Model comparison
Llama 3.1-8B vs MiniMax M1
MiniMax M1 is the stronger model overall, scoring 39.7 to 22.8 on the Noometry Index. Llama 3.1-8B costs 15× less per token, which makes it the better buy when MiniMax M1'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 M1 in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where MiniMax M1 leads 35.7 to 8.0.
- Llama 3.1-8B is cheaper at $0.05 / $0.08 per million input/output tokens, against $0.40 / $2.20 for MiniMax M1.
- MiniMax M1 accepts more context: 1M tokens versus 128K.
Side by side
| Llama 3.1-8B | MiniMax M1 | |
|---|---|---|
| Provider | Meta | MiniMax |
| Noometry Index | 22.8 | 39.7 |
| Released | 2024-07-23 | 2025-06-13 |
| Weights | Open | Open |
| Context window | 128K | 1M |
| Max output | 4K | 40K |
| Input $ / M tokens | $0.05 | $0.40 |
| Output $ / M tokens | $0.08 | $2.20 |
| Results tracked | 43 | 18 |
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Category by category
Coding MiniMax M1 leads
Llama 3.1-8B: 19.9 (#335), MiniMax M1: 39.2 (#149)
| Benchmark | Llama 3.1-8B | MiniMax M1 |
|---|---|---|
| LMArena Coding | 1195 | 1359 |
| SciCode | 13.2% | — |
| WeirdML | 1.7% | — |
| BigCodeBench Instruct | 32.8% | — |
| BigCodeBench Complete | 40.5% | — |
| HumanEval+ | 62.8% | — |
| MBPP+ | 55.6% | — |
Agentic & Tool Use Not comparable
Llama 3.1-8B: 22.3 (#123), MiniMax M1: —
| Benchmark | Llama 3.1-8B | MiniMax M1 |
|---|---|---|
| Berkeley Function Calling Leaderboard | 25.8% | — |
| BALROG | 15.1% | — |
Reasoning MiniMax M1 leads
Llama 3.1-8B: 14.5 (#316), MiniMax M1: 25.8 (#125)
| Benchmark | Llama 3.1-8B | MiniMax M1 |
|---|---|---|
| LMArena Hard Prompts | 1175 | 1340 |
| CritPt | 0% | — |
| Chess Puzzles | 0% | — |
| DTBench | 50.9% | — |
| LMCA | 5.4% | — |
| Epoch Capabilities Index | 116.48 | — |
| PIQA | 81.2% | — |
Math MiniMax M1 leads
Llama 3.1-8B: 9.5 (#311), MiniMax M1: 34.8 (#152)
| Benchmark | Llama 3.1-8B | MiniMax M1 |
|---|---|---|
| LMArena Math | 1179 | 1360 |
| OTIS Mock AIME 2024-2025 | 1.7% | — |
| Omni-MATH | 13.7% | — |
| MATH Level 5 | 22.9% | — |
| GSM8K | 82.4% | — |
Knowledge MiniMax M1 leads
Llama 3.1-8B: 8.0 (#302), MiniMax M1: 35.7 (#168)
| Benchmark | Llama 3.1-8B | MiniMax M1 |
|---|---|---|
| LMArena Expert | 1144 | 1316 |
| GPQA Diamond | 27% | — |
| MMLU-Pro | 40.6% | — |
| GPQA (HELM) | 24.7% | — |
| BoolQ | 82.8% | — |
| MMLU | 56.1% | — |
Multilingual MiniMax M1 leads
Llama 3.1-8B: 34.2 (#244), MiniMax M1: 46.0 (#157)
| Benchmark | Llama 3.1-8B | MiniMax M1 |
|---|---|---|
| LMArena Non-English | 1148 | 1320 |
| LMArena Chinese | 1151 | 1361 |
| LMArena French | 1176 | 1369 |
| LMArena German | 1144 | 1350 |
| LMArena Japanese | 1061 | 1220 |
| LMArena Korean | 1053 | 1263 |
| LMArena Russian | 1158 | 1330 |
| LMArena Spanish | 1169 | 1354 |
Instruction Following MiniMax M1 leads
Llama 3.1-8B: 59.1 (#253), MiniMax M1: 69.5 (#169)
| Benchmark | Llama 3.1-8B | MiniMax M1 |
|---|---|---|
| LMArena Instruction Following | 1159 | 1312 |
| IFEval | 74.3% | — |
Long Context MiniMax M1 leads
Llama 3.1-8B: 35.8 (#233), MiniMax M1: 41.4 (#136)
| Benchmark | Llama 3.1-8B | MiniMax M1 |
|---|---|---|
| LMArena Longer Query | 1182 | 1326 |
| Fiction.LiveBench | — | 69.4% |
Writing & Preference MiniMax M1 leads
Llama 3.1-8B: 29.9 (#285), MiniMax M1: 53.3 (#155)
| Benchmark | Llama 3.1-8B | MiniMax M1 |
|---|---|---|
| LMArena Text | 1187 | 1343 |
| LMArena Creative Writing | 1154 | 1298 |
| LMArena Multi-Turn | 1172 | 1335 |
| EQ-Bench Creative Writing | 713 | — |
| WildBench | 68.7% | — |
Frequently asked questions
Is Llama 3.1-8B better than MiniMax M1?
MiniMax M1 is the stronger model overall, scoring 39.7 to 22.8 on the Noometry Index. Llama 3.1-8B costs 15× less per token, which makes it the better buy when MiniMax M1's lead doesn't matter for your workload.
Which is cheaper, Llama 3.1-8B or MiniMax M1?
Llama 3.1-8B is cheaper. It lists at $0.05 per million input tokens and $0.08 per million output tokens; MiniMax M1 lists at $0.40 and $2.20.
Is Llama 3.1-8B or MiniMax M1 better for coding?
MiniMax M1 scores higher on coding benchmarks: 39.2 versus 19.9 in the Noometry coding category.
Which has the bigger context window?
MiniMax M1 does, with 1M tokens against 128K.
How many benchmarks do Llama 3.1-8B and MiniMax M1 share?
17 benchmarks have published results for both models. Llama 3.1-8B has 43 scored results on Noometry and MiniMax M1 has 18.