Model comparison
o4-mini vs Qwen3.6 27B
o4-mini and Qwen3.6 27B score almost the same on the Noometry Index (41.6 vs 42.2), so choose on price, context window or the category you care about most.
Last verified . 9 shared benchmarks.
Alibaba (Qwen)Summary
- They share 9 benchmarks with published results for both. o4-mini scores higher in 2 categories and Qwen3.6 27B in 3 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Qwen3.6 27B leads 52.4 to 43.6.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 81.7% for o4-mini and 91.1% for Qwen3.6 27B.
- Qwen3.6 27B is cheaper at $0.60 / $3.60 per million input/output tokens, against $1.10 / $4.40 for o4-mini.
- Qwen3.6 27B accepts more context: 262K tokens versus 200K.
- Qwen3.6 27B has downloadable open weights; the other is API-only.
Side by side
| o4-mini | Qwen3.6 27B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 41.6 | 42.2 |
| Released | 2025-04-16 | 2026-04-22 |
| Weights | Proprietary | Open |
| Context window | 200K | 262K |
| Max output | 100K | 66K |
| Input $ / M tokens | $1.10 | $0.60 |
| Output $ / M tokens | $4.40 | $3.60 |
| Results tracked | 60 | 11 |
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Category by category
Coding o4-mini leads
o4-mini: 40.9 (#127), Qwen3.6 27B: 39.1 (#163)
| Benchmark | o4-mini | Qwen3.6 27B |
|---|---|---|
| SWE-bench Verified (bash only) | 45% | — |
| Aider Polyglot | 72% | — |
| SciCode | — | 37.3% |
| GSO | 3.6% | — |
| WeirdML | 52.6% | — |
| LMArena Coding | 1368 | — |
| CadEval | 62% | — |
| ALE-Bench | 826.17 | — |
| AlgoTune | 1.72 | — |
Agentic & Tool Use Not comparable
o4-mini: 32.6 (#61), Qwen3.6 27B: —
| Benchmark | o4-mini | Qwen3.6 27B |
|---|---|---|
| Berkeley Function Calling Leaderboard | 53.2% | — |
| GDPval | 25.3% | — |
| METR Time Horizons | 63.9% | — |
Reasoning Too close to call
o4-mini: 24.6 (#162), Qwen3.6 27B: 25.0 (#153)
| Benchmark | o4-mini | Qwen3.6 27B |
|---|---|---|
| CritPt | 0.6% | 0.9% |
| Chess Puzzles | 26% | 22% |
| Mystery Game Puzzles | 5% | 7% |
| DTBench | 77.6% | 78.1% |
| LMCA | 26.5% | 34.5% |
| Epoch Capabilities Index | 145.64 | 146.5 |
| 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 | — |
| ForecastBench | 61.8 | — |
Math Qwen3.6 27B leads
o4-mini: 40.8 (#89), Qwen3.6 27B: 48.5 (#62)
| Benchmark | o4-mini | Qwen3.6 27B |
|---|---|---|
| FrontierMath (Tiers 1-3) | 36.1% | 35.1% |
| OTIS Mock AIME 2024-2025 | 81.7% | 91.1% |
| FrontierMath Tier 4 | 4.9% | — |
| Omni-MATH | 72% | — |
| LMArena Math | 1389 | — |
| MATH Level 5 | 97.8% | — |
| FrontierMath (Feb 2025 set) | 24.8% | — |
| FrontierMath Tier 4 (v1) | 6.3% | — |
Knowledge Qwen3.6 27B leads
o4-mini: 43.6 (#91), Qwen3.6 27B: 52.4 (#63)
| Benchmark | o4-mini | Qwen3.6 27B |
|---|---|---|
| GPQA Diamond | 79.6% | 85.9% |
| 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 | 1343 | — |
Multimodal Not comparable
o4-mini: 40.2 (#49), Qwen3.6 27B: —
| Benchmark | o4-mini | Qwen3.6 27B |
|---|---|---|
| LMArena Vision | 1194 | — |
| GeoBench | 64% | — |
| VPCT | 57.5% | — |
Multilingual Not comparable
o4-mini: 47.0 (#154), Qwen3.6 27B: —
| Benchmark | o4-mini | Qwen3.6 27B |
|---|---|---|
| LMArena Non-English | 1337 | — |
| LMArena Chinese | 1354 | — |
| LMArena French | 1364 | — |
| LMArena German | 1336 | — |
| LMArena Japanese | 1308 | — |
| LMArena Korean | 1312 | — |
| LMArena Russian | 1334 | — |
| LMArena Spanish | 1347 | — |
Instruction Following Not comparable
o4-mini: 75.2 (#68), Qwen3.6 27B: —
| Benchmark | o4-mini | Qwen3.6 27B |
|---|---|---|
| IFEval | 92.8% | — |
| LMArena Instruction Following | 1321 | — |
Long Context Not comparable
o4-mini: 45.5 (#33), Qwen3.6 27B: —
| Benchmark | o4-mini | Qwen3.6 27B |
|---|---|---|
| Fiction.LiveBench | 77.8% | — |
| LMArena Longer Query | 1315 | — |
Writing & Preference o4-mini leads
o4-mini: 54.0 (#152), Qwen3.6 27B: 50.3 (#181)
| Benchmark | o4-mini | Qwen3.6 27B |
|---|---|---|
| LMArena Text | 1353 | — |
| LMArena Creative Writing | 1294 | — |
| Short-Story Creative Writing | 75% | — |
| WildBench | 85.4% | — |
| EQ-Bench 4 | — | 1026 |
| LMArena Multi-Turn | 1350 | — |
Frequently asked questions
Is o4-mini better than Qwen3.6 27B?
o4-mini and Qwen3.6 27B score almost the same on the Noometry Index (41.6 vs 42.2), so choose on price, context window or the category you care about most.
Which is cheaper, o4-mini or Qwen3.6 27B?
Qwen3.6 27B is cheaper. It lists at $0.60 per million input tokens and $3.60 per million output tokens; o4-mini lists at $1.10 and $4.40.
Is o4-mini or Qwen3.6 27B better for coding?
o4-mini scores higher on coding benchmarks: 40.9 versus 39.1 in the Noometry coding category.
Which has the bigger context window?
Qwen3.6 27B does, with 262K tokens against 200K.
How many benchmarks do o4-mini and Qwen3.6 27B share?
9 benchmarks have published results for both models. o4-mini has 60 scored results on Noometry and Qwen3.6 27B has 11.