# Llama-3.3-70B-Instruct vs MiMo-V2-Omni

> MiMo-V2-Omni is the stronger model overall, scoring 43.6 to 30.6 on the Noometry Index.

- Canonical page: https://noometry.com/compare/llama-3-3-70b-instruct-vs-mimo-v2-omni
- Last updated: 2026-10-12
- Shared benchmarks: 17

## Summary

- They share 17 benchmarks with published results for both. Llama-3.3-70B-Instruct scores higher in 0 categories and MiMo-V2-Omni in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where MiMo-V2-Omni leads 39.1 to 15.3.
- Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 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-3.3-70B-Instruct has downloadable open weights; the other is API-only.

## Snapshot

| | Llama-3.3-70B-Instruct | MiMo-V2-Omni |
|---|---|---|
| Provider | Meta | Xiaomi |
| Noometry Index | 30.6 | 43.6 |
| Rank | 291 | 88 |
| Context | 128K | 262K |
| Input $/M | $0.10 | $0.14 |
| Output $/M | $0.32 | $0.28 |
| Weights | Open | Proprietary |

## Coding

- Llama-3.3-70B-Instruct: 31.0 (#290)
- MiMo-V2-Omni: 43.3 (#89)

| Benchmark | Llama-3.3-70B-Instruct | MiMo-V2-Omni |
|---|---|---|
| LMArena Coding | 1268 | 1466 |
| SciCode | 26% | — |
| WeirdML | 14.4% | — |
| BigCodeBench Instruct | 46.9% | — |
| LiveBench Coding | 36.6% | — |
| BigCodeBench Complete | 57.5% | — |

## Agentic & Tool Use

- Llama-3.3-70B-Instruct: 25.8 (#105)
- MiMo-V2-Omni: —

| Benchmark | Llama-3.3-70B-Instruct | MiMo-V2-Omni |
|---|---|---|
| Berkeley Function Calling Leaderboard | 31.9% | — |
| BALROG | 23% | — |

## Reasoning

- Llama-3.3-70B-Instruct: 14.1 (#327)
- MiMo-V2-Omni: 29.7 (#88)

| Benchmark | Llama-3.3-70B-Instruct | MiMo-V2-Omni |
|---|---|---|
| LMArena Hard Prompts | 1257 | 1445 |
| SimpleBench | 19.9% | — |
| CritPt | 0% | — |
| LiveBench Reasoning | 50.8% | — |
| DTBench | 59.5% | — |
| LiveBench Data Analysis | 49.5% | — |
| LMCA | 17.5% | — |
| Epoch Capabilities Index | 127.33 | — |
| ForecastBench | 58.6 | — |
| LiveBench | 50.2% | — |

## Math

- Llama-3.3-70B-Instruct: 15.3 (#298)
- MiMo-V2-Omni: 39.1 (#115)

| Benchmark | Llama-3.3-70B-Instruct | MiMo-V2-Omni |
|---|---|---|
| LMArena Math | 1267 | 1430 |
| OTIS Mock AIME 2024-2025 | 5.1% | — |
| LiveBench Math | 42.2% | — |
| MATH Level 5 | 41.6% | — |

## Knowledge

- Llama-3.3-70B-Instruct: 30.6 (#226)
- MiMo-V2-Omni: 40.5 (#118)

| Benchmark | Llama-3.3-70B-Instruct | MiMo-V2-Omni |
|---|---|---|
| LMArena Expert | 1225 | 1449 |
| GPQA Diamond | 47.4% | — |
| Confabulations | 22.8% | — |
| Vectara Hallucination Rate | 4.1% | — |
| MMLU | 86.3% | — |

## Multimodal

- Llama-3.3-70B-Instruct: —
- MiMo-V2-Omni: 38.6 (#63)

| Benchmark | Llama-3.3-70B-Instruct | MiMo-V2-Omni |
|---|---|---|
| LMArena Vision | — | 1228 |

## Multilingual

- Llama-3.3-70B-Instruct: 39.9 (#220)
- MiMo-V2-Omni: 51.8 (#102)

| Benchmark | Llama-3.3-70B-Instruct | MiMo-V2-Omni |
|---|---|---|
| LMArena Non-English | 1236 | 1404 |
| LMArena Chinese | 1217 | 1465 |
| LMArena French | 1281 | 1447 |
| LMArena German | 1251 | 1399 |
| LMArena Japanese | 1150 | 1317 |
| LMArena Korean | 1143 | 1355 |
| LMArena Russian | 1252 | 1412 |
| LMArena Spanish | 1270 | 1434 |

## Instruction Following

- Llama-3.3-70B-Instruct: 71.1 (#157)
- MiMo-V2-Omni: 75.2 (#66)

| Benchmark | Llama-3.3-70B-Instruct | MiMo-V2-Omni |
|---|---|---|
| LMArena Instruction Following | 1242 | 1428 |
| LiveBench Instruction Following | 82.7% | — |

## Long Context

- Llama-3.3-70B-Instruct: 26.4 (#295)
- MiMo-V2-Omni: 44.1 (#76)

| Benchmark | Llama-3.3-70B-Instruct | MiMo-V2-Omni |
|---|---|---|
| LMArena Longer Query | 1256 | 1442 |
| Fiction.LiveBench | 33.3% | — |

## Writing & Preference

- Llama-3.3-70B-Instruct: 47.6 (#207)
- MiMo-V2-Omni: 61.4 (#87)

| Benchmark | Llama-3.3-70B-Instruct | MiMo-V2-Omni |
|---|---|---|
| LMArena Text | 1274 | 1423 |
| LMArena Creative Writing | 1250 | 1392 |
| LMArena Multi-Turn | 1280 | 1445 |
| LiveBench Language | 39.2% | — |

## FAQ

### Is Llama-3.3-70B-Instruct better than MiMo-V2-Omni?

MiMo-V2-Omni is the stronger model overall, scoring 43.6 to 30.6 on the Noometry Index.

### Which is cheaper, Llama-3.3-70B-Instruct or MiMo-V2-Omni?

Llama-3.3-70B-Instruct is cheaper. It lists at $0.10 per million input tokens and $0.32 per million output tokens; MiMo-V2-Omni lists at $0.14 and $0.28.

### Is Llama-3.3-70B-Instruct or MiMo-V2-Omni better for coding?

MiMo-V2-Omni scores higher on coding benchmarks: 43.3 versus 31.0 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-3.3-70B-Instruct and MiMo-V2-Omni share?

17 benchmarks have published results for both models. Llama-3.3-70B-Instruct has 43 scored results on Noometry and MiMo-V2-Omni has 18.