Model comparison
Llama 3.1-8B vs MiniMax-M2.1
MiniMax-M2.1 is the stronger model overall, scoring 38.4 to 22.8 on the Noometry Index. Llama 3.1-8B costs 9.1× less per token, which makes it the better buy when MiniMax-M2.1's lead doesn't matter for your workload.
Last verified . 17 shared benchmarks.
Meta
MiniMaxSummary
- They share 17 benchmarks with published results for both. Llama 3.1-8B scores higher in 0 categories and MiniMax-M2.1 in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where MiniMax-M2.1 leads 37.7 to 8.0.
- Llama 3.1-8B is cheaper at $0.05 / $0.08 per million input/output tokens, against $0.30 / $1.20 for MiniMax-M2.1.
- MiniMax-M2.1 accepts more context: 205K tokens versus 128K.
Side by side
| Llama 3.1-8B | MiniMax-M2.1 | |
|---|---|---|
| Provider | Meta | MiniMax |
| Noometry Index | 22.8 | 38.4 |
| Released | 2024-07-23 | 2025-12-23 |
| Weights | Open | Open |
| Context window | 128K | 205K |
| Max output | 4K | 131K |
| Input $ / M tokens | $0.05 | $0.30 |
| Output $ / M tokens | $0.08 | $1.20 |
| Results tracked | 43 | 22 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding MiniMax-M2.1 leads
Llama 3.1-8B: 19.9 (#335), MiniMax-M2.1: 40.1 (#132)
| Benchmark | Llama 3.1-8B | MiniMax-M2.1 |
|---|---|---|
| LMArena Coding | 1195 | 1421 |
| LMArena WebDev | — | 1384 |
| SciCode | 13.2% | — |
| WeirdML | 1.7% | — |
| BigCodeBench Instruct | 32.8% | — |
| BigCodeBench Complete | 40.5% | — |
| ALE-Bench | — | 623.83 |
| HumanEval+ | 62.8% | — |
| MBPP+ | 55.6% | — |
Agentic & Tool Use MiniMax-M2.1 leads
Llama 3.1-8B: 22.3 (#123), MiniMax-M2.1: 27.7 (#91)
| Benchmark | Llama 3.1-8B | MiniMax-M2.1 |
|---|---|---|
| Terminal-Bench | — | 36.6% |
| Berkeley Function Calling Leaderboard | 25.8% | — |
| BALROG | 15.1% | — |
Reasoning MiniMax-M2.1 leads
Llama 3.1-8B: 14.5 (#316), MiniMax-M2.1: 15.8 (#301)
| Benchmark | Llama 3.1-8B | MiniMax-M2.1 |
|---|---|---|
| LMArena Hard Prompts | 1175 | 1410 |
| NYT Connections (extended) | — | 11.2% |
| CritPt | 0% | — |
| Chess Puzzles | 0% | — |
| DTBench | 50.9% | — |
| LMCA | 5.4% | — |
| Epoch Capabilities Index | 116.48 | — |
| PIQA | 81.2% | — |
Math MiniMax-M2.1 leads
Llama 3.1-8B: 9.5 (#311), MiniMax-M2.1: 35.6 (#142)
| Benchmark | Llama 3.1-8B | MiniMax-M2.1 |
|---|---|---|
| LMArena Math | 1179 | 1396 |
| OTIS Mock AIME 2024-2025 | 1.7% | — |
| Omni-MATH | 13.7% | — |
| MATH Level 5 | 22.9% | — |
| GSM8K | 82.4% | — |
Knowledge MiniMax-M2.1 leads
Llama 3.1-8B: 8.0 (#302), MiniMax-M2.1: 37.7 (#143)
| Benchmark | Llama 3.1-8B | MiniMax-M2.1 |
|---|---|---|
| LMArena Expert | 1144 | 1432 |
| GPQA Diamond | 27% | — |
| MMLU-Pro | 40.6% | — |
| Vectara Hallucination Rate | — | 11.8% |
| GPQA (HELM) | 24.7% | — |
| BoolQ | 82.8% | — |
| MMLU | 56.1% | — |
Multilingual MiniMax-M2.1 leads
Llama 3.1-8B: 34.2 (#244), MiniMax-M2.1: 50.2 (#123)
| Benchmark | Llama 3.1-8B | MiniMax-M2.1 |
|---|---|---|
| LMArena Non-English | 1148 | 1378 |
| LMArena Chinese | 1151 | 1430 |
| LMArena French | 1176 | 1405 |
| LMArena German | 1144 | 1380 |
| LMArena Japanese | 1061 | 1287 |
| LMArena Korean | 1053 | 1297 |
| LMArena Russian | 1158 | 1386 |
| LMArena Spanish | 1169 | 1396 |
Instruction Following MiniMax-M2.1 leads
Llama 3.1-8B: 59.1 (#253), MiniMax-M2.1: 74.0 (#108)
| Benchmark | Llama 3.1-8B | MiniMax-M2.1 |
|---|---|---|
| LMArena Instruction Following | 1159 | 1399 |
| IFEval | 74.3% | — |
Long Context MiniMax-M2.1 leads
Llama 3.1-8B: 35.8 (#233), MiniMax-M2.1: 43.2 (#98)
| Benchmark | Llama 3.1-8B | MiniMax-M2.1 |
|---|---|---|
| LMArena Longer Query | 1182 | 1416 |
Writing & Preference MiniMax-M2.1 leads
Llama 3.1-8B: 29.9 (#285), MiniMax-M2.1: 58.5 (#114)
| Benchmark | Llama 3.1-8B | MiniMax-M2.1 |
|---|---|---|
| LMArena Text | 1187 | 1392 |
| LMArena Creative Writing | 1154 | 1362 |
| LMArena Multi-Turn | 1172 | 1396 |
| EQ-Bench Creative Writing | 713 | — |
| WildBench | 68.7% | — |
Frequently asked questions
Is Llama 3.1-8B better than MiniMax-M2.1?
MiniMax-M2.1 is the stronger model overall, scoring 38.4 to 22.8 on the Noometry Index. Llama 3.1-8B costs 9.1× less per token, which makes it the better buy when MiniMax-M2.1's lead doesn't matter for your workload.
Which is cheaper, Llama 3.1-8B or MiniMax-M2.1?
Llama 3.1-8B is cheaper. It lists at $0.05 per million input tokens and $0.08 per million output tokens; MiniMax-M2.1 lists at $0.30 and $1.20.
Is Llama 3.1-8B or MiniMax-M2.1 better for coding?
MiniMax-M2.1 scores higher on coding benchmarks: 40.1 versus 19.9 in the Noometry coding category.
Which has the bigger context window?
MiniMax-M2.1 does, with 205K tokens against 128K.
How many benchmarks do Llama 3.1-8B and MiniMax-M2.1 share?
17 benchmarks have published results for both models. Llama 3.1-8B has 43 scored results on Noometry and MiniMax-M2.1 has 22.