Model comparison
Llama 3.1-70B vs MiniMax-M2.5
MiniMax-M2.5 is the stronger model overall, scoring 38.1 to 29.4 on the Noometry Index.
Last verified . 19 shared benchmarks.
Meta
MiniMaxSummary
- They share 19 benchmarks with published results for both. Llama 3.1-70B scores higher in 2 categories and MiniMax-M2.5 in 7 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where MiniMax-M2.5 leads 54.1 to 35.6.
- Llama 3.1-70B is cheaper at $0.40 / $0.40 per million input/output tokens, against $0.30 / $1.20 for MiniMax-M2.5.
- MiniMax-M2.5 accepts more context: 205K tokens versus 128K.
Side by side
| Llama 3.1-70B | MiniMax-M2.5 | |
|---|---|---|
| Provider | Meta | MiniMax |
| Noometry Index | 29.4 | 38.1 |
| Released | 2024-07-23 | 2026-02-12 |
| Weights | Open | Open |
| Context window | 128K | 205K |
| Max output | 4K | 131K |
| Input $ / M tokens | $0.40 | $0.30 |
| Output $ / M tokens | $0.40 | $1.20 |
| Results tracked | 35 | 33 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding MiniMax-M2.5 leads
Llama 3.1-70B: 29.8 (#292), MiniMax-M2.5: 47.9 (#53)
| Benchmark | Llama 3.1-70B | MiniMax-M2.5 |
|---|---|---|
| LMArena Coding | 1260 | 1380 |
| SWE-bench Verified (bash only) | — | 75.8% |
| LMArena WebDev | — | 1386 |
| SWE-bench Multilingual | — | 68.3% |
| WeirdML | 9% | — |
| BigCodeBench Instruct | 46.1% | — |
| BigCodeBench Complete | 54.8% | — |
| ALE-Bench | — | 618.17 |
Agentic & Tool Use MiniMax-M2.5 leads
Llama 3.1-70B: 24.9 (#104), MiniMax-M2.5: 30.2 (#70)
| Benchmark | Llama 3.1-70B | MiniMax-M2.5 |
|---|---|---|
| Terminal-Bench | — | 42.7% |
| TheAgentCompany | 6.9% | — |
| BALROG | 27.9% | — |
| Vending-Bench 2 | — | -23.16 |
Reasoning Llama 3.1-70B leads
Llama 3.1-70B: 20.8 (#215), MiniMax-M2.5: 17.6 (#281)
| Benchmark | Llama 3.1-70B | MiniMax-M2.5 |
|---|---|---|
| LMArena Hard Prompts | 1241 | 1372 |
| Epoch Capabilities Index | 125.91 | 146.68 |
| ARC-AGI-2 | — | 4.9% |
| Kagi LLM Benchmark | — | 55.2% |
| NYT Connections (extended) | — | 16.8% |
| ARC-AGI-1 | — | 63.7% |
| DTBench | 60% | — |
| LMCA | 14.8% | — |
Math MiniMax-M2.5 leads
Llama 3.1-70B: 12.7 (#298), MiniMax-M2.5: 25.8 (#242)
| Benchmark | Llama 3.1-70B | MiniMax-M2.5 |
|---|---|---|
| LMArena Math | 1252 | 1378 |
| OTIS Mock AIME 2024-2025 | 3.6% | — |
| ProofBench | — | 4% |
| Omni-MATH | 21% | — |
| MATH Level 5 | 36.7% | — |
Knowledge MiniMax-M2.5 leads
Llama 3.1-70B: 24.1 (#264), MiniMax-M2.5: 38.6 (#132)
| Benchmark | Llama 3.1-70B | MiniMax-M2.5 |
|---|---|---|
| LMArena Expert | 1209 | 1378 |
| GPQA Diamond | 44.2% | — |
| MMLU-Pro | 65.3% | — |
| Vectara Hallucination Rate | — | 9.1% |
| GPQA (HELM) | 42.6% | — |
| MMLU | 80.1% | — |
Multilingual MiniMax-M2.5 leads
Llama 3.1-70B: 38.9 (#220), MiniMax-M2.5: 47.3 (#147)
| Benchmark | Llama 3.1-70B | MiniMax-M2.5 |
|---|---|---|
| LMArena Non-English | 1219 | 1338 |
| LMArena Chinese | 1215 | 1392 |
| LMArena French | 1260 | 1363 |
| LMArena German | 1222 | 1361 |
| LMArena Japanese | 1132 | 1169 |
| LMArena Korean | 1140 | 1232 |
| LMArena Russian | 1234 | 1359 |
| LMArena Spanish | 1253 | 1354 |
Instruction Following MiniMax-M2.5 leads
Llama 3.1-70B: 65.5 (#218), MiniMax-M2.5: 71.7 (#143)
| Benchmark | Llama 3.1-70B | MiniMax-M2.5 |
|---|---|---|
| LMArena Instruction Following | 1231 | 1353 |
| IFEval | 82.1% | — |
Long Context Too close to call
Llama 3.1-70B: 37.6 (#208), MiniMax-M2.5: 37.4 (#211)
| Benchmark | Llama 3.1-70B | MiniMax-M2.5 |
|---|---|---|
| LMArena Longer Query | 1241 | 1367 |
| CL-bench | — | 11.4% |
| CL-bench Life | — | 6.3% |
Writing & Preference MiniMax-M2.5 leads
Llama 3.1-70B: 35.6 (#262), MiniMax-M2.5: 54.1 (#149)
| Benchmark | Llama 3.1-70B | MiniMax-M2.5 |
|---|---|---|
| LMArena Text | 1261 | 1359 |
| LMArena Creative Writing | 1232 | 1331 |
| EQ-Bench Creative Writing | 784 | 1361 |
| LMArena Multi-Turn | 1256 | 1364 |
| WildBench | 75.8% | — |
Frequently asked questions
Is Llama 3.1-70B better than MiniMax-M2.5?
MiniMax-M2.5 is the stronger model overall, scoring 38.1 to 29.4 on the Noometry Index.
Which is cheaper, Llama 3.1-70B or MiniMax-M2.5?
Llama 3.1-70B is cheaper. It lists at $0.40 per million input tokens and $0.40 per million output tokens; MiniMax-M2.5 lists at $0.30 and $1.20.
Is Llama 3.1-70B or MiniMax-M2.5 better for coding?
MiniMax-M2.5 scores higher on coding benchmarks: 47.9 versus 29.8 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 3.1-70B and MiniMax-M2.5 share?
19 benchmarks have published results for both models. Llama 3.1-70B has 35 scored results on Noometry and MiniMax-M2.5 has 33.