# GPT-4.1 mini vs Qwen2.5-Coder-32B

> GPT-4.1 mini and Qwen2.5-Coder-32B score almost the same on the Noometry Index (33.6 vs 33.4), so choose on price, context window or the category you care about most.

- Canonical page: https://noometry.com/compare/gpt-4-1-mini-vs-qwen2-5-coder-32b
- Last updated: 2026-10-10
- Shared benchmarks: 16

## Summary

- They share 16 benchmarks with published results for both. GPT-4.1 mini scores higher in 5 categories and Qwen2.5-Coder-32B in 3 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in instruction following, where GPT-4.1 mini leads 73.7 to 61.4.
- The biggest single-benchmark swing is Aider Polyglot: 32.4% for GPT-4.1 mini and 16.4% for Qwen2.5-Coder-32B.
- GPT-4.1 mini is cheaper at $0.40 / $1.60 per million input/output tokens, against $0.66 / $1 for Qwen2.5-Coder-32B.
- GPT-4.1 mini accepts more context: 1.05M tokens versus 33K.
- Qwen2.5-Coder-32B has downloadable open weights; the other is API-only.

## Snapshot

| | GPT-4.1 mini | Qwen2.5-Coder-32B |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 33.6 | 33.4 |
| Rank | 240 | 245 |
| Context | 1.05M | 33K |
| Input $/M | $0.40 | $0.66 |
| Output $/M | $1.60 | $1 |
| Weights | Proprietary | Open |

## Coding

- GPT-4.1 mini: 30.6 (#293)
- Qwen2.5-Coder-32B: 22.6 (#333)

| Benchmark | GPT-4.1 mini | Qwen2.5-Coder-32B |
|---|---|---|
| SWE-bench Verified (bash only) | 23.9% | 9% |
| Aider Polyglot | 32.4% | 16.4% |
| BigCodeBench Instruct | 48.9% | 49% |
| LMArena Coding | 1367 | 1276 |
| SciCode | 40.4% | — |
| WeirdML | 37.6% | — |
| LiveBench Coding | — | 56.9% |
| BigCodeBench Complete | — | 58% |
| CadEval | 16% | — |
| HumanEval+ | — | 87.2% |
| MBPP+ | — | 77% |

## Agentic & Tool Use

- GPT-4.1 mini: 33.3 (#55)
- Qwen2.5-Coder-32B: —

| Benchmark | GPT-4.1 mini | Qwen2.5-Coder-32B |
|---|---|---|
| Berkeley Function Calling Leaderboard | 50.5% | — |

## Reasoning

- GPT-4.1 mini: 10.8 (#340)
- Qwen2.5-Coder-32B: 21.2 (#225)

| Benchmark | GPT-4.1 mini | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Hard Prompts | 1349 | 1251 |
| Epoch Capabilities Index | 135.01 | 119.49 |
| ARC-AGI-2 | 0% | — |
| Kagi LLM Benchmark | 48.6% | — |
| ARC-AGI-1 | 3.5% | — |
| CritPt | 0% | — |
| Chess Puzzles | 7% | — |
| LiveBench Reasoning | — | 42.1% |
| Mystery Game Puzzles | 7% | — |
| DTBench | 68.8% | — |
| LiveBench Data Analysis | — | 49.9% |
| LMCA | 21.1% | — |
| HellaSwag | — | 83% |
| LiveBench | — | 46.2% |
| WinoGrande | — | 80.8% |

## Math

- GPT-4.1 mini: 24.1 (#270)
- Qwen2.5-Coder-32B: 33.3 (#204)

| Benchmark | GPT-4.1 mini | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Math | 1343 | 1251 |
| FrontierMath (Tiers 1-3) | 6.7% | — |
| OTIS Mock AIME 2024-2025 | 44.7% | — |
| Omni-MATH | 49.1% | — |
| LiveBench Math | — | 46.6% |
| MATH Level 5 | 87.3% | — |
| FrontierMath (Feb 2025 set) | 4.5% | — |
| GSM8K | — | 93% |

## Knowledge

- GPT-4.1 mini: 34.7 (#194)
- Qwen2.5-Coder-32B: 33.4 (#203)

| Benchmark | GPT-4.1 mini | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Expert | 1338 | 1221 |
| GPQA Diamond | 65.8% | — |
| SimpleQA Verified | 12.7% | — |
| MMLU-Pro | 78.3% | — |
| GPQA (HELM) | 61.4% | — |
| ARC (AI2) Challenge | — | 70.5% |
| MMLU | — | 79.1% |

## Multimodal

- GPT-4.1 mini: 35.8 (#82)
- Qwen2.5-Coder-32B: —

| Benchmark | GPT-4.1 mini | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Vision | 1181 | — |

## Multilingual

- GPT-4.1 mini: 45.7 (#166)
- Qwen2.5-Coder-32B: 37.8 (#235)

| Benchmark | GPT-4.1 mini | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Non-English | 1318 | 1205 |
| LMArena Chinese | 1329 | 1222 |
| LMArena Russian | 1324 | 1228 |
| LMArena French | 1358 | — |
| LMArena German | 1351 | — |
| LMArena Japanese | 1290 | — |
| LMArena Korean | 1298 | — |
| LMArena Spanish | 1319 | — |

## Instruction Following

- GPT-4.1 mini: 73.7 (#118)
- Qwen2.5-Coder-32B: 61.4 (#245)

| Benchmark | GPT-4.1 mini | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Instruction Following | 1333 | 1223 |
| LiveBench Instruction Following | — | 58.7% |
| IFEval | 90.4% | — |

## Long Context

- GPT-4.1 mini: 31.8 (#275)
- Qwen2.5-Coder-32B: 38.0 (#208)

| Benchmark | GPT-4.1 mini | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Longer Query | 1344 | 1251 |
| Fiction.LiveBench | 44.4% | — |

## Writing & Preference

- GPT-4.1 mini: 48.6 (#199)
- Qwen2.5-Coder-32B: 41.6 (#240)

| Benchmark | GPT-4.1 mini | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Text | 1340 | 1230 |
| LMArena Creative Writing | 1300 | 1174 |
| LMArena Multi-Turn | 1354 | 1222 |
| EQ-Bench Creative Writing | 1147 | — |
| WildBench | 83.8% | — |
| LiveBench Language | — | 23.3% |

## FAQ

### Is GPT-4.1 mini better than Qwen2.5-Coder-32B?

GPT-4.1 mini and Qwen2.5-Coder-32B score almost the same on the Noometry Index (33.6 vs 33.4), so choose on price, context window or the category you care about most.

### Which is cheaper, GPT-4.1 mini or Qwen2.5-Coder-32B?

GPT-4.1 mini is cheaper. It lists at $0.40 per million input tokens and $1.60 per million output tokens; Qwen2.5-Coder-32B lists at $0.66 and $1.

### Is GPT-4.1 mini or Qwen2.5-Coder-32B better for coding?

GPT-4.1 mini scores higher on coding benchmarks: 30.6 versus 22.6 in the Noometry coding category.

### Which has the bigger context window?

GPT-4.1 mini does, with 1.05M tokens against 33K.

### How many benchmarks do GPT-4.1 mini and Qwen2.5-Coder-32B share?

16 benchmarks have published results for both models. GPT-4.1 mini has 47 scored results on Noometry and Qwen2.5-Coder-32B has 31.