Model comparison
MiMo-V2-Pro vs Qwen3 14B
MiMo-V2-Pro is the stronger model overall, scoring 43.0 to 35.5 on the Noometry Index.
Last verified . 0 shared benchmarks.
Alibaba (Qwen)Summary
- The widest gap is in coding, where MiMo-V2-Pro leads 43.8 to 37.3.
- MiMo-V2-Pro is cheaper at $0.43 / $0.87 per million input/output tokens, against $0.35 / $1.40 for Qwen3 14B.
- MiMo-V2-Pro accepts more context: 1.05M tokens versus 131K.
- Qwen3 14B has downloadable open weights; the other is API-only.
Side by side
| MiMo-V2-Pro | Qwen3 14B | |
|---|---|---|
| Provider | Xiaomi | Alibaba (Qwen) |
| Noometry Index | 43.0 | 35.5 |
| Released | 2026-03-18 | 2025-04 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 131K |
| Max output | 131K | 8K |
| Input $ / M tokens | $0.43 | $0.35 |
| Output $ / M tokens | $0.87 | $1.40 |
| Results tracked | 23 | 12 |
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Category by category
Coding MiMo-V2-Pro leads
MiMo-V2-Pro: 43.8 (#83), Qwen3 14B: 37.3 (#195)
| Benchmark | MiMo-V2-Pro | Qwen3 14B |
|---|---|---|
| LMArena WebDev | 1433 | — |
| SciCode | — | 31.6% |
| LMArena Coding | 1476 | — |
| ALE-Bench | 785.17 | — |
Agentic & Tool Use Not comparable
MiMo-V2-Pro: —, Qwen3 14B: 29.6 (#83)
| Benchmark | MiMo-V2-Pro | Qwen3 14B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 41% |
Reasoning MiMo-V2-Pro leads
MiMo-V2-Pro: 22.1 (#206), Qwen3 14B: 18.5 (#280)
| Benchmark | MiMo-V2-Pro | Qwen3 14B |
|---|---|---|
| Kagi LLM Benchmark | — | 49.1% |
| NYT Connections (extended) | 25.8% | — |
| CritPt | — | 0% |
| Chess Puzzles | — | 4% |
| Thematic Generalization | 45.9% | — |
| LMArena Hard Prompts | 1457 | — |
| DTBench | — | 64% |
| LMCA | — | 18.2% |
| Epoch Capabilities Index | — | 138.23 |
Math Too close to call
MiMo-V2-Pro: 39.5 (#102), Qwen3 14B: 38.6 (#133)
| Benchmark | MiMo-V2-Pro | Qwen3 14B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 66.4% |
| LMArena Math | 1447 | — |
Knowledge MiMo-V2-Pro leads
MiMo-V2-Pro: 41.4 (#111), Qwen3 14B: 39.3 (#134)
| Benchmark | MiMo-V2-Pro | Qwen3 14B |
|---|---|---|
| GPQA Diamond | — | 63.8% |
| Vectara Hallucination Rate | — | 5.4% |
| LMArena Expert | 1478 | — |
Multilingual Not comparable
MiMo-V2-Pro: 52.7 (#81), Qwen3 14B: —
| Benchmark | MiMo-V2-Pro | Qwen3 14B |
|---|---|---|
| LMArena Non-English | 1416 | — |
| LMArena Chinese | 1456 | — |
| LMArena French | 1469 | — |
| LMArena German | 1417 | — |
| LMArena Japanese | 1366 | — |
| LMArena Korean | 1400 | — |
| LMArena Russian | 1427 | — |
| LMArena Spanish | 1457 | — |
Instruction Following Not comparable
MiMo-V2-Pro: 76.0 (#49), Qwen3 14B: —
| Benchmark | MiMo-V2-Pro | Qwen3 14B |
|---|---|---|
| LMArena Instruction Following | 1445 | — |
Long Context MiMo-V2-Pro leads
MiMo-V2-Pro: 41.5 (#138), Qwen3 14B: 38.1 (#204)
| Benchmark | MiMo-V2-Pro | Qwen3 14B |
|---|---|---|
| Fiction.LiveBench | — | 62.5% |
| CL-bench | 15.7% | — |
| CL-bench Life | 6.9% | — |
| LMArena Longer Query | 1455 | — |
Writing & Preference Not comparable
MiMo-V2-Pro: 62.8 (#70), Qwen3 14B: —
| Benchmark | MiMo-V2-Pro | Qwen3 14B |
|---|---|---|
| LMArena Text | 1436 | — |
| LMArena Creative Writing | 1415 | — |
| LMArena Multi-Turn | 1456 | — |
Frequently asked questions
Is MiMo-V2-Pro better than Qwen3 14B?
MiMo-V2-Pro is the stronger model overall, scoring 43.0 to 35.5 on the Noometry Index.
Which is cheaper, MiMo-V2-Pro or Qwen3 14B?
MiMo-V2-Pro is cheaper. It lists at $0.43 per million input tokens and $0.87 per million output tokens; Qwen3 14B lists at $0.35 and $1.40.
Is MiMo-V2-Pro or Qwen3 14B better for coding?
MiMo-V2-Pro scores higher on coding benchmarks: 43.8 versus 37.3 in the Noometry coding category.
Which has the bigger context window?
MiMo-V2-Pro does, with 1.05M tokens against 131K.
How many benchmarks do MiMo-V2-Pro and Qwen3 14B share?
0 benchmarks have published results for both models. MiMo-V2-Pro has 23 scored results on Noometry and Qwen3 14B has 12.