Model comparison
DeepSeek V4 Pro vs MiMo-V2-Pro
DeepSeek V4 Pro is the stronger model overall, scoring 54.3 to 43.0 on the Noometry Index. MiMo-V2-Pro costs 1.8× less per token, which makes it the better buy when DeepSeek V4 Pro's lead doesn't matter for your workload.
Last verified . 21 shared benchmarks.
DeepSeekSummary
- They share 21 benchmarks with published results for both. DeepSeek V4 Pro scores higher in 8 categories and MiMo-V2-Pro in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where DeepSeek V4 Pro leads 56.5 to 22.1.
- The biggest single-benchmark swing is NYT Connections (extended): 91.3% for DeepSeek V4 Pro and 25.8% for MiMo-V2-Pro.
- MiMo-V2-Pro is cheaper at $0.43 / $0.87 per million input/output tokens, against $0.66 / $1.98 for DeepSeek V4 Pro.
- MiMo-V2-Pro accepts more context: 1.05M tokens versus 1M.
- DeepSeek V4 Pro has downloadable open weights; the other is API-only.
Side by side
| DeepSeek V4 Pro | MiMo-V2-Pro | |
|---|---|---|
| Provider | DeepSeek | Xiaomi |
| Noometry Index | 54.3 | 43.0 |
| Released | 2026-04-24 | 2026-03-18 |
| Weights | Open | Proprietary |
| Context window | 1M | 1.05M |
| Max output | 393K | 131K |
| Input $ / M tokens | $0.66 | $0.43 |
| Output $ / M tokens | $1.98 | $0.87 |
| Results tracked | 48 | 23 |
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Category by category
Coding DeepSeek V4 Pro leads
DeepSeek V4 Pro: 52.4 (#34), MiMo-V2-Pro: 43.8 (#83)
| Benchmark | DeepSeek V4 Pro | MiMo-V2-Pro |
|---|---|---|
| LMArena WebDev | 1582 | 1433 |
| LMArena Coding | 1470 | 1476 |
| ALE-Bench | 1,403 | 785.17 |
| SWE-bench Verified | 77.6% | — |
| FrontierCode | 28.6% | — |
| SciCode | 51% | — |
| WeirdML | 66.2% | — |
Agentic & Tool Use Not comparable
DeepSeek V4 Pro: 32.8 (#58), MiMo-V2-Pro: —
| Benchmark | DeepSeek V4 Pro | MiMo-V2-Pro |
|---|---|---|
| APEX-Agents | 47.3% | — |
| Vending-Bench 2 | 3,285 | — |
Reasoning DeepSeek V4 Pro leads
DeepSeek V4 Pro: 56.5 (#24), MiMo-V2-Pro: 22.1 (#206)
| Benchmark | DeepSeek V4 Pro | MiMo-V2-Pro |
|---|---|---|
| NYT Connections (extended) | 91.3% | 25.8% |
| LMArena Hard Prompts | 1461 | 1457 |
| ARC-AGI-2 | 61.3% | — |
| Kagi LLM Benchmark | 53.5% | — |
| ARC-AGI-1 | 90.5% | — |
| CritPt | 18% | — |
| Chess Puzzles | 47% | — |
| Thematic Generalization | — | 45.9% |
| Mystery Game Puzzles | 43% | — |
| DTBench | 93.9% | — |
| LMCA | 45.5% | — |
| Surface Evolver Bench | 40% | — |
| Epoch Capabilities Index | 155.31 | — |
| ForecastBench | 56.1 | — |
Math DeepSeek V4 Pro leads
DeepSeek V4 Pro: 64.8 (#30), MiMo-V2-Pro: 39.5 (#102)
| Benchmark | DeepSeek V4 Pro | MiMo-V2-Pro |
|---|---|---|
| LMArena Math | 1455 | 1447 |
| FrontierMath (Tiers 1-3) | 64.6% | — |
| FrontierMath Tier 4 | 26.8% | — |
| MathArena Final-Answer Competitions | 76.6% | — |
| OTIS Mock AIME 2024-2025 | 98.6% | — |
| ProofBench | 50% | — |
Knowledge DeepSeek V4 Pro leads
DeepSeek V4 Pro: 59.5 (#31), MiMo-V2-Pro: 41.4 (#111)
| Benchmark | DeepSeek V4 Pro | MiMo-V2-Pro |
|---|---|---|
| LMArena Expert | 1464 | 1478 |
| GPQA Diamond | 91.7% | — |
| SimpleQA Verified | 52.9% | — |
| Vectara Hallucination Rate | 8.6% | — |
Multilingual DeepSeek V4 Pro leads
DeepSeek V4 Pro: 54.4 (#45), MiMo-V2-Pro: 52.7 (#81)
| Benchmark | DeepSeek V4 Pro | MiMo-V2-Pro |
|---|---|---|
| LMArena Non-English | 1439 | 1416 |
| LMArena Chinese | 1486 | 1456 |
| LMArena French | 1472 | 1469 |
| LMArena German | 1458 | 1417 |
| LMArena Japanese | 1445 | 1366 |
| LMArena Korean | 1447 | 1400 |
| LMArena Russian | 1453 | 1427 |
| LMArena Spanish | 1458 | 1457 |
Instruction Following Too close to call
DeepSeek V4 Pro: 76.1 (#47), MiMo-V2-Pro: 76.0 (#49)
| Benchmark | DeepSeek V4 Pro | MiMo-V2-Pro |
|---|---|---|
| LMArena Instruction Following | 1448 | 1445 |
Long Context DeepSeek V4 Pro leads
DeepSeek V4 Pro: 45.0 (#51), MiMo-V2-Pro: 41.5 (#138)
| Benchmark | DeepSeek V4 Pro | MiMo-V2-Pro |
|---|---|---|
| CL-bench Life | 13.5% | 6.9% |
| LMArena Longer Query | 1458 | 1455 |
| CL-bench | — | 15.7% |
Writing & Preference DeepSeek V4 Pro leads
DeepSeek V4 Pro: 65.5 (#46), MiMo-V2-Pro: 62.8 (#70)
| Benchmark | DeepSeek V4 Pro | MiMo-V2-Pro |
|---|---|---|
| LMArena Text | 1451 | 1436 |
| LMArena Creative Writing | 1446 | 1415 |
| LMArena Multi-Turn | 1467 | 1456 |
| EQ-Bench Creative Writing | 1553 | — |
| EQ-Bench 4 | 1166 | — |
Frequently asked questions
Is DeepSeek V4 Pro better than MiMo-V2-Pro?
DeepSeek V4 Pro is the stronger model overall, scoring 54.3 to 43.0 on the Noometry Index. MiMo-V2-Pro costs 1.8× less per token, which makes it the better buy when DeepSeek V4 Pro's lead doesn't matter for your workload.
Which is cheaper, DeepSeek V4 Pro or MiMo-V2-Pro?
MiMo-V2-Pro is cheaper. It lists at $0.43 per million input tokens and $0.87 per million output tokens; DeepSeek V4 Pro lists at $0.66 and $1.98.
Is DeepSeek V4 Pro or MiMo-V2-Pro better for coding?
DeepSeek V4 Pro scores higher on coding benchmarks: 52.4 versus 43.8 in the Noometry coding category.
Which has the bigger context window?
MiMo-V2-Pro does, with 1.05M tokens against 1M.
How many benchmarks do DeepSeek V4 Pro and MiMo-V2-Pro share?
21 benchmarks have published results for both models. DeepSeek V4 Pro has 48 scored results on Noometry and MiMo-V2-Pro has 23.