Model comparison
Llama 3.2 1B vs MiMo-V2-Omni
MiMo-V2-Omni is the stronger model overall, scoring 42.9 to 19.9 on the Noometry Index. Llama 3.2 1B costs 2.5× less per token, which makes it the better buy when MiMo-V2-Omni's lead doesn't matter for your workload.
Last verified . 13 shared benchmarks.
MetaSummary
- They share 13 benchmarks with published results for both. Llama 3.2 1B scores higher in 0 categories and MiMo-V2-Omni in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where MiMo-V2-Omni leads 61.6 to 21.4.
- Llama 3.2 1B is cheaper at $0.027 / $0.20 per million input/output tokens, against $0.14 / $0.28 for MiMo-V2-Omni.
- MiMo-V2-Omni accepts more context: 262K tokens versus 60K.
- Llama 3.2 1B has downloadable open weights; the other is API-only.
Side by side
| Llama 3.2 1B | MiMo-V2-Omni | |
|---|---|---|
| Provider | Meta | Xiaomi |
| Noometry Index | 19.9 | 42.9 |
| Released | 2024-09-24 | 2026-03-18 |
| Weights | Open | Proprietary |
| Context window | 60K | 262K |
| Max output | 54K | 131K |
| Input $ / M tokens | $0.027 | $0.14 |
| Output $ / M tokens | $0.20 | $0.28 |
| Results tracked | 22 | 18 |
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Category by category
Coding MiMo-V2-Omni leads
Llama 3.2 1B: 20.6 (#333), MiMo-V2-Omni: 42.7 (#87)
| Benchmark | Llama 3.2 1B | MiMo-V2-Omni |
|---|---|---|
| LMArena Coding | 1071 | 1467 |
| BigCodeBench Instruct | 8.2% | — |
| BigCodeBench Complete | 11.3% | — |
Agentic & Tool Use Not comparable
Llama 3.2 1B: 14.5 (#142), MiMo-V2-Omni: —
| Benchmark | Llama 3.2 1B | MiMo-V2-Omni |
|---|---|---|
| Berkeley Function Calling Leaderboard | 10.8% | — |
| BALROG | 6.6% | — |
Reasoning MiMo-V2-Omni leads
Llama 3.2 1B: 15.6 (#304), MiMo-V2-Omni: 28.6 (#86)
| Benchmark | Llama 3.2 1B | MiMo-V2-Omni |
|---|---|---|
| LMArena Hard Prompts | 1044 | 1445 |
| Chess Puzzles | 0% | — |
| Epoch Capabilities Index | 101.95 | — |
Math MiMo-V2-Omni leads
Llama 3.2 1B: 9.7 (#308), MiMo-V2-Omni: 36.4 (#112)
| Benchmark | Llama 3.2 1B | MiMo-V2-Omni |
|---|---|---|
| LMArena Math | 1086 | 1432 |
| OTIS Mock AIME 2024-2025 | 0.6% | — |
Knowledge MiMo-V2-Omni leads
Llama 3.2 1B: 7.2 (#307), MiMo-V2-Omni: 39.9 (#114)
| Benchmark | Llama 3.2 1B | MiMo-V2-Omni |
|---|---|---|
| LMArena Expert | 1007 | 1449 |
| GPQA Diamond | 23.9% | — |
Multimodal Not comparable
Llama 3.2 1B: —, MiMo-V2-Omni: 37.9 (#62)
| Benchmark | Llama 3.2 1B | MiMo-V2-Omni |
|---|---|---|
| LMArena Vision | — | 1228 |
Multilingual MiMo-V2-Omni leads
Llama 3.2 1B: 23.9 (#287), MiMo-V2-Omni: 52.0 (#97)
| Benchmark | Llama 3.2 1B | MiMo-V2-Omni |
|---|---|---|
| LMArena Non-English | 973 | 1404 |
| LMArena Chinese | 958 | 1469 |
| LMArena German | 1014 | 1398 |
| LMArena Russian | 941 | 1412 |
| LMArena French | — | 1448 |
| LMArena Japanese | — | 1318 |
| LMArena Korean | — | 1355 |
| LMArena Spanish | — | 1432 |
Instruction Following MiMo-V2-Omni leads
Llama 3.2 1B: 52.6 (#285), MiMo-V2-Omni: 75.4 (#62)
| Benchmark | Llama 3.2 1B | MiMo-V2-Omni |
|---|---|---|
| LMArena Instruction Following | 1031 | 1428 |
Long Context MiMo-V2-Omni leads
Llama 3.2 1B: 31.9 (#269), MiMo-V2-Omni: 44.1 (#72)
| Benchmark | Llama 3.2 1B | MiMo-V2-Omni |
|---|---|---|
| LMArena Longer Query | 1050 | 1442 |
Writing & Preference MiMo-V2-Omni leads
Llama 3.2 1B: 21.4 (#305), MiMo-V2-Omni: 61.6 (#83)
| Benchmark | Llama 3.2 1B | MiMo-V2-Omni |
|---|---|---|
| LMArena Text | 1055 | 1424 |
| LMArena Creative Writing | 1033 | 1393 |
| LMArena Multi-Turn | 1030 | 1446 |
| EQ-Bench Creative Writing | 200 | — |
Frequently asked questions
Is Llama 3.2 1B better than MiMo-V2-Omni?
MiMo-V2-Omni is the stronger model overall, scoring 42.9 to 19.9 on the Noometry Index. Llama 3.2 1B costs 2.5× less per token, which makes it the better buy when MiMo-V2-Omni's lead doesn't matter for your workload.
Which is cheaper, Llama 3.2 1B or MiMo-V2-Omni?
Llama 3.2 1B is cheaper. It lists at $0.027 per million input tokens and $0.20 per million output tokens; MiMo-V2-Omni lists at $0.14 and $0.28.
Is Llama 3.2 1B or MiMo-V2-Omni better for coding?
MiMo-V2-Omni scores higher on coding benchmarks: 42.7 versus 20.6 in the Noometry coding category.
Which has the bigger context window?
MiMo-V2-Omni does, with 262K tokens against 60K.
How many benchmarks do Llama 3.2 1B and MiMo-V2-Omni share?
13 benchmarks have published results for both models. Llama 3.2 1B has 22 scored results on Noometry and MiMo-V2-Omni has 18.