Model comparison
GPT-4o mini vs Qwen2.5 7B Instruct
Qwen2.5 7B Instruct is the stronger model overall, scoring 29.0 to 25.5 on the Noometry Index.
Last verified . 15 shared benchmarks.
Alibaba (Qwen)Summary
- They share 15 benchmarks with published results for both. GPT-4o mini scores higher in 2 categories and Qwen2.5 7B Instruct in 5 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in coding, where Qwen2.5 7B Instruct leads 36.5 to 22.0.
- The biggest single-benchmark swing is BigCodeBench Complete: 57.4% for GPT-4o mini and 46.1% for Qwen2.5 7B Instruct.
- GPT-4o mini is cheaper at $0.15 / $0.60 per million input/output tokens, against $0.17 / $0.70 for Qwen2.5 7B Instruct.
- 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 mini | Qwen2.5 7B Instruct | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 25.5 | 29.0 |
| Released | 2024-07-18 | 2024-09 |
| Weights | Proprietary | Open |
| Context window | 128K | 131K |
| Max output | 16K | 8K |
| Input $ / M tokens | $0.15 | $0.17 |
| Output $ / M tokens | $0.60 | $0.70 |
| Results tracked | 60 | 15 |
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Category by category
Coding Qwen2.5 7B Instruct leads
GPT-4o mini: 22.0 (#335), Qwen2.5 7B Instruct: 36.5 (#208)
| Benchmark | GPT-4o mini | Qwen2.5 7B Instruct |
|---|---|---|
| BigCodeBench Instruct | 46.1% | 37.6% |
| BigCodeBench Complete | 57.4% | 46.1% |
| Aider Polyglot | 3.6% | — |
| WeirdML | 11.8% | — |
| LiveBench Coding | 43.1% | — |
| LMArena Coding | 1290 | — |
| HumanEval+ | 83.5% | — |
| MBPP+ | 72.2% | — |
Agentic & Tool Use GPT-4o mini leads
GPT-4o mini: 27.5 (#101), Qwen2.5 7B Instruct: 23.8 (#124)
| Benchmark | GPT-4o mini | Qwen2.5 7B Instruct |
|---|---|---|
| BALROG | 17.4% | 7.8% |
Reasoning Qwen2.5 7B Instruct leads
GPT-4o mini: 8.7 (#347), Qwen2.5 7B Instruct: 14.8 (#322)
| Benchmark | GPT-4o mini | Qwen2.5 7B Instruct |
|---|---|---|
| Chess Puzzles | 0% | 0% |
| DTBench | 54.4% | 47.7% |
| LMCA | 10.4% | 6.4% |
| Epoch Capabilities Index | 126.56 | 118.51 |
| ARC-AGI-2 | 0% | — |
| SimpleBench | 10.7% | — |
| Kagi LLM Benchmark | 28.8% | — |
| LiveBench Reasoning | 32.8% | — |
| LMArena Hard Prompts | 1267 | — |
| Mystery Game Puzzles | 12% | — |
| LiveBench Data Analysis | 50% | — |
| LiveBench | 41.3% | — |
| PIQA | 88.7% | — |
Math Qwen2.5 7B Instruct leads
GPT-4o mini: 10.4 (#314), Qwen2.5 7B Instruct: 12.6 (#306)
| Benchmark | GPT-4o mini | Qwen2.5 7B Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 6.9% | 2.5% |
| Omni-MATH | 28% | 29.4% |
| FrontierMath (Tiers 1-3) | 0.7% | — |
| LiveBench Math | 36.3% | — |
| LMArena Math | 1267 | — |
| MATH Level 5 | 52.6% | — |
| GSM8K | 91.3% | — |
Knowledge Too close to call
GPT-4o mini: 17.7 (#284), Qwen2.5 7B Instruct: 17.0 (#286)
| Benchmark | GPT-4o mini | Qwen2.5 7B Instruct |
|---|---|---|
| GPQA Diamond | 37.7% | 35.5% |
| MMLU-Pro | 60.3% | 53.9% |
| GPQA (HELM) | 36.8% | 34.1% |
| MMLU | 81.8% | 72.9% |
| SimpleQA Verified | 8.3% | — |
| Confabulations | 37.2% | — |
| LMArena Expert | 1235 | — |
| BoolQ | 88.7% | — |
Multimodal Not comparable
GPT-4o mini: 25.9 (#122), Qwen2.5 7B Instruct: —
| Benchmark | GPT-4o mini | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Vision | 1066 | — |
| Video-MME | 64.8% | — |
| GeoBench | 64% | — |
| VPCT | 34% | — |
Multilingual Not comparable
GPT-4o mini: 42.0 (#199), Qwen2.5 7B Instruct: —
| Benchmark | GPT-4o mini | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Non-English | 1266 | — |
| LMArena Chinese | 1265 | — |
| LMArena French | 1297 | — |
| LMArena German | 1272 | — |
| LMArena Japanese | 1216 | — |
| LMArena Korean | 1195 | — |
| LMArena Russian | 1275 | — |
| LMArena Spanish | 1276 | — |
Instruction Following Qwen2.5 7B Instruct leads
GPT-4o mini: 61.9 (#239), Qwen2.5 7B Instruct: 63.2 (#231)
| Benchmark | GPT-4o mini | Qwen2.5 7B Instruct |
|---|---|---|
| IFEval | 78.2% | 74.1% |
| LiveBench Instruction Following | 56.8% | — |
| LMArena Instruction Following | 1258 | — |
Long Context Not comparable
GPT-4o mini: 39.1 (#186), Qwen2.5 7B Instruct: —
| Benchmark | GPT-4o mini | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Longer Query | 1289 | — |
Writing & Preference Qwen2.5 7B Instruct leads
GPT-4o mini: 39.5 (#248), Qwen2.5 7B Instruct: 48.8 (#195)
| Benchmark | GPT-4o mini | Qwen2.5 7B Instruct |
|---|---|---|
| WildBench | 79.1% | 73.1% |
| LMArena Text | 1286 | — |
| LMArena Creative Writing | 1268 | — |
| Short-Story Creative Writing | 67.2% | — |
| EQ-Bench Creative Writing | 873 | — |
| LMArena Multi-Turn | 1285 | — |
| LiveBench Language | 28.6% | — |
Frequently asked questions
Is GPT-4o mini better than Qwen2.5 7B Instruct?
Qwen2.5 7B Instruct is the stronger model overall, scoring 29.0 to 25.5 on the Noometry Index.
Which is cheaper, GPT-4o mini or Qwen2.5 7B Instruct?
GPT-4o mini is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; Qwen2.5 7B Instruct lists at $0.17 and $0.70.
Is GPT-4o mini or Qwen2.5 7B Instruct better for coding?
Qwen2.5 7B Instruct scores higher on coding benchmarks: 36.5 versus 22.0 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 mini and Qwen2.5 7B Instruct share?
15 benchmarks have published results for both models. GPT-4o mini has 60 scored results on Noometry and Qwen2.5 7B Instruct has 15.