Model comparison
Llama 3.1-8B vs MiMo-V2-Omni
MiMo-V2-Omni is the stronger model overall, scoring 42.9 to 22.8 on the Noometry Index. Llama 3.1-8B costs 3.0× less per token, which makes it the better buy when MiMo-V2-Omni'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-Omni in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where MiMo-V2-Omni leads 39.9 to 8.0.
- Llama 3.1-8B is cheaper at $0.05 / $0.08 per million input/output tokens, against $0.14 / $0.28 for MiMo-V2-Omni.
- MiMo-V2-Omni accepts more context: 262K 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-Omni | |
|---|---|---|
| Provider | Meta | Xiaomi |
| Noometry Index | 22.8 | 42.9 |
| Released | 2024-07-23 | 2026-03-18 |
| Weights | Open | Proprietary |
| Context window | 128K | 262K |
| Max output | 4K | 131K |
| Input $ / M tokens | $0.05 | $0.14 |
| Output $ / M tokens | $0.08 | $0.28 |
| Results tracked | 43 | 18 |
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Category by category
Coding MiMo-V2-Omni leads
Llama 3.1-8B: 19.9 (#335), MiMo-V2-Omni: 42.7 (#87)
| Benchmark | Llama 3.1-8B | MiMo-V2-Omni |
|---|---|---|
| LMArena Coding | 1195 | 1467 |
| SciCode | 13.2% | — |
| WeirdML | 1.7% | — |
| BigCodeBench Instruct | 32.8% | — |
| BigCodeBench Complete | 40.5% | — |
| HumanEval+ | 62.8% | — |
| MBPP+ | 55.6% | — |
Agentic & Tool Use Not comparable
Llama 3.1-8B: 22.3 (#123), MiMo-V2-Omni: —
| Benchmark | Llama 3.1-8B | MiMo-V2-Omni |
|---|---|---|
| Berkeley Function Calling Leaderboard | 25.8% | — |
| BALROG | 15.1% | — |
Reasoning MiMo-V2-Omni leads
Llama 3.1-8B: 14.5 (#316), MiMo-V2-Omni: 28.6 (#86)
| Benchmark | Llama 3.1-8B | MiMo-V2-Omni |
|---|---|---|
| LMArena Hard Prompts | 1175 | 1445 |
| CritPt | 0% | — |
| Chess Puzzles | 0% | — |
| DTBench | 50.9% | — |
| LMCA | 5.4% | — |
| Epoch Capabilities Index | 116.48 | — |
| PIQA | 81.2% | — |
Math MiMo-V2-Omni leads
Llama 3.1-8B: 9.5 (#311), MiMo-V2-Omni: 36.4 (#112)
| Benchmark | Llama 3.1-8B | MiMo-V2-Omni |
|---|---|---|
| LMArena Math | 1179 | 1432 |
| OTIS Mock AIME 2024-2025 | 1.7% | — |
| Omni-MATH | 13.7% | — |
| MATH Level 5 | 22.9% | — |
| GSM8K | 82.4% | — |
Knowledge MiMo-V2-Omni leads
Llama 3.1-8B: 8.0 (#302), MiMo-V2-Omni: 39.9 (#114)
| Benchmark | Llama 3.1-8B | MiMo-V2-Omni |
|---|---|---|
| LMArena Expert | 1144 | 1449 |
| GPQA Diamond | 27% | — |
| MMLU-Pro | 40.6% | — |
| GPQA (HELM) | 24.7% | — |
| BoolQ | 82.8% | — |
| MMLU | 56.1% | — |
Multimodal Not comparable
Llama 3.1-8B: —, MiMo-V2-Omni: 37.9 (#62)
| Benchmark | Llama 3.1-8B | MiMo-V2-Omni |
|---|---|---|
| LMArena Vision | — | 1228 |
Multilingual MiMo-V2-Omni leads
Llama 3.1-8B: 34.2 (#244), MiMo-V2-Omni: 52.0 (#97)
| Benchmark | Llama 3.1-8B | MiMo-V2-Omni |
|---|---|---|
| LMArena Non-English | 1148 | 1404 |
| LMArena Chinese | 1151 | 1469 |
| LMArena French | 1176 | 1448 |
| LMArena German | 1144 | 1398 |
| LMArena Japanese | 1061 | 1318 |
| LMArena Korean | 1053 | 1355 |
| LMArena Russian | 1158 | 1412 |
| LMArena Spanish | 1169 | 1432 |
Instruction Following MiMo-V2-Omni leads
Llama 3.1-8B: 59.1 (#253), MiMo-V2-Omni: 75.4 (#62)
| Benchmark | Llama 3.1-8B | MiMo-V2-Omni |
|---|---|---|
| LMArena Instruction Following | 1159 | 1428 |
| IFEval | 74.3% | — |
Long Context MiMo-V2-Omni leads
Llama 3.1-8B: 35.8 (#233), MiMo-V2-Omni: 44.1 (#72)
| Benchmark | Llama 3.1-8B | MiMo-V2-Omni |
|---|---|---|
| LMArena Longer Query | 1182 | 1442 |
Writing & Preference MiMo-V2-Omni leads
Llama 3.1-8B: 29.9 (#285), MiMo-V2-Omni: 61.6 (#83)
| Benchmark | Llama 3.1-8B | MiMo-V2-Omni |
|---|---|---|
| LMArena Text | 1187 | 1424 |
| LMArena Creative Writing | 1154 | 1393 |
| LMArena Multi-Turn | 1172 | 1446 |
| EQ-Bench Creative Writing | 713 | — |
| WildBench | 68.7% | — |
Frequently asked questions
Is Llama 3.1-8B better than MiMo-V2-Omni?
MiMo-V2-Omni is the stronger model overall, scoring 42.9 to 22.8 on the Noometry Index. Llama 3.1-8B costs 3.0× less per token, which makes it the better buy when MiMo-V2-Omni's lead doesn't matter for your workload.
Which is cheaper, Llama 3.1-8B or MiMo-V2-Omni?
Llama 3.1-8B is cheaper. It lists at $0.05 per million input tokens and $0.08 per million output tokens; MiMo-V2-Omni lists at $0.14 and $0.28.
Is Llama 3.1-8B or MiMo-V2-Omni better for coding?
MiMo-V2-Omni scores higher on coding benchmarks: 42.7 versus 19.9 in the Noometry coding category.
Which has the bigger context window?
MiMo-V2-Omni does, with 262K tokens against 128K.
How many benchmarks do Llama 3.1-8B and MiMo-V2-Omni share?
17 benchmarks have published results for both models. Llama 3.1-8B has 43 scored results on Noometry and MiMo-V2-Omni has 18.