# o1 vs Qwen2.5 7B Instruct

> o1 is the stronger model overall, scoring 40.6 to 28.6 on the Noometry Index. Qwen2.5 7B Instruct costs 86× less per token, which makes it the better buy when o1's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/o1-vs-qwen2-5-7b-instruct
- Last updated: 2026-10-10
- Shared benchmarks: 6

## Summary

- They share 6 benchmarks with published results for both. o1 scores higher in 7 categories and Qwen2.5 7B Instruct in 0 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where o1 leads 41.5 to 17.0.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 73.3% for o1 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 $15 / $60 for o1.
- o1 accepts more context: 200K tokens versus 131K.
- Qwen2.5 7B Instruct has downloadable open weights; the other is API-only.

## Snapshot

| | o1 | Qwen2.5 7B Instruct |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 40.6 | 28.6 |
| Rank | 140 | 314 |
| Context | 200K | 131K |
| Input $/M | $15 | $0.17 |
| Output $/M | $60 | $0.70 |
| Weights | Proprietary | Open |

## Coding

- o1: 45.7 (#64)
- Qwen2.5 7B Instruct: 35.9 (#204)

| Benchmark | o1 | Qwen2.5 7B Instruct |
|---|---|---|
| Aider Polyglot | 61.7% | — |
| WeirdML | 47.6% | — |
| BigCodeBench Instruct | — | 37.6% |
| LiveBench Coding | 69.7% | — |
| LMArena Coding | 1367 | — |
| BigCodeBench Complete | — | 46.1% |
| CadEval | 56% | — |
| HumanEval+ | 89% | — |
| MBPP+ | 80.2% | — |

## Agentic & Tool Use

- o1: 24.3 (#111)
- Qwen2.5 7B Instruct: 23.5 (#117)

| Benchmark | o1 | Qwen2.5 7B Instruct |
|---|---|---|
| Cybench | 10% | — |
| BALROG | — | 7.8% |
| METR Time Horizons | 51.1% | — |

## Reasoning

- o1: 27.5 (#103)
- Qwen2.5 7B Instruct: 14.3 (#317)

| Benchmark | o1 | Qwen2.5 7B Instruct |
|---|---|---|
| Chess Puzzles | 15% | 0% |
| DTBench | 74.7% | 47.7% |
| LMCA | 22.3% | 6.4% |
| Epoch Capabilities Index | 141.92 | 118.44 |
| SimpleBench | 41.7% | — |
| ARC-AGI-1 | 30.7% | — |
| EnigmaEval | 5.7% | — |
| LiveBench Reasoning | 91.6% | — |
| LMArena Hard Prompts | 1372 | — |
| LiveBench Data Analysis | 65.5% | — |
| LiveBench | 75.7% | — |

## Math

- o1: 34.9 (#150)
- Qwen2.5 7B Instruct: 11.8 (#300)

| Benchmark | o1 | Qwen2.5 7B Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 73.3% | 2.5% |
| FrontierMath (Tiers 1-3) | 14.7% | — |
| Omni-MATH | — | 29.4% |
| LiveBench Math | 80.3% | — |
| LMArena Math | 1388 | — |
| MATH Level 5 | 94.7% | — |
| FrontierMath (Feb 2025 set) | 9.3% | — |

## Knowledge

- o1: 41.5 (#101)
- Qwen2.5 7B Instruct: 17.0 (#281)

| Benchmark | o1 | Qwen2.5 7B Instruct |
|---|---|---|
| GPQA Diamond | 76.8% | 35.5% |
| Humanity's Last Exam | 8% | — |
| SimpleQA Verified | 41.1% | — |
| MMLU-Pro | — | 53.9% |
| Confabulations | 11.7% | — |
| GPQA (HELM) | — | 34.1% |
| LMArena Expert | 1362 | — |
| MMLU | — | 72.9% |

## Multimodal

- o1: 33.6 (#87)
- Qwen2.5 7B Instruct: —

| Benchmark | o1 | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Vision | 1168 | — |
| GeoBench | 80% | — |
| VPCT | 37% | — |
| SpatialViz-Bench | 41.4% | — |

## Multilingual

- o1: 48.8 (#137)
- Qwen2.5 7B Instruct: —

| Benchmark | o1 | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Non-English | 1359 | — |
| LMArena Chinese | 1394 | — |
| LMArena French | 1343 | — |
| LMArena German | 1337 | — |
| LMArena Japanese | 1346 | — |
| LMArena Korean | 1396 | — |
| LMArena Russian | 1356 | — |
| LMArena Spanish | 1345 | — |

## Instruction Following

- o1: 75.0 (#82)
- Qwen2.5 7B Instruct: 63.4 (#226)

| Benchmark | o1 | Qwen2.5 7B Instruct |
|---|---|---|
| LiveBench Instruction Following | 81.5% | — |
| IFEval | — | 74.1% |
| LMArena Instruction Following | 1368 | — |

## Long Context

- o1: 50.3 (#9)
- Qwen2.5 7B Instruct: —

| Benchmark | o1 | Qwen2.5 7B Instruct |
|---|---|---|
| Fiction.LiveBench | 83.3% | — |
| LMArena Longer Query | 1378 | — |

## Writing & Preference

- o1: 55.8 (#139)
- Qwen2.5 7B Instruct: 48.8 (#194)

| Benchmark | o1 | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Text | 1366 | — |
| LMArena Creative Writing | 1347 | — |
| Short-Story Creative Writing | 70.2% | — |
| WildBench | — | 73.1% |
| LMArena Multi-Turn | 1368 | — |
| LiveBench Language | 65.4% | — |

## FAQ

### Is o1 better than Qwen2.5 7B Instruct?

o1 is the stronger model overall, scoring 40.6 to 28.6 on the Noometry Index. Qwen2.5 7B Instruct costs 86× less per token, which makes it the better buy when o1's lead doesn't matter for your workload.

### Which is cheaper, o1 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; o1 lists at $15 and $60.

### Is o1 or Qwen2.5 7B Instruct better for coding?

o1 scores higher on coding benchmarks: 45.7 versus 35.9 in the Noometry coding category.

### Which has the bigger context window?

o1 does, with 200K tokens against 131K.

### How many benchmarks do o1 and Qwen2.5 7B Instruct share?

6 benchmarks have published results for both models. o1 has 52 scored results on Noometry and Qwen2.5 7B Instruct has 15.