Model comparison
o1 vs Qwen2.5 7B Instruct
o1 is the stronger model overall, scoring 40.9 to 29.0 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.
Last verified . 6 shared benchmarks.
Alibaba (Qwen)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.
Side by side
| o1 | Qwen2.5 7B Instruct | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 40.9 | 29.0 |
| Released | 2024-09-12 | 2024-09 |
| Weights | Proprietary | Open |
| Context window | 200K | 131K |
| Max output | 100K | 8K |
| Input $ / M tokens | $15 | $0.17 |
| Output $ / M tokens | $60 | $0.70 |
| Results tracked | 52 | 15 |
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Category by category
Coding o1 leads
o1: 46.1 (#70), Qwen2.5 7B Instruct: 36.5 (#208)
| 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 Too close to call
o1: 24.6 (#117), Qwen2.5 7B Instruct: 23.8 (#124)
| Benchmark | o1 | Qwen2.5 7B Instruct |
|---|---|---|
| Cybench | 10% | — |
| BALROG | — | 7.8% |
| METR Time Horizons | 51.1% | — |
Reasoning o1 leads
o1: 27.9 (#111), Qwen2.5 7B Instruct: 14.8 (#322)
| Benchmark | o1 | Qwen2.5 7B Instruct |
|---|---|---|
| Chess Puzzles | 15% | 0% |
| DTBench | 74.7% | 47.7% |
| LMCA | 22.3% | 6.4% |
| Epoch Capabilities Index | 141.91 | 118.51 |
| SimpleBench | 41.7% | — |
| ARC-AGI-1 | 30.7% | — |
| EnigmaEval | 5.7% | — |
| LiveBench Reasoning | 91.6% | — |
| LMArena Hard Prompts | 1371 | — |
| LiveBench Data Analysis | 65.5% | — |
| LiveBench | 75.7% | — |
Math o1 leads
o1: 36.1 (#175), Qwen2.5 7B Instruct: 12.6 (#306)
| 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 leads
o1: 41.5 (#110), Qwen2.5 7B Instruct: 17.0 (#286)
| 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 | 1361 | — |
| MMLU | — | 72.9% |
Multimodal Not comparable
o1: 34.2 (#93), Qwen2.5 7B Instruct: —
| Benchmark | o1 | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Vision | 1168 | — |
| GeoBench | 80% | — |
| VPCT | 37% | — |
| SpatialViz-Bench | 41.4% | — |
Multilingual Not comparable
o1: 48.6 (#142), Qwen2.5 7B Instruct: —
| Benchmark | o1 | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Non-English | 1358 | — |
| LMArena Chinese | 1394 | — |
| LMArena French | 1344 | — |
| LMArena German | 1337 | — |
| LMArena Japanese | 1346 | — |
| LMArena Korean | 1396 | — |
| LMArena Russian | 1356 | — |
| LMArena Spanish | 1345 | — |
Instruction Following o1 leads
o1: 74.8 (#86), Qwen2.5 7B Instruct: 63.2 (#231)
| Benchmark | o1 | Qwen2.5 7B Instruct |
|---|---|---|
| LiveBench Instruction Following | 81.5% | — |
| IFEval | — | 74.1% |
| LMArena Instruction Following | 1367 | — |
Long Context Not comparable
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 leads
o1: 55.6 (#144), Qwen2.5 7B Instruct: 48.8 (#195)
| Benchmark | o1 | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Text | 1366 | — |
| LMArena Creative Writing | 1348 | — |
| Short-Story Creative Writing | 70.2% | — |
| WildBench | — | 73.1% |
| LMArena Multi-Turn | 1369 | — |
| LiveBench Language | 65.4% | — |
Frequently asked questions
Is o1 better than Qwen2.5 7B Instruct?
o1 is the stronger model overall, scoring 40.9 to 29.0 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: 46.1 versus 36.5 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.