# Llama-3.3-70B-Instruct vs MiniMax-M2.5

> MiniMax-M2.5 is the stronger model overall, scoring 38.3 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 3.4× less per token, which makes it the better buy when MiniMax-M2.5's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/llama-3-3-70b-instruct-vs-minimax-m2-5
- Last updated: 2026-10-12
- Shared benchmarks: 19

## Summary

- They share 19 benchmarks with published results for both. Llama-3.3-70B-Instruct scores higher in 0 categories and MiniMax-M2.5 in 9 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in coding, where MiniMax-M2.5 leads 48.1 to 31.0.
- The biggest single-benchmark swing is Vectara Hallucination Rate: 4.1% for Llama-3.3-70B-Instruct and 9.1% for MiniMax-M2.5.
- Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 per million input/output tokens, against $0.30 / $1.20 for MiniMax-M2.5.
- MiniMax-M2.5 accepts more context: 205K tokens versus 128K.

## Snapshot

| | Llama-3.3-70B-Instruct | MiniMax-M2.5 |
|---|---|---|
| Provider | Meta | MiniMax |
| Noometry Index | 30.6 | 38.3 |
| Rank | 291 | 188 |
| Context | 128K | 205K |
| Input $/M | $0.10 | $0.30 |
| Output $/M | $0.32 | $1.20 |
| Weights | Open | Open |

## Coding

- Llama-3.3-70B-Instruct: 31.0 (#290)
- MiniMax-M2.5: 48.1 (#58)

| Benchmark | Llama-3.3-70B-Instruct | MiniMax-M2.5 |
|---|---|---|
| LMArena Coding | 1268 | 1381 |
| SWE-bench Verified (bash only) | — | 75.8% |
| LMArena WebDev | — | 1387 |
| SWE-bench Multilingual | — | 68.3% |
| SciCode | 26% | — |
| WeirdML | 14.4% | — |
| BigCodeBench Instruct | 46.9% | — |
| LiveBench Coding | 36.6% | — |
| BigCodeBench Complete | 57.5% | — |
| ALE-Bench | — | 618.17 |

## Agentic & Tool Use

- Llama-3.3-70B-Instruct: 25.8 (#105)
- MiniMax-M2.5: 30.4 (#77)

| Benchmark | Llama-3.3-70B-Instruct | MiniMax-M2.5 |
|---|---|---|
| Terminal-Bench | — | 42.7% |
| Berkeley Function Calling Leaderboard | 31.9% | — |
| BALROG | 23% | — |
| Vending-Bench 2 | — | -23.16 |

## Reasoning

- Llama-3.3-70B-Instruct: 14.1 (#327)
- MiniMax-M2.5: 17.5 (#292)

| Benchmark | Llama-3.3-70B-Instruct | MiniMax-M2.5 |
|---|---|---|
| LMArena Hard Prompts | 1257 | 1372 |
| Epoch Capabilities Index | 127.33 | 146.68 |
| ARC-AGI-2 | — | 4.9% |
| SimpleBench | 19.9% | — |
| Kagi LLM Benchmark | — | 55.2% |
| NYT Connections (extended) | — | 16.8% |
| ARC-AGI-1 | — | 63.7% |
| CritPt | 0% | — |
| LiveBench Reasoning | 50.8% | — |
| DTBench | 59.5% | — |
| LiveBench Data Analysis | 49.5% | — |
| LMCA | 17.5% | — |
| ForecastBench | 58.6 | — |
| LiveBench | 50.2% | — |

## Math

- Llama-3.3-70B-Instruct: 15.3 (#298)
- MiniMax-M2.5: 26.9 (#253)

| Benchmark | Llama-3.3-70B-Instruct | MiniMax-M2.5 |
|---|---|---|
| LMArena Math | 1267 | 1378 |
| OTIS Mock AIME 2024-2025 | 5.1% | — |
| ProofBench | — | 4% |
| LiveBench Math | 42.2% | — |
| MATH Level 5 | 41.6% | — |

## Knowledge

- Llama-3.3-70B-Instruct: 30.6 (#226)
- MiniMax-M2.5: 39.2 (#135)

| Benchmark | Llama-3.3-70B-Instruct | MiniMax-M2.5 |
|---|---|---|
| Vectara Hallucination Rate | 4.1% | 9.1% |
| LMArena Expert | 1225 | 1379 |
| GPQA Diamond | 47.4% | — |
| Confabulations | 22.8% | — |
| MMLU | 86.3% | — |

## Multilingual

- Llama-3.3-70B-Instruct: 39.9 (#220)
- MiniMax-M2.5: 47.1 (#152)

| Benchmark | Llama-3.3-70B-Instruct | MiniMax-M2.5 |
|---|---|---|
| LMArena Non-English | 1236 | 1338 |
| LMArena Chinese | 1217 | 1393 |
| LMArena French | 1281 | 1362 |
| LMArena German | 1251 | 1362 |
| LMArena Japanese | 1150 | 1171 |
| LMArena Korean | 1143 | 1232 |
| LMArena Russian | 1252 | 1358 |
| LMArena Spanish | 1270 | 1354 |

## Instruction Following

- Llama-3.3-70B-Instruct: 71.1 (#157)
- MiniMax-M2.5: 71.5 (#148)

| Benchmark | Llama-3.3-70B-Instruct | MiniMax-M2.5 |
|---|---|---|
| LMArena Instruction Following | 1242 | 1353 |
| LiveBench Instruction Following | 82.7% | — |

## Long Context

- Llama-3.3-70B-Instruct: 26.4 (#295)
- MiniMax-M2.5: 37.5 (#216)

| Benchmark | Llama-3.3-70B-Instruct | MiniMax-M2.5 |
|---|---|---|
| LMArena Longer Query | 1256 | 1366 |
| Fiction.LiveBench | 33.3% | — |
| CL-bench | — | 11.4% |
| CL-bench Life | — | 6.3% |

## Writing & Preference

- Llama-3.3-70B-Instruct: 47.6 (#207)
- MiniMax-M2.5: 53.9 (#153)

| Benchmark | Llama-3.3-70B-Instruct | MiniMax-M2.5 |
|---|---|---|
| LMArena Text | 1274 | 1359 |
| LMArena Creative Writing | 1250 | 1331 |
| LMArena Multi-Turn | 1280 | 1364 |
| EQ-Bench Creative Writing | — | 1361 |
| LiveBench Language | 39.2% | — |

## FAQ

### Is Llama-3.3-70B-Instruct better than MiniMax-M2.5?

MiniMax-M2.5 is the stronger model overall, scoring 38.3 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 3.4× less per token, which makes it the better buy when MiniMax-M2.5's lead doesn't matter for your workload.

### Which is cheaper, Llama-3.3-70B-Instruct or MiniMax-M2.5?

Llama-3.3-70B-Instruct is cheaper. It lists at $0.10 per million input tokens and $0.32 per million output tokens; MiniMax-M2.5 lists at $0.30 and $1.20.

### Is Llama-3.3-70B-Instruct or MiniMax-M2.5 better for coding?

MiniMax-M2.5 scores higher on coding benchmarks: 48.1 versus 31.0 in the Noometry coding category.

### Which has the bigger context window?

MiniMax-M2.5 does, with 205K tokens against 128K.

### How many benchmarks do Llama-3.3-70B-Instruct and MiniMax-M2.5 share?

19 benchmarks have published results for both models. Llama-3.3-70B-Instruct has 43 scored results on Noometry and MiniMax-M2.5 has 33.