Model comparison
Llama 4 Scout vs MiMo-V2-Pro
MiMo-V2-Pro is the stronger model overall, scoring 43.0 to 27.7 on the Noometry Index. Llama 4 Scout costs 3.6× less per token, which makes it the better buy when MiMo-V2-Pro's lead doesn't matter for your workload.
Last verified . 17 shared benchmarks.
MetaSummary
- They share 17 benchmarks with published results for both. Llama 4 Scout scores higher in 0 categories and MiMo-V2-Pro in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where MiMo-V2-Pro leads 62.8 to 37.0.
- Llama 4 Scout is cheaper at $0.10 / $0.30 per million input/output tokens, against $0.43 / $0.87 for MiMo-V2-Pro.
- MiMo-V2-Pro accepts more context: 1.05M tokens versus 128K.
- Llama 4 Scout has downloadable open weights; the other is API-only.
Side by side
| Llama 4 Scout | MiMo-V2-Pro | |
|---|---|---|
| Provider | Meta | Xiaomi |
| Noometry Index | 27.7 | 43.0 |
| Released | 2025-04-05 | 2026-03-18 |
| Weights | Open | Proprietary |
| Context window | 128K | 1.05M |
| Max output | 4K | 131K |
| Input $ / M tokens | $0.10 | $0.43 |
| Output $ / M tokens | $0.30 | $0.87 |
| Results tracked | 43 | 23 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding MiMo-V2-Pro leads
Llama 4 Scout: 20.2 (#339), MiMo-V2-Pro: 43.8 (#83)
| Benchmark | Llama 4 Scout | MiMo-V2-Pro |
|---|---|---|
| LMArena Coding | 1286 | 1476 |
| SWE-bench Verified (bash only) | 9.1% | — |
| LMArena WebDev | — | 1433 |
| SciCode | 17% | — |
| BigCodeBench Complete | 43.1% | — |
| ALE-Bench | — | 785.17 |
Agentic & Tool Use Not comparable
Llama 4 Scout: 24.6 (#119), MiMo-V2-Pro: —
| Benchmark | Llama 4 Scout | MiMo-V2-Pro |
|---|---|---|
| Berkeley Function Calling Leaderboard | 28.1% | — |
Reasoning MiMo-V2-Pro leads
Llama 4 Scout: 9.1 (#345), MiMo-V2-Pro: 22.1 (#206)
| Benchmark | Llama 4 Scout | MiMo-V2-Pro |
|---|---|---|
| LMArena Hard Prompts | 1266 | 1457 |
| ARC-AGI-2 | 0% | — |
| Kagi LLM Benchmark | 36.9% | — |
| NYT Connections (extended) | — | 25.8% |
| ARC-AGI-1 | 0.5% | — |
| CritPt | 0% | — |
| Thematic Generalization | — | 45.9% |
| DTBench | 57.9% | — |
| LMCA | 12% | — |
| Epoch Capabilities Index | 129.64 | — |
| ForecastBench | 57.5 | — |
Math MiMo-V2-Pro leads
Llama 4 Scout: 19.6 (#286), MiMo-V2-Pro: 39.5 (#102)
| Benchmark | Llama 4 Scout | MiMo-V2-Pro |
|---|---|---|
| LMArena Math | 1287 | 1447 |
| OTIS Mock AIME 2024-2025 | 7.8% | — |
| Omni-MATH | 37.3% | — |
| MATH Level 5 | 62.3% | — |
| FrontierMath (Feb 2025 set) | 0% | — |
Knowledge MiMo-V2-Pro leads
Llama 4 Scout: 31.9 (#217), MiMo-V2-Pro: 41.4 (#111)
| Benchmark | Llama 4 Scout | MiMo-V2-Pro |
|---|---|---|
| LMArena Expert | 1235 | 1478 |
| GPQA Diamond | 51.8% | — |
| MMLU-Pro | 74.2% | — |
| Vectara Hallucination Rate | 7.7% | — |
| GPQA (HELM) | 50.7% | — |
Multimodal Not comparable
Llama 4 Scout: 32.2 (#102), MiMo-V2-Pro: —
| Benchmark | Llama 4 Scout | MiMo-V2-Pro |
|---|---|---|
| LMArena Vision | 1118 | — |
| SpatialViz-Bench | 34.2% | — |
Multilingual MiMo-V2-Pro leads
Llama 4 Scout: 41.0 (#212), MiMo-V2-Pro: 52.7 (#81)
| Benchmark | Llama 4 Scout | MiMo-V2-Pro |
|---|---|---|
| LMArena Non-English | 1252 | 1416 |
| LMArena Chinese | 1255 | 1456 |
| LMArena French | 1282 | 1469 |
| LMArena German | 1272 | 1417 |
| LMArena Japanese | 1206 | 1366 |
| LMArena Korean | 1207 | 1400 |
| LMArena Russian | 1263 | 1427 |
| LMArena Spanish | 1278 | 1457 |
Instruction Following MiMo-V2-Pro leads
Llama 4 Scout: 65.8 (#217), MiMo-V2-Pro: 76.0 (#49)
| Benchmark | Llama 4 Scout | MiMo-V2-Pro |
|---|---|---|
| LMArena Instruction Following | 1248 | 1445 |
| IFEval | 81.8% | — |
Long Context MiMo-V2-Pro leads
Llama 4 Scout: 27.5 (#294), MiMo-V2-Pro: 41.5 (#138)
| Benchmark | Llama 4 Scout | MiMo-V2-Pro |
|---|---|---|
| LMArena Longer Query | 1265 | 1455 |
| Fiction.LiveBench | 36% | — |
| CL-bench | — | 15.7% |
| CL-bench Life | — | 6.9% |
Writing & Preference MiMo-V2-Pro leads
Llama 4 Scout: 37.0 (#261), MiMo-V2-Pro: 62.8 (#70)
| Benchmark | Llama 4 Scout | MiMo-V2-Pro |
|---|---|---|
| LMArena Text | 1279 | 1436 |
| LMArena Creative Writing | 1249 | 1415 |
| LMArena Multi-Turn | 1280 | 1456 |
| EQ-Bench Creative Writing | 783 | — |
| WildBench | 78% | — |
Frequently asked questions
Is Llama 4 Scout better than MiMo-V2-Pro?
MiMo-V2-Pro is the stronger model overall, scoring 43.0 to 27.7 on the Noometry Index. Llama 4 Scout costs 3.6× less per token, which makes it the better buy when MiMo-V2-Pro's lead doesn't matter for your workload.
Which is cheaper, Llama 4 Scout or MiMo-V2-Pro?
Llama 4 Scout is cheaper. It lists at $0.10 per million input tokens and $0.30 per million output tokens; MiMo-V2-Pro lists at $0.43 and $0.87.
Is Llama 4 Scout or MiMo-V2-Pro better for coding?
MiMo-V2-Pro scores higher on coding benchmarks: 43.8 versus 20.2 in the Noometry coding category.
Which has the bigger context window?
MiMo-V2-Pro does, with 1.05M tokens against 128K.
How many benchmarks do Llama 4 Scout and MiMo-V2-Pro share?
17 benchmarks have published results for both models. Llama 4 Scout has 43 scored results on Noometry and MiMo-V2-Pro has 23.