# Llama-3.3-70B-Instruct vs MiMo-V2.6-Pro

> MiMo-V2.6-Pro is the stronger model overall, scoring 46.7 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 3.5× less per token, which makes it the better buy when MiMo-V2.6-Pro's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/llama-3-3-70b-instruct-vs-mimo-v2-6-pro
- Last updated: 2026-10-10
- Shared benchmarks: 12

## Summary

- They share 12 benchmarks with published results for both. Llama-3.3-70B-Instruct scores higher in 0 categories and MiMo-V2.6-Pro in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where MiMo-V2.6-Pro leads 37.9 to 14.3.
- Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 per million input/output tokens, against $0.43 / $0.87 for MiMo-V2.6-Pro.
- MiMo-V2.6-Pro accepts more context: 1.05M tokens versus 128K.

## Snapshot

| | Llama-3.3-70B-Instruct | MiMo-V2.6-Pro |
|---|---|---|
| Provider | Meta | Xiaomi |
| Noometry Index | 30.3 | 46.7 |
| Rank | 283 | 55 |
| Context | 128K | 1.05M |
| Input $/M | $0.10 | $0.43 |
| Output $/M | $0.32 | $0.87 |
| Weights | Open | Open |

## Coding

- Llama-3.3-70B-Instruct: 30.6 (#284)
- MiMo-V2.6-Pro: 53.2 (#27)

| Benchmark | Llama-3.3-70B-Instruct | MiMo-V2.6-Pro |
|---|---|---|
| LMArena Coding | 1268 | 1538 |
| LMArena WebDev | — | 1618 |
| 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: 25.6 (#100)
- MiMo-V2.6-Pro: —

| Benchmark | Llama-3.3-70B-Instruct | MiMo-V2.6-Pro |
|---|---|---|
| Berkeley Function Calling Leaderboard | 31.9% | — |
| BALROG | 23% | — |

## Reasoning

- Llama-3.3-70B-Instruct: 13.7 (#325)
- MiMo-V2.6-Pro: 30.4 (#76)

| Benchmark | Llama-3.3-70B-Instruct | MiMo-V2.6-Pro |
|---|---|---|
| LMArena Hard Prompts | 1257 | 1511 |
| SimpleBench | 19.9% | — |
| CritPt | 0% | — |
| LiveBench Reasoning | 50.8% | — |
| DTBench | 59.5% | — |
| LiveBench Data Analysis | 49.5% | — |
| LMCA | 17.5% | — |
| Epoch Capabilities Index | 127.32 | — |
| ForecastBench | 58.6 | — |
| LiveBench | 50.2% | — |

## Math

- Llama-3.3-70B-Instruct: 14.3 (#292)
- MiMo-V2.6-Pro: 37.9 (#91)

| Benchmark | Llama-3.3-70B-Instruct | MiMo-V2.6-Pro |
|---|---|---|
| LMArena Math | 1268 | 1497 |
| OTIS Mock AIME 2024-2025 | 5.1% | — |
| LiveBench Math | 42.2% | — |
| MATH Level 5 | 41.6% | — |

## Knowledge

- Llama-3.3-70B-Instruct: 30.5 (#220)
- MiMo-V2.6-Pro: 43.0 (#85)

| Benchmark | Llama-3.3-70B-Instruct | MiMo-V2.6-Pro |
|---|---|---|
| LMArena Expert | 1225 | 1548 |
| GPQA Diamond | 47.4% | — |
| Confabulations | 22.8% | — |
| Vectara Hallucination Rate | 4.1% | — |
| MMLU | 86.3% | — |

## Multimodal

- Llama-3.3-70B-Instruct: —
- MiMo-V2.6-Pro: 40.2 (#40)

| Benchmark | Llama-3.3-70B-Instruct | MiMo-V2.6-Pro |
|---|---|---|
| LMArena Vision | — | 1265 |

## Multilingual

- Llama-3.3-70B-Instruct: 40.1 (#215)
- MiMo-V2.6-Pro: 57.0 (#15)

| Benchmark | Llama-3.3-70B-Instruct | MiMo-V2.6-Pro |
|---|---|---|
| LMArena Non-English | 1236 | 1474 |
| LMArena Chinese | 1217 | 1532 |
| LMArena Russian | 1252 | 1481 |
| LMArena French | 1280 | — |
| LMArena German | 1251 | — |
| LMArena Japanese | 1149 | — |
| LMArena Korean | 1142 | — |
| LMArena Spanish | 1269 | — |

## Instruction Following

- Llama-3.3-70B-Instruct: 71.3 (#152)
- MiMo-V2.6-Pro: 78.2 (#10)

| Benchmark | Llama-3.3-70B-Instruct | MiMo-V2.6-Pro |
|---|---|---|
| LMArena Instruction Following | 1242 | 1490 |
| LiveBench Instruction Following | 82.7% | — |

## Long Context

- Llama-3.3-70B-Instruct: 26.4 (#290)
- MiMo-V2.6-Pro: 46.0 (#26)

| Benchmark | Llama-3.3-70B-Instruct | MiMo-V2.6-Pro |
|---|---|---|
| LMArena Longer Query | 1256 | 1500 |
| Fiction.LiveBench | 33.3% | — |

## Writing & Preference

- Llama-3.3-70B-Instruct: 47.8 (#203)
- MiMo-V2.6-Pro: 66.9 (#32)

| Benchmark | Llama-3.3-70B-Instruct | MiMo-V2.6-Pro |
|---|---|---|
| LMArena Text | 1274 | 1491 |
| LMArena Creative Writing | 1250 | 1465 |
| LMArena Multi-Turn | 1279 | 1463 |
| LiveBench Language | 39.2% | — |

## FAQ

### Is Llama-3.3-70B-Instruct better than MiMo-V2.6-Pro?

MiMo-V2.6-Pro is the stronger model overall, scoring 46.7 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 3.5× less per token, which makes it the better buy when MiMo-V2.6-Pro's lead doesn't matter for your workload.

### Which is cheaper, Llama-3.3-70B-Instruct or MiMo-V2.6-Pro?

Llama-3.3-70B-Instruct is cheaper. It lists at $0.10 per million input tokens and $0.32 per million output tokens; MiMo-V2.6-Pro lists at $0.43 and $0.87.

### Is Llama-3.3-70B-Instruct or MiMo-V2.6-Pro better for coding?

MiMo-V2.6-Pro scores higher on coding benchmarks: 53.2 versus 30.6 in the Noometry coding category.

### Which has the bigger context window?

MiMo-V2.6-Pro does, with 1.05M tokens against 128K.

### How many benchmarks do Llama-3.3-70B-Instruct and MiMo-V2.6-Pro share?

12 benchmarks have published results for both models. Llama-3.3-70B-Instruct has 43 scored results on Noometry and MiMo-V2.6-Pro has 14.