Model comparison
Llama 3.2 1B vs MiniMax-M2.7
MiniMax-M2.7 is the stronger model overall, scoring 37.4 to 19.9 on the Noometry Index. Llama 3.2 1B costs 7.4× less per token, which makes it the better buy when MiniMax-M2.7's lead doesn't matter for your workload.
Last verified . 14 shared benchmarks.
Meta
MiniMaxSummary
- They share 14 benchmarks with published results for both. Llama 3.2 1B scores higher in 0 categories and MiniMax-M2.7 in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where MiniMax-M2.7 leads 59.1 to 21.4.
- Llama 3.2 1B is cheaper at $0.027 / $0.20 per million input/output tokens, against $0.30 / $1.20 for MiniMax-M2.7.
- MiniMax-M2.7 accepts more context: 205K tokens versus 60K.
Side by side
| Llama 3.2 1B | MiniMax-M2.7 | |
|---|---|---|
| Provider | Meta | MiniMax |
| Noometry Index | 19.9 | 37.4 |
| Released | 2024-09-24 | 2026-03-18 |
| Weights | Open | Open |
| Context window | 60K | 205K |
| Max output | 54K | 131K |
| Input $ / M tokens | $0.027 | $0.30 |
| Output $ / M tokens | $0.20 | $1.20 |
| Results tracked | 22 | 30 |
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Category by category
Coding MiniMax-M2.7 leads
Llama 3.2 1B: 20.6 (#333), MiniMax-M2.7: 41.4 (#115)
| Benchmark | Llama 3.2 1B | MiniMax-M2.7 |
|---|---|---|
| LMArena Coding | 1071 | 1454 |
| LMArena WebDev | — | 1397 |
| SciCode | — | 47% |
| WeirdML | — | 37% |
| BigCodeBench Instruct | 8.2% | — |
| BigCodeBench Complete | 11.3% | — |
| ALE-Bench | — | 599.25 |
Agentic & Tool Use MiniMax-M2.7 leads
Llama 3.2 1B: 14.5 (#142), MiniMax-M2.7: 24.9 (#103)
| Benchmark | Llama 3.2 1B | MiniMax-M2.7 |
|---|---|---|
| Terminal-Bench | — | 45.1% |
| Berkeley Function Calling Leaderboard | 10.8% | — |
| BALROG | 6.6% | — |
| ExploitBench | — | 13.3% |
| GBAEval | — | 0% |
Reasoning MiniMax-M2.7 leads
Llama 3.2 1B: 15.6 (#304), MiniMax-M2.7: 19.0 (#248)
| Benchmark | Llama 3.2 1B | MiniMax-M2.7 |
|---|---|---|
| LMArena Hard Prompts | 1044 | 1422 |
| Epoch Capabilities Index | 101.95 | 145.84 |
| NYT Connections (extended) | — | 24.7% |
| CritPt | — | 0.6% |
| Chess Puzzles | 0% | — |
| Thematic Generalization | — | 39.3% |
Math MiniMax-M2.7 leads
Llama 3.2 1B: 9.7 (#308), MiniMax-M2.7: 24.9 (#254)
| Benchmark | Llama 3.2 1B | MiniMax-M2.7 |
|---|---|---|
| LMArena Math | 1086 | 1420 |
| OTIS Mock AIME 2024-2025 | 0.6% | — |
| ProofBench | — | 3% |
Knowledge MiniMax-M2.7 leads
Llama 3.2 1B: 7.2 (#307), MiniMax-M2.7: 37.1 (#151)
| Benchmark | Llama 3.2 1B | MiniMax-M2.7 |
|---|---|---|
| LMArena Expert | 1007 | 1443 |
| GPQA Diamond | 23.9% | — |
| Vectara Hallucination Rate | — | 12.9% |
Multilingual MiniMax-M2.7 leads
Llama 3.2 1B: 23.9 (#287), MiniMax-M2.7: 50.5 (#120)
| Benchmark | Llama 3.2 1B | MiniMax-M2.7 |
|---|---|---|
| LMArena Non-English | 973 | 1382 |
| LMArena Chinese | 958 | 1439 |
| LMArena German | 1014 | 1398 |
| LMArena Russian | 941 | 1383 |
| LMArena French | — | 1422 |
| LMArena Japanese | — | 1261 |
| LMArena Korean | — | 1313 |
| LMArena Spanish | — | 1403 |
Instruction Following MiniMax-M2.7 leads
Llama 3.2 1B: 52.6 (#285), MiniMax-M2.7: 74.2 (#101)
| Benchmark | Llama 3.2 1B | MiniMax-M2.7 |
|---|---|---|
| LMArena Instruction Following | 1031 | 1404 |
Long Context MiniMax-M2.7 leads
Llama 3.2 1B: 31.9 (#269), MiniMax-M2.7: 43.3 (#94)
| Benchmark | Llama 3.2 1B | MiniMax-M2.7 |
|---|---|---|
| LMArena Longer Query | 1050 | 1419 |
Writing & Preference MiniMax-M2.7 leads
Llama 3.2 1B: 21.4 (#305), MiniMax-M2.7: 59.1 (#107)
| Benchmark | Llama 3.2 1B | MiniMax-M2.7 |
|---|---|---|
| LMArena Text | 1055 | 1404 |
| LMArena Creative Writing | 1033 | 1354 |
| LMArena Multi-Turn | 1030 | 1413 |
| EQ-Bench Creative Writing | 200 | — |
Frequently asked questions
Is Llama 3.2 1B better than MiniMax-M2.7?
MiniMax-M2.7 is the stronger model overall, scoring 37.4 to 19.9 on the Noometry Index. Llama 3.2 1B costs 7.4× less per token, which makes it the better buy when MiniMax-M2.7's lead doesn't matter for your workload.
Which is cheaper, Llama 3.2 1B or MiniMax-M2.7?
Llama 3.2 1B is cheaper. It lists at $0.027 per million input tokens and $0.20 per million output tokens; MiniMax-M2.7 lists at $0.30 and $1.20.
Is Llama 3.2 1B or MiniMax-M2.7 better for coding?
MiniMax-M2.7 scores higher on coding benchmarks: 41.4 versus 20.6 in the Noometry coding category.
Which has the bigger context window?
MiniMax-M2.7 does, with 205K tokens against 60K.
How many benchmarks do Llama 3.2 1B and MiniMax-M2.7 share?
14 benchmarks have published results for both models. Llama 3.2 1B has 22 scored results on Noometry and MiniMax-M2.7 has 30.