Model comparison
o4-mini vs Qwen3.5-9B
o4-mini is the stronger model overall, scoring 41.6 to 33.3 on the Noometry Index. Qwen3.5-9B costs 17× less per token, which makes it the better buy when o4-mini's lead doesn't matter for your workload.
Last verified . 7 shared benchmarks.
Alibaba (Qwen)Summary
- They share 7 benchmarks with published results for both. o4-mini scores higher in 4 categories and Qwen3.5-9B in 1 category; 5 gaps are clear of the uncertainty.
- The widest gap is in agentic & tool use, where o4-mini leads 32.3 to 14.4.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 81.7% for o4-mini and 61.7% for Qwen3.5-9B.
- Qwen3.5-9B is cheaper at $0.10 / $0.15 per million input/output tokens, against $1.10 / $4.40 for o4-mini.
- Qwen3.5-9B accepts more context: 262K tokens versus 200K.
- Qwen3.5-9B has downloadable open weights; the other is API-only.
Side by side
| o4-mini | Qwen3.5-9B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 41.6 | 33.3 |
| Released | 2025-04-16 | 2026-02-23 |
| Weights | Proprietary | Open |
| Context window | 200K | 262K |
| Max output | 100K | 66K |
| Input $ / M tokens | $1.10 | $0.10 |
| Output $ / M tokens | $4.40 | $0.15 |
| Results tracked | 60 | 10 |
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Category by category
Coding o4-mini leads
o4-mini: 41.6 (#110), Qwen3.5-9B: 35.3 (#213)
| Benchmark | o4-mini | Qwen3.5-9B |
|---|---|---|
| SWE-bench Verified (bash only) | 45% | — |
| Aider Polyglot | 72% | — |
| SciCode | — | 27.5% |
| GSO | 3.6% | — |
| WeirdML | 52.6% | — |
| LMArena Coding | 1369 | — |
| CadEval | 62% | — |
| ALE-Bench | 826.17 | — |
| AlgoTune | 1.72 | — |
Agentic & Tool Use o4-mini leads
o4-mini: 32.3 (#57), Qwen3.5-9B: 14.4 (#143)
| Benchmark | o4-mini | Qwen3.5-9B |
|---|---|---|
| Terminal-Bench | — | 9.2% |
| Berkeley Function Calling Leaderboard | 53.2% | — |
| GDPval | 25.3% | — |
| METR Time Horizons | 63.9% | — |
Reasoning o4-mini leads
o4-mini: 24.6 (#143), Qwen3.5-9B: 22.5 (#178)
| Benchmark | o4-mini | Qwen3.5-9B |
|---|---|---|
| CritPt | 0.6% | 0.3% |
| Chess Puzzles | 26% | 12% |
| DTBench | 77.6% | 71.2% |
| LMCA | 26.5% | 24.5% |
| Epoch Capabilities Index | 145.63 | 139.45 |
| ARC-AGI-2 | 6.1% | — |
| SimpleBench | 38.7% | — |
| Kagi LLM Benchmark | 67.6% | — |
| ARC-AGI-1 | 58.7% | — |
| EnigmaEval | 9.2% | — |
| LMArena Hard Prompts | 1351 | — |
| Mystery Game Puzzles | 5% | — |
| ForecastBench | 61.8 | — |
Math o4-mini leads
o4-mini: 40.2 (#79), Qwen3.5-9B: 33.5 (#174)
| Benchmark | o4-mini | Qwen3.5-9B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 81.7% | 61.7% |
| FrontierMath (Tiers 1-3) | 36.1% | — |
| FrontierMath Tier 4 | 4.9% | — |
| MathArena Final-Answer Competitions | — | 48.5% |
| Omni-MATH | 72% | — |
| LMArena Math | 1390 | — |
| MATH Level 5 | 97.8% | — |
| FrontierMath (Feb 2025 set) | 24.8% | — |
| FrontierMath Tier 4 (v1) | 6.3% | — |
Knowledge Qwen3.5-9B leads
o4-mini: 43.5 (#83), Qwen3.5-9B: 46.0 (#77)
| Benchmark | o4-mini | Qwen3.5-9B |
|---|---|---|
| GPQA Diamond | 79.6% | 79% |
| Humanity's Last Exam | 18.1% | — |
| SimpleQA Verified | 19.6% | — |
| MMLU-Pro | 82% | — |
| Confabulations | 15.8% | — |
| Vectara Hallucination Rate | 18.6% | — |
| GPQA (HELM) | 73.5% | — |
| LMArena Expert | 1344 | — |
Multimodal Not comparable
o4-mini: 39.5 (#46), Qwen3.5-9B: —
| Benchmark | o4-mini | Qwen3.5-9B |
|---|---|---|
| LMArena Vision | 1194 | — |
| GeoBench | 64% | — |
| VPCT | 57.5% | — |
Multilingual Not comparable
o4-mini: 47.2 (#149), Qwen3.5-9B: —
| Benchmark | o4-mini | Qwen3.5-9B |
|---|---|---|
| LMArena Non-English | 1336 | — |
| LMArena Chinese | 1355 | — |
| LMArena French | 1362 | — |
| LMArena German | 1336 | — |
| LMArena Japanese | 1308 | — |
| LMArena Korean | 1313 | — |
| LMArena Russian | 1334 | — |
| LMArena Spanish | 1346 | — |
Instruction Following Not comparable
o4-mini: 75.4 (#63), Qwen3.5-9B: —
| Benchmark | o4-mini | Qwen3.5-9B |
|---|---|---|
| IFEval | 92.8% | — |
| LMArena Instruction Following | 1321 | — |
Long Context Not comparable
o4-mini: 45.5 (#31), Qwen3.5-9B: —
| Benchmark | o4-mini | Qwen3.5-9B |
|---|---|---|
| Fiction.LiveBench | 77.8% | — |
| LMArena Longer Query | 1315 | — |
Writing & Preference Not comparable
o4-mini: 54.2 (#148), Qwen3.5-9B: —
| Benchmark | o4-mini | Qwen3.5-9B |
|---|---|---|
| LMArena Text | 1353 | — |
| LMArena Creative Writing | 1294 | — |
| Short-Story Creative Writing | 75% | — |
| WildBench | 85.4% | — |
| LMArena Multi-Turn | 1350 | — |
Frequently asked questions
Is o4-mini better than Qwen3.5-9B?
o4-mini is the stronger model overall, scoring 41.6 to 33.3 on the Noometry Index. Qwen3.5-9B costs 17× less per token, which makes it the better buy when o4-mini's lead doesn't matter for your workload.
Which is cheaper, o4-mini or Qwen3.5-9B?
Qwen3.5-9B is cheaper. It lists at $0.10 per million input tokens and $0.15 per million output tokens; o4-mini lists at $1.10 and $4.40.
Is o4-mini or Qwen3.5-9B better for coding?
o4-mini scores higher on coding benchmarks: 41.6 versus 35.3 in the Noometry coding category.
Which has the bigger context window?
Qwen3.5-9B does, with 262K tokens against 200K.
How many benchmarks do o4-mini and Qwen3.5-9B share?
7 benchmarks have published results for both models. o4-mini has 60 scored results on Noometry and Qwen3.5-9B has 10.