Model comparison
Llama 3.1-8B vs MiMo-V2-Pro
MiMo-V2-Pro is the stronger model overall, scoring 43.0 to 23.0 on the Noometry Index. Llama 3.1-8B costs 9.5× 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 3.1-8B scores higher in 0 categories and MiMo-V2-Pro in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where MiMo-V2-Pro leads 41.4 to 8.0.
- Llama 3.1-8B is cheaper at $0.05 / $0.08 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 3.1-8B has downloadable open weights; the other is API-only.
Side by side
| Llama 3.1-8B | MiMo-V2-Pro | |
|---|---|---|
| Provider | Meta | Xiaomi |
| Noometry Index | 23.0 | 43.0 |
| Released | 2024-07-23 | 2026-03-18 |
| Weights | Open | Proprietary |
| Context window | 128K | 1.05M |
| Max output | 4K | 131K |
| Input $ / M tokens | $0.05 | $0.43 |
| Output $ / M tokens | $0.08 | $0.87 |
| Results tracked | 43 | 23 |
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Category by category
Coding MiMo-V2-Pro leads
Llama 3.1-8B: 20.2 (#340), MiMo-V2-Pro: 43.8 (#83)
| Benchmark | Llama 3.1-8B | MiMo-V2-Pro |
|---|---|---|
| LMArena Coding | 1195 | 1476 |
| LMArena WebDev | — | 1433 |
| SciCode | 13.2% | — |
| WeirdML | 1.7% | — |
| BigCodeBench Instruct | 32.8% | — |
| BigCodeBench Complete | 40.5% | — |
| ALE-Bench | — | 785.17 |
| HumanEval+ | 62.8% | — |
| MBPP+ | 55.6% | — |
Agentic & Tool Use Not comparable
Llama 3.1-8B: 22.5 (#131), MiMo-V2-Pro: —
| Benchmark | Llama 3.1-8B | MiMo-V2-Pro |
|---|---|---|
| Berkeley Function Calling Leaderboard | 25.8% | — |
| BALROG | 15.1% | — |
Reasoning MiMo-V2-Pro leads
Llama 3.1-8B: 14.9 (#321), MiMo-V2-Pro: 22.1 (#206)
| Benchmark | Llama 3.1-8B | MiMo-V2-Pro |
|---|---|---|
| LMArena Hard Prompts | 1175 | 1457 |
| NYT Connections (extended) | — | 25.8% |
| CritPt | 0% | — |
| Chess Puzzles | 0% | — |
| Thematic Generalization | — | 45.9% |
| DTBench | 50.9% | — |
| LMCA | 5.4% | — |
| Epoch Capabilities Index | 116.57 | — |
| PIQA | 81.2% | — |
Math MiMo-V2-Pro leads
Llama 3.1-8B: 10.2 (#317), MiMo-V2-Pro: 39.5 (#102)
| Benchmark | Llama 3.1-8B | MiMo-V2-Pro |
|---|---|---|
| LMArena Math | 1179 | 1447 |
| OTIS Mock AIME 2024-2025 | 1.7% | — |
| Omni-MATH | 13.7% | — |
| MATH Level 5 | 22.9% | — |
| GSM8K | 82.4% | — |
Knowledge MiMo-V2-Pro leads
Llama 3.1-8B: 8.0 (#307), MiMo-V2-Pro: 41.4 (#111)
| Benchmark | Llama 3.1-8B | MiMo-V2-Pro |
|---|---|---|
| LMArena Expert | 1144 | 1478 |
| GPQA Diamond | 27% | — |
| MMLU-Pro | 40.6% | — |
| GPQA (HELM) | 24.7% | — |
| BoolQ | 82.8% | — |
| MMLU | 56.1% | — |
Multilingual MiMo-V2-Pro leads
Llama 3.1-8B: 34.0 (#249), MiMo-V2-Pro: 52.7 (#81)
| Benchmark | Llama 3.1-8B | MiMo-V2-Pro |
|---|---|---|
| LMArena Non-English | 1148 | 1416 |
| LMArena Chinese | 1151 | 1456 |
| LMArena French | 1177 | 1469 |
| LMArena German | 1144 | 1417 |
| LMArena Japanese | 1061 | 1366 |
| LMArena Korean | 1053 | 1400 |
| LMArena Russian | 1158 | 1427 |
| LMArena Spanish | 1169 | 1457 |
Instruction Following MiMo-V2-Pro leads
Llama 3.1-8B: 58.9 (#258), MiMo-V2-Pro: 76.0 (#49)
| Benchmark | Llama 3.1-8B | MiMo-V2-Pro |
|---|---|---|
| LMArena Instruction Following | 1159 | 1445 |
| IFEval | 74.3% | — |
Long Context MiMo-V2-Pro leads
Llama 3.1-8B: 35.8 (#238), MiMo-V2-Pro: 41.5 (#138)
| Benchmark | Llama 3.1-8B | MiMo-V2-Pro |
|---|---|---|
| LMArena Longer Query | 1182 | 1455 |
| CL-bench | — | 15.7% |
| CL-bench Life | — | 6.9% |
Writing & Preference MiMo-V2-Pro leads
Llama 3.1-8B: 29.7 (#290), MiMo-V2-Pro: 62.8 (#70)
| Benchmark | Llama 3.1-8B | MiMo-V2-Pro |
|---|---|---|
| LMArena Text | 1187 | 1436 |
| LMArena Creative Writing | 1154 | 1415 |
| LMArena Multi-Turn | 1172 | 1456 |
| EQ-Bench Creative Writing | 713 | — |
| WildBench | 68.7% | — |
Frequently asked questions
Is Llama 3.1-8B better than MiMo-V2-Pro?
MiMo-V2-Pro is the stronger model overall, scoring 43.0 to 23.0 on the Noometry Index. Llama 3.1-8B costs 9.5× 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 3.1-8B or MiMo-V2-Pro?
Llama 3.1-8B is cheaper. It lists at $0.05 per million input tokens and $0.08 per million output tokens; MiMo-V2-Pro lists at $0.43 and $0.87.
Is Llama 3.1-8B 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 3.1-8B and MiMo-V2-Pro share?
17 benchmarks have published results for both models. Llama 3.1-8B has 43 scored results on Noometry and MiMo-V2-Pro has 23.