# o3-mini vs Qwen2.5 7B Instruct

> o3-mini is the stronger model overall, scoring 36.7 to 28.6 on the Noometry Index. Qwen2.5 7B Instruct costs 6.3× less per token, which makes it the better buy when o3-mini's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/o3-mini-vs-qwen2-5-7b-instruct
- Last updated: 2026-10-10
- Shared benchmarks: 6

## Summary

- They share 6 benchmarks with published results for both. o3-mini 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 knowledge, where o3-mini leads 38.3 to 17.0.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 76.9% for o3-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 $1.10 / $4.40 for o3-mini.
- o3-mini accepts more context: 200K tokens versus 131K.
- Qwen2.5 7B Instruct has downloadable open weights; the other is API-only.

## Snapshot

| | o3-mini | Qwen2.5 7B Instruct |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 36.7 | 28.6 |
| Rank | 205 | 314 |
| Context | 200K | 131K |
| Input $/M | $1.10 | $0.17 |
| Output $/M | $4.40 | $0.70 |
| Weights | Proprietary | Open |

## Coding

- o3-mini: 41.4 (#116)
- Qwen2.5 7B Instruct: 35.9 (#204)

| Benchmark | o3-mini | Qwen2.5 7B Instruct |
|---|---|---|
| Aider Polyglot | 60.4% | — |
| SciCode | 39.8% | — |
| GSO | 1.3% | — |
| WeirdML | 43.7% | — |
| BigCodeBench Instruct | — | 37.6% |
| LiveBench Coding | 82.7% | — |
| LMArena Coding | 1378 | — |
| BigCodeBench Complete | — | 46.1% |
| CadEval | 54% | — |

## Agentic & Tool Use

- o3-mini: 29.3 (#79)
- Qwen2.5 7B Instruct: 23.5 (#117)

| Benchmark | o3-mini | Qwen2.5 7B Instruct |
|---|---|---|
| Cybench | 22.5% | — |
| BALROG | — | 7.8% |

## Reasoning

- o3-mini: 16.3 (#295)
- Qwen2.5 7B Instruct: 14.3 (#317)

| Benchmark | o3-mini | Qwen2.5 7B Instruct |
|---|---|---|
| Chess Puzzles | 17% | 0% |
| DTBench | 68.8% | 47.7% |
| LMCA | 19% | 6.4% |
| Epoch Capabilities Index | 140.35 | 118.44 |
| ARC-AGI-2 | 3% | — |
| SimpleBench | 22.8% | — |
| ARC-AGI-1 | 34.5% | — |
| CritPt | 0.3% | — |
| LiveBench Reasoning | 89.6% | — |
| LMArena Hard Prompts | 1366 | — |
| Mystery Game Puzzles | 7% | — |
| LiveBench Data Analysis | 70.6% | — |
| ForecastBench | 59.6 | — |
| LiveBench | 75.9% | — |

## Math

- o3-mini: 27.6 (#236)
- Qwen2.5 7B Instruct: 11.8 (#300)

| Benchmark | o3-mini | Qwen2.5 7B Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 76.9% | 2.5% |
| FrontierMath (Tiers 1-3) | 18.6% | — |
| FrontierMath Tier 4 | 0% | — |
| Omni-MATH | — | 29.4% |
| LiveBench Math | 77.3% | — |
| LMArena Math | 1396 | — |
| MATH Level 5 | 96.5% | — |
| FrontierMath (Feb 2025 set) | 12.4% | — |
| FrontierMath Tier 4 (v1) | 4.2% | — |

## Knowledge

- o3-mini: 38.3 (#135)
- Qwen2.5 7B Instruct: 17.0 (#281)

| Benchmark | o3-mini | Qwen2.5 7B Instruct |
|---|---|---|
| GPQA Diamond | 77% | 35.5% |
| SimpleQA Verified | 15.3% | — |
| MMLU-Pro | — | 53.9% |
| Confabulations | 17.9% | — |
| GPQA (HELM) | — | 34.1% |
| LMArena Expert | 1364 | — |
| MMLU | — | 72.9% |

## Multilingual

- o3-mini: 45.9 (#159)
- Qwen2.5 7B Instruct: —

| Benchmark | o3-mini | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Non-English | 1319 | — |
| LMArena Chinese | 1379 | — |
| LMArena French | 1333 | — |
| LMArena German | 1303 | — |
| LMArena Japanese | 1286 | — |
| LMArena Korean | 1314 | — |
| LMArena Russian | 1304 | — |
| LMArena Spanish | 1321 | — |

## Instruction Following

- o3-mini: 75.3 (#67)
- Qwen2.5 7B Instruct: 63.4 (#226)

| Benchmark | o3-mini | Qwen2.5 7B Instruct |
|---|---|---|
| LiveBench Instruction Following | 84.4% | — |
| IFEval | — | 74.1% |
| LMArena Instruction Following | 1338 | — |

## Long Context

- o3-mini: 33.8 (#251)
- Qwen2.5 7B Instruct: —

| Benchmark | o3-mini | Qwen2.5 7B Instruct |
|---|---|---|
| Fiction.LiveBench | 50% | — |
| LMArena Longer Query | 1343 | — |

## Writing & Preference

- o3-mini: 50.4 (#177)
- Qwen2.5 7B Instruct: 48.8 (#194)

| Benchmark | o3-mini | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Text | 1337 | — |
| LMArena Creative Writing | 1286 | — |
| Short-Story Creative Writing | 61.7% | — |
| WildBench | — | 73.1% |
| LMArena Multi-Turn | 1320 | — |
| LiveBench Language | 50.7% | — |

## FAQ

### Is o3-mini better than Qwen2.5 7B Instruct?

o3-mini is the stronger model overall, scoring 36.7 to 28.6 on the Noometry Index. Qwen2.5 7B Instruct costs 6.3× less per token, which makes it the better buy when o3-mini's lead doesn't matter for your workload.

### Which is cheaper, o3-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; o3-mini lists at $1.10 and $4.40.

### Is o3-mini or Qwen2.5 7B Instruct better for coding?

o3-mini scores higher on coding benchmarks: 41.4 versus 35.9 in the Noometry coding category.

### Which has the bigger context window?

o3-mini does, with 200K tokens against 131K.

### How many benchmarks do o3-mini and Qwen2.5 7B Instruct share?

6 benchmarks have published results for both models. o3-mini has 51 scored results on Noometry and Qwen2.5 7B Instruct has 15.