Model comparison
o3 vs Qwen2.5 7B Instruct
o3 is the stronger model overall, scoring 47.5 to 29.0 on the Noometry Index. Qwen2.5 7B Instruct costs 11× less per token, which makes it the better buy when o3's lead doesn't matter for your workload.
Last verified . 11 shared benchmarks.
Alibaba (Qwen)Summary
- They share 11 benchmarks with published results for both. o3 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 math, where o3 leads 50.2 to 12.6.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 84.4% for o3 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 $2 / $8 for o3.
- o3 accepts more context: 200K tokens versus 131K.
- Qwen2.5 7B Instruct has downloadable open weights; the other is API-only.
Side by side
| o3 | Qwen2.5 7B Instruct | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 47.5 | 29.0 |
| Released | 2025-04-16 | 2024-09 |
| Weights | Proprietary | Open |
| Context window | 200K | 131K |
| Max output | 100K | 8K |
| Input $ / M tokens | $2 | $0.17 |
| Output $ / M tokens | $8 | $0.70 |
| Results tracked | 63 | 15 |
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Category by category
Coding o3 leads
o3: 46.8 (#64), Qwen2.5 7B Instruct: 36.5 (#208)
| Benchmark | o3 | Qwen2.5 7B Instruct |
|---|---|---|
| SWE-bench Verified | 62.3% | — |
| SWE-bench Verified (bash only) | 58.4% | — |
| Aider Polyglot | 81.3% | — |
| GSO | 8.8% | — |
| WeirdML | 52.4% | — |
| BigCodeBench Instruct | — | 37.6% |
| LMArena Coding | 1408 | — |
| BigCodeBench Complete | — | 46.1% |
| CadEval | 74% | — |
| ALE-Bench | 933.55 | — |
Agentic & Tool Use o3 leads
o3: 34.5 (#44), Qwen2.5 7B Instruct: 23.8 (#124)
| Benchmark | o3 | Qwen2.5 7B Instruct |
|---|---|---|
| Berkeley Function Calling Leaderboard | 63% | — |
| GDPval | 30.8% | — |
| DeepResearch Bench | 45.2% | — |
| OSWorld | 23% | — |
| BALROG | — | 7.8% |
| LMArena Search | 1144 | — |
| METR Time Horizons | 65.4% | — |
Reasoning o3 leads
o3: 32.0 (#78), Qwen2.5 7B Instruct: 14.8 (#322)
| Benchmark | o3 | Qwen2.5 7B Instruct |
|---|---|---|
| Chess Puzzles | 38% | 0% |
| DTBench | 84.8% | 47.7% |
| LMCA | 39.7% | 6.4% |
| Epoch Capabilities Index | 146.86 | 118.51 |
| ARC-AGI-2 | 6.5% | — |
| SimpleBench | 53.1% | — |
| Kagi LLM Benchmark | 67.6% | — |
| ARC-AGI-1 | 60.8% | — |
| CritPt | 1.4% | — |
| EnigmaEval | 13.1% | — |
| LMArena Hard Prompts | 1402 | — |
| Mystery Game Puzzles | 29% | — |
| ForecastBench | 62.5 | — |
Math o3 leads
o3: 50.2 (#58), Qwen2.5 7B Instruct: 12.6 (#306)
| Benchmark | o3 | Qwen2.5 7B Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 84.4% | 2.5% |
| Omni-MATH | 71.4% | 29.4% |
| FrontierMath (Tiers 1-3) | 33.3% | — |
| LMArena Math | 1426 | — |
| MATH Level 5 | 97.8% | — |
| FrontierMath (Feb 2025 set) | 18.7% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge o3 leads
o3: 54.6 (#52), Qwen2.5 7B Instruct: 17.0 (#286)
| Benchmark | o3 | Qwen2.5 7B Instruct |
|---|---|---|
| GPQA Diamond | 81.8% | 35.5% |
| MMLU-Pro | 85.9% | 53.9% |
| GPQA (HELM) | 75.3% | 34.1% |
| Humanity's Last Exam | 20.3% | — |
| SimpleQA Verified | 49.4% | — |
| Confabulations | 14.4% | — |
| LMArena Expert | 1402 | — |
| MMLU | — | 72.9% |
Multimodal Not comparable
o3: 41.4 (#36), Qwen2.5 7B Instruct: —
| Benchmark | o3 | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Vision | 1214 | — |
| GeoBench | 74% | — |
| VPCT | 52% | — |
Multilingual Not comparable
o3: 51.7 (#105), Qwen2.5 7B Instruct: —
| Benchmark | o3 | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Non-English | 1401 | — |
| LMArena Chinese | 1437 | — |
| LMArena French | 1430 | — |
| LMArena German | 1420 | — |
| LMArena Japanese | 1403 | — |
| LMArena Korean | 1370 | — |
| LMArena Russian | 1406 | — |
| LMArena Spanish | 1395 | — |
Instruction Following o3 leads
o3: 72.8 (#127), Qwen2.5 7B Instruct: 63.2 (#231)
| Benchmark | o3 | Qwen2.5 7B Instruct |
|---|---|---|
| IFEval | 86.9% | 74.1% |
| LMArena Instruction Following | 1368 | — |
Long Context Not comparable
o3: 53.3 (#6), Qwen2.5 7B Instruct: —
| Benchmark | o3 | Qwen2.5 7B Instruct |
|---|---|---|
| Fiction.LiveBench | 88.9% | — |
| CL-bench | 17.8% | — |
| LMArena Longer Query | 1372 | — |
Writing & Preference o3 leads
o3: 63.5 (#64), Qwen2.5 7B Instruct: 48.8 (#195)
| Benchmark | o3 | Qwen2.5 7B Instruct |
|---|---|---|
| WildBench | 86.1% | 73.1% |
| LMArena Text | 1410 | — |
| LMArena Creative Writing | 1359 | — |
| Short-Story Creative Writing | 83.9% | — |
| EQ-Bench Creative Writing | 1676 | — |
| LMArena Multi-Turn | 1405 | — |
Frequently asked questions
Is o3 better than Qwen2.5 7B Instruct?
o3 is the stronger model overall, scoring 47.5 to 29.0 on the Noometry Index. Qwen2.5 7B Instruct costs 11× less per token, which makes it the better buy when o3's lead doesn't matter for your workload.
Which is cheaper, o3 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 lists at $2 and $8.
Is o3 or Qwen2.5 7B Instruct better for coding?
o3 scores higher on coding benchmarks: 46.8 versus 36.5 in the Noometry coding category.
Which has the bigger context window?
o3 does, with 200K tokens against 131K.
How many benchmarks do o3 and Qwen2.5 7B Instruct share?
11 benchmarks have published results for both models. o3 has 63 scored results on Noometry and Qwen2.5 7B Instruct has 15.