative Writing | 1292 | — |
| Short-Story Creative Writing | 81.8% | — |
| LMArena Multi-Turn | 1302 | — |
| LiveBench Language | 47.6% | — |
Frequently asked questions
Is GPT-4o better than Qwen2.5 7B Instruct?
GPT-4o and Qwen2.5 7B Instruct score almost the same on the Noometry Index (28.6 vs 29.0), so choose on price, context window or the category you care about most.
Which is cheaper, GPT-4o 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-4o lists at $2.50 and $10.
Is GPT-4o or Qwen2.5 7B Instruct better for coding?
Qwen2.5 7B Instruct scores higher on coding benchmarks: 36.5 versus 24.8 in the Noometry coding category.
Which has the bigger context window?
Qwen2.5 7B Instruct does, with 131K tokens against 128K.
How many benchmarks do GPT-4o and Qwen2.5 7B Instruct share?
15 benchmarks have published results for both models. GPT-4o has 72 scored results on Noometry and Qwen2.5 7B Instruct has 15.