Model comparison
GPT-4.1 mini vs Qwen2.5 7B Instruct
GPT-4.1 mini is the stronger model overall, scoring 33.6 to 29.0 on the Noometry Index. Qwen2.5 7B Instruct costs 2.3× less per token, which makes it the better buy when GPT-4.1 mini's lead doesn't matter for your workload.
Last verified . 12 shared benchmarks.
Alibaba (Qwen)Summary
- They share 12 benchmarks with published results for both. GPT-4.1 mini scores higher in 4 categories and Qwen2.5 7B Instruct in 3 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GPT-4.1 mini leads 34.7 to 17.0.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 44.7% for GPT-4.1 mini 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 $0.40 / $1.60 for GPT-4.1 mini.
- GPT-4.1 mini 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-4.1 mini | Qwen2.5 7B Instruct | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 33.6 | 29.0 |
| Released | 2025-04-14 | 2024-09 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 131K |
| Max output | 33K | 8K |
| Input $ / M tokens | $0.40 | $0.17 |
| Output $ / M tokens | $1.60 | $0.70 |
| Results tracked | 47 | 15 |
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Category by category
Coding Qwen2.5 7B Instruct leads
GPT-4.1 mini: 30.6 (#293), Qwen2.5 7B Instruct: 36.5 (#208)
| Benchmark | GPT-4.1 mini | Qwen2.5 7B Instruct |
|---|---|---|
| BigCodeBench Instruct | 48.9% | 37.6% |
| SWE-bench Verified (bash only) | 23.9% | — |
| Aider Polyglot | 32.4% | — |
| SciCode | 40.4% | — |
| WeirdML | 37.6% | — |
| LMArena Coding | 1367 | — |
| BigCodeBench Complete | — | 46.1% |
| CadEval | 16% | — |
Agentic & Tool Use GPT-4.1 mini leads
GPT-4.1 mini: 33.3 (#55), Qwen2.5 7B Instruct: 23.8 (#124)
| Benchmark | GPT-4.1 mini | Qwen2.5 7B Instruct |
|---|---|---|
| Berkeley Function Calling Leaderboard | 50.5% | — |
| BALROG | — | 7.8% |
Reasoning Qwen2.5 7B Instruct leads
GPT-4.1 mini: 10.8 (#340), Qwen2.5 7B Instruct: 14.8 (#322)
| Benchmark | GPT-4.1 mini | Qwen2.5 7B Instruct |
|---|---|---|
| Chess Puzzles | 7% | 0% |
| DTBench | 68.8% | 47.7% |
| LMCA | 21.1% | 6.4% |
| Epoch Capabilities Index | 135.01 | 118.51 |
| ARC-AGI-2 | 0% | — |
| Kagi LLM Benchmark | 48.6% | — |
| ARC-AGI-1 | 3.5% | — |
| CritPt | 0% | — |
| LMArena Hard Prompts | 1349 | — |
| Mystery Game Puzzles | 7% | — |
Math GPT-4.1 mini leads
GPT-4.1 mini: 24.1 (#270), Qwen2.5 7B Instruct: 12.6 (#306)
| Benchmark | GPT-4.1 mini | Qwen2.5 7B Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 44.7% | 2.5% |
| Omni-MATH | 49.1% | 29.4% |
| FrontierMath (Tiers 1-3) | 6.7% | — |
| LMArena Math | 1343 | — |
| MATH Level 5 | 87.3% | — |
| FrontierMath (Feb 2025 set) | 4.5% | — |
Knowledge GPT-4.1 mini leads
GPT-4.1 mini: 34.7 (#194), Qwen2.5 7B Instruct: 17.0 (#286)
| Benchmark | GPT-4.1 mini | Qwen2.5 7B Instruct |
|---|---|---|
| GPQA Diamond | 65.8% | 35.5% |
| MMLU-Pro | 78.3% | 53.9% |
| GPQA (HELM) | 61.4% | 34.1% |
| SimpleQA Verified | 12.7% | — |
| LMArena Expert | 1338 | — |
| MMLU | — | 72.9% |
Multimodal Not comparable
GPT-4.1 mini: 35.8 (#82), Qwen2.5 7B Instruct: —
| Benchmark | GPT-4.1 mini | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Vision | 1181 | — |
Multilingual Not comparable
GPT-4.1 mini: 45.7 (#166), Qwen2.5 7B Instruct: —
| Benchmark | GPT-4.1 mini | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Non-English | 1318 | — |
| LMArena Chinese | 1329 | — |
| LMArena French | 1358 | — |
| LMArena German | 1351 | — |
| LMArena Japanese | 1290 | — |
| LMArena Korean | 1298 | — |
| LMArena Russian | 1324 | — |
| LMArena Spanish | 1319 | — |
Instruction Following GPT-4.1 mini leads
GPT-4.1 mini: 73.7 (#118), Qwen2.5 7B Instruct: 63.2 (#231)
| Benchmark | GPT-4.1 mini | Qwen2.5 7B Instruct |
|---|---|---|
| IFEval | 90.4% | 74.1% |
| LMArena Instruction Following | 1333 | — |
Long Context Not comparable
GPT-4.1 mini: 31.8 (#275), Qwen2.5 7B Instruct: —
| Benchmark | GPT-4.1 mini | Qwen2.5 7B Instruct |
|---|---|---|
| Fiction.LiveBench | 44.4% | — |
| LMArena Longer Query | 1344 | — |
Writing & Preference Too close to call
GPT-4.1 mini: 48.6 (#199), Qwen2.5 7B Instruct: 48.8 (#195)
| Benchmark | GPT-4.1 mini | Qwen2.5 7B Instruct |
|---|---|---|
| WildBench | 83.8% | 73.1% |
| LMArena Text | 1340 | — |
| LMArena Creative Writing | 1300 | — |
| EQ-Bench Creative Writing | 1147 | — |
| LMArena Multi-Turn | 1354 | — |
Frequently asked questions
Is GPT-4.1 mini better than Qwen2.5 7B Instruct?
GPT-4.1 mini is the stronger model overall, scoring 33.6 to 29.0 on the Noometry Index. Qwen2.5 7B Instruct costs 2.3× less per token, which makes it the better buy when GPT-4.1 mini's lead doesn't matter for your workload.
Which is cheaper, GPT-4.1 mini 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-4.1 mini lists at $0.40 and $1.60.
Is GPT-4.1 mini or Qwen2.5 7B Instruct better for coding?
Qwen2.5 7B Instruct scores higher on coding benchmarks: 36.5 versus 30.6 in the Noometry coding category.
Which has the bigger context window?
GPT-4.1 mini does, with 1.05M tokens against 131K.
How many benchmarks do GPT-4.1 mini and Qwen2.5 7B Instruct share?
12 benchmarks have published results for both models. GPT-4.1 mini has 47 scored results on Noometry and Qwen2.5 7B Instruct has 15.