Model comparison
GPT-4o mini vs Qwen3.6 27B
Qwen3.6 27B is the stronger model overall, scoring 41.9 to 25.4 on the Noometry Index. GPT-4o mini costs 5.1× less per token, which makes it the better buy when Qwen3.6 27B's lead doesn't matter for your workload.
Last verified . 8 shared benchmarks.
Alibaba (Qwen)Summary
- They share 8 benchmarks with published results for both. GPT-4o mini scores higher in 0 categories and Qwen3.6 27B in 5 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3.6 27B leads 47.5 to 9.9.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 6.9% for GPT-4o mini and 91.1% for Qwen3.6 27B.
- GPT-4o mini is cheaper at $0.15 / $0.60 per million input/output tokens, against $0.60 / $3.60 for Qwen3.6 27B.
- Qwen3.6 27B accepts more context: 262K tokens versus 128K.
- Qwen3.6 27B has downloadable open weights; the other is API-only.
Side by side
| GPT-4o mini | Qwen3.6 27B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 25.4 | 41.9 |
| Released | 2024-07-18 | 2026-04-22 |
| Weights | Proprietary | Open |
| Context window | 128K | 262K |
| Max output | 16K | 66K |
| Input $ / M tokens | $0.15 | $0.60 |
| Output $ / M tokens | $0.60 | $3.60 |
| Results tracked | 60 | 11 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Qwen3.6 27B leads
GPT-4o mini: 21.6 (#330), Qwen3.6 27B: 38.5 (#162)
| Benchmark | GPT-4o mini | Qwen3.6 27B |
|---|---|---|
| Aider Polyglot | 3.6% | — |
| SciCode | — | 37.3% |
| WeirdML | 11.8% | — |
| BigCodeBench Instruct | 46.1% | — |
| LiveBench Coding | 43.1% | — |
| LMArena Coding | 1290 | — |
| BigCodeBench Complete | 57.4% | — |
| HumanEval+ | 83.5% | — |
| MBPP+ | 72.2% | — |
Agentic & Tool Use Not comparable
GPT-4o mini: 27.2 (#95), Qwen3.6 27B: —
| Benchmark | GPT-4o mini | Qwen3.6 27B |
|---|---|---|
| BALROG | 17.4% | — |
Reasoning Qwen3.6 27B leads
GPT-4o mini: 8.7 (#343), Qwen3.6 27B: 24.4 (#148)
| Benchmark | GPT-4o mini | Qwen3.6 27B |
|---|---|---|
| Chess Puzzles | 0% | 22% |
| Mystery Game Puzzles | 12% | 7% |
| DTBench | 54.4% | 78.1% |
| LMCA | 10.4% | 34.5% |
| Epoch Capabilities Index | 126.56 | 146.47 |
| ARC-AGI-2 | 0% | — |
| SimpleBench | 10.7% | — |
| Kagi LLM Benchmark | 28.8% | — |
| CritPt | — | 0.9% |
| LiveBench Reasoning | 32.8% | — |
| LMArena Hard Prompts | 1267 | — |
| LiveBench Data Analysis | 50% | — |
| LiveBench | 41.3% | — |
| PIQA | 88.7% | — |
Math Qwen3.6 27B leads
GPT-4o mini: 9.9 (#307), Qwen3.6 27B: 47.5 (#56)
| Benchmark | GPT-4o mini | Qwen3.6 27B |
|---|---|---|
| FrontierMath (Tiers 1-3) | 0.7% | 35.1% |
| OTIS Mock AIME 2024-2025 | 6.9% | 91.1% |
| Omni-MATH | 28% | — |
| LiveBench Math | 36.3% | — |
| LMArena Math | 1267 | — |
| MATH Level 5 | 52.6% | — |
| GSM8K | 91.3% | — |
Knowledge Qwen3.6 27B leads
GPT-4o mini: 17.7 (#279), Qwen3.6 27B: 52.4 (#58)
| Benchmark | GPT-4o mini | Qwen3.6 27B |
|---|---|---|
| GPQA Diamond | 37.7% | 85.9% |
| SimpleQA Verified | 8.3% | — |
| MMLU-Pro | 60.3% | — |
| Confabulations | 37.2% | — |
| GPQA (HELM) | 36.8% | — |
| LMArena Expert | 1235 | — |
| BoolQ | 88.7% | — |
| MMLU | 81.8% | — |
Multimodal Not comparable
GPT-4o mini: 25.3 (#117), Qwen3.6 27B: —
| Benchmark | GPT-4o mini | Qwen3.6 27B |
|---|---|---|
| LMArena Vision | 1066 | — |
| Video-MME | 64.8% | — |
| GeoBench | 64% | — |
| VPCT | 34% | — |
Multilingual Not comparable
GPT-4o mini: 42.2 (#194), Qwen3.6 27B: —
| Benchmark | GPT-4o mini | Qwen3.6 27B |
|---|---|---|
| LMArena Non-English | 1266 | — |
| LMArena Chinese | 1264 | — |
| LMArena French | 1296 | — |
| LMArena German | 1272 | — |
| LMArena Japanese | 1216 | — |
| LMArena Korean | 1195 | — |
| LMArena Russian | 1275 | — |
| LMArena Spanish | 1276 | — |
Instruction Following Not comparable
GPT-4o mini: 62.1 (#234), Qwen3.6 27B: —
| Benchmark | GPT-4o mini | Qwen3.6 27B |
|---|---|---|
| LiveBench Instruction Following | 56.8% | — |
| IFEval | 78.2% | — |
| LMArena Instruction Following | 1259 | — |
Long Context Not comparable
GPT-4o mini: 39.1 (#181), Qwen3.6 27B: —
| Benchmark | GPT-4o mini | Qwen3.6 27B |
|---|---|---|
| LMArena Longer Query | 1289 | — |
Writing & Preference Qwen3.6 27B leads
GPT-4o mini: 39.7 (#243), Qwen3.6 27B: 50.3 (#180)
| Benchmark | GPT-4o mini | Qwen3.6 27B |
|---|---|---|
| LMArena Text | 1286 | — |
| LMArena Creative Writing | 1268 | — |
| Short-Story Creative Writing | 67.2% | — |
| EQ-Bench Creative Writing | 873 | — |
| WildBench | 79.1% | — |
| EQ-Bench 4 | — | 1026 |
| LMArena Multi-Turn | 1285 | — |
| LiveBench Language | 28.6% | — |
Frequently asked questions
Is GPT-4o mini better than Qwen3.6 27B?
Qwen3.6 27B is the stronger model overall, scoring 41.9 to 25.4 on the Noometry Index. GPT-4o mini costs 5.1× less per token, which makes it the better buy when Qwen3.6 27B's lead doesn't matter for your workload.
Which is cheaper, GPT-4o mini or Qwen3.6 27B?
GPT-4o mini is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; Qwen3.6 27B lists at $0.60 and $3.60.
Is GPT-4o mini or Qwen3.6 27B better for coding?
Qwen3.6 27B scores higher on coding benchmarks: 38.5 versus 21.6 in the Noometry coding category.
Which has the bigger context window?
Qwen3.6 27B does, with 262K tokens against 128K.
How many benchmarks do GPT-4o mini and Qwen3.6 27B share?
8 benchmarks have published results for both models. GPT-4o mini has 60 scored results on Noometry and Qwen3.6 27B has 11.