Model comparison
Llama-3.3-70B-Instruct vs MiMo-V2-Pro
MiMo-V2-Pro is the stronger model overall, scoring 42.4 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 3.5× less per token, which makes it the better buy when MiMo-V2-Pro's lead doesn't matter for your workload.
Last verified . 17 shared benchmarks.
MetaSummary
- They share 17 benchmarks with published results for both. Llama-3.3-70B-Instruct scores higher in 0 categories and MiMo-V2-Pro in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where MiMo-V2-Pro leads 36.7 to 14.3.
- Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 per million input/output tokens, against $0.43 / $0.87 for MiMo-V2-Pro.
- MiMo-V2-Pro accepts more context: 1.05M tokens versus 128K.
- Llama-3.3-70B-Instruct has downloadable open weights; the other is API-only.
Side by side
| Llama-3.3-70B-Instruct | MiMo-V2-Pro | |
|---|---|---|
| Provider | Meta | Xiaomi |
| Noometry Index | 30.3 | 42.4 |
| Released | 2024-12-06 | 2026-03-18 |
| Weights | Open | Proprietary |
| Context window | 128K | 1.05M |
| Max output | 4K | 131K |
| Input $ / M tokens | $0.10 | $0.43 |
| Output $ / M tokens | $0.32 | $0.87 |
| Results tracked | 43 | 23 |
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Category by category
Coding MiMo-V2-Pro leads
Llama-3.3-70B-Instruct: 30.6 (#284), MiMo-V2-Pro: 43.4 (#76)
| Benchmark | Llama-3.3-70B-Instruct | MiMo-V2-Pro |
|---|---|---|
| LMArena Coding | 1268 | 1476 |
| LMArena WebDev | — | 1433 |
| SciCode | 26% | — |
| WeirdML | 14.4% | — |
| BigCodeBench Instruct | 46.9% | — |
| LiveBench Coding | 36.6% | — |
| BigCodeBench Complete | 57.5% | — |
| ALE-Bench | — | 785.17 |
Agentic & Tool Use Not comparable
Llama-3.3-70B-Instruct: 25.6 (#100), MiMo-V2-Pro: —
| Benchmark | Llama-3.3-70B-Instruct | MiMo-V2-Pro |
|---|---|---|
| Berkeley Function Calling Leaderboard | 31.9% | — |
| BALROG | 23% | — |
Reasoning MiMo-V2-Pro leads
Llama-3.3-70B-Instruct: 13.7 (#325), MiMo-V2-Pro: 21.3 (#200)
| Benchmark | Llama-3.3-70B-Instruct | MiMo-V2-Pro |
|---|---|---|
| LMArena Hard Prompts | 1257 | 1457 |
| SimpleBench | 19.9% | — |
| NYT Connections (extended) | — | 25.8% |
| CritPt | 0% | — |
| Thematic Generalization | — | 45.9% |
| LiveBench Reasoning | 50.8% | — |
| DTBench | 59.5% | — |
| LiveBench Data Analysis | 49.5% | — |
| LMCA | 17.5% | — |
| Epoch Capabilities Index | 127.32 | — |
| ForecastBench | 58.6 | — |
| LiveBench | 50.2% | — |
Math MiMo-V2-Pro leads
Llama-3.3-70B-Instruct: 14.3 (#292), MiMo-V2-Pro: 36.7 (#104)
| Benchmark | Llama-3.3-70B-Instruct | MiMo-V2-Pro |
|---|---|---|
| LMArena Math | 1268 | 1447 |
| OTIS Mock AIME 2024-2025 | 5.1% | — |
| LiveBench Math | 42.2% | — |
| MATH Level 5 | 41.6% | — |
Knowledge MiMo-V2-Pro leads
Llama-3.3-70B-Instruct: 30.5 (#220), MiMo-V2-Pro: 40.8 (#106)
| Benchmark | Llama-3.3-70B-Instruct | MiMo-V2-Pro |
|---|---|---|
| LMArena Expert | 1225 | 1479 |
| GPQA Diamond | 47.4% | — |
| Confabulations | 22.8% | — |
| Vectara Hallucination Rate | 4.1% | — |
| MMLU | 86.3% | — |
Multilingual MiMo-V2-Pro leads
Llama-3.3-70B-Instruct: 40.1 (#215), MiMo-V2-Pro: 52.9 (#77)
| Benchmark | Llama-3.3-70B-Instruct | MiMo-V2-Pro |
|---|---|---|
| LMArena Non-English | 1236 | 1416 |
| LMArena Chinese | 1217 | 1455 |
| LMArena French | 1280 | 1470 |
| LMArena German | 1251 | 1417 |
| LMArena Japanese | 1149 | 1366 |
| LMArena Korean | 1142 | 1401 |
| LMArena Russian | 1252 | 1427 |
| LMArena Spanish | 1269 | 1458 |
Instruction Following MiMo-V2-Pro leads
Llama-3.3-70B-Instruct: 71.3 (#152), MiMo-V2-Pro: 76.2 (#46)
| Benchmark | Llama-3.3-70B-Instruct | MiMo-V2-Pro |
|---|---|---|
| LMArena Instruction Following | 1242 | 1444 |
| LiveBench Instruction Following | 82.7% | — |
Long Context MiMo-V2-Pro leads
Llama-3.3-70B-Instruct: 26.4 (#290), MiMo-V2-Pro: 41.5 (#133)
| Benchmark | Llama-3.3-70B-Instruct | MiMo-V2-Pro |
|---|---|---|
| LMArena Longer Query | 1256 | 1455 |
| Fiction.LiveBench | 33.3% | — |
| CL-bench | — | 15.7% |
| CL-bench Life | — | 6.9% |
Writing & Preference MiMo-V2-Pro leads
Llama-3.3-70B-Instruct: 47.8 (#203), MiMo-V2-Pro: 62.9 (#67)
| Benchmark | Llama-3.3-70B-Instruct | MiMo-V2-Pro |
|---|---|---|
| LMArena Text | 1274 | 1436 |
| LMArena Creative Writing | 1250 | 1415 |
| LMArena Multi-Turn | 1279 | 1456 |
| LiveBench Language | 39.2% | — |
Frequently asked questions
Is Llama-3.3-70B-Instruct better than MiMo-V2-Pro?
MiMo-V2-Pro is the stronger model overall, scoring 42.4 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 3.5× less per token, which makes it the better buy when MiMo-V2-Pro's lead doesn't matter for your workload.
Which is cheaper, Llama-3.3-70B-Instruct or MiMo-V2-Pro?
Llama-3.3-70B-Instruct is cheaper. It lists at $0.10 per million input tokens and $0.32 per million output tokens; MiMo-V2-Pro lists at $0.43 and $0.87.
Is Llama-3.3-70B-Instruct or MiMo-V2-Pro better for coding?
MiMo-V2-Pro scores higher on coding benchmarks: 43.4 versus 30.6 in the Noometry coding category.
Which has the bigger context window?
MiMo-V2-Pro does, with 1.05M tokens against 128K.
How many benchmarks do Llama-3.3-70B-Instruct and MiMo-V2-Pro share?
17 benchmarks have published results for both models. Llama-3.3-70B-Instruct has 43 scored results on Noometry and MiMo-V2-Pro has 23.