Model comparison
DeepSeek V4 Pro vs MiMo-V2-Omni
DeepSeek V4 Pro is the stronger model overall, scoring 54.3 to 43.6 on the Noometry Index. MiMo-V2-Omni costs 5.7× less per token, which makes it the better buy when DeepSeek V4 Pro's lead doesn't matter for your workload.
Last verified . 17 shared benchmarks.
DeepSeekSummary
- They share 17 benchmarks with published results for both. DeepSeek V4 Pro scores higher in 8 categories and MiMo-V2-Omni in 0 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where DeepSeek V4 Pro leads 56.5 to 29.7.
- MiMo-V2-Omni is cheaper at $0.14 / $0.28 per million input/output tokens, against $0.66 / $1.98 for DeepSeek V4 Pro.
- DeepSeek V4 Pro accepts more context: 1M tokens versus 262K.
- DeepSeek V4 Pro has downloadable open weights; the other is API-only.
Side by side
| DeepSeek V4 Pro | MiMo-V2-Omni | |
|---|---|---|
| Provider | DeepSeek | Xiaomi |
| Noometry Index | 54.3 | 43.6 |
| Released | 2026-04-24 | 2026-03-18 |
| Weights | Open | Proprietary |
| Context window | 1M | 262K |
| Max output | 393K | 131K |
| Input $ / M tokens | $0.66 | $0.14 |
| Output $ / M tokens | $1.98 | $0.28 |
| Results tracked | 48 | 18 |
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Category by category
Coding DeepSeek V4 Pro leads
DeepSeek V4 Pro: 52.4 (#34), MiMo-V2-Omni: 43.3 (#89)
| Benchmark | DeepSeek V4 Pro | MiMo-V2-Omni |
|---|---|---|
| LMArena Coding | 1470 | 1466 |
| SWE-bench Verified | 77.6% | — |
| FrontierCode | 28.6% | — |
| LMArena WebDev | 1582 | — |
| SciCode | 51% | — |
| WeirdML | 66.2% | — |
| ALE-Bench | 1,403 | — |
Agentic & Tool Use Not comparable
DeepSeek V4 Pro: 32.8 (#58), MiMo-V2-Omni: —
| Benchmark | DeepSeek V4 Pro | MiMo-V2-Omni |
|---|---|---|
| APEX-Agents | 47.3% | — |
| Vending-Bench 2 | 3,285 | — |
Reasoning DeepSeek V4 Pro leads
DeepSeek V4 Pro: 56.5 (#24), MiMo-V2-Omni: 29.7 (#88)
| Benchmark | DeepSeek V4 Pro | MiMo-V2-Omni |
|---|---|---|
| LMArena Hard Prompts | 1461 | 1445 |
| ARC-AGI-2 | 61.3% | — |
| Kagi LLM Benchmark | 53.5% | — |
| NYT Connections (extended) | 91.3% | — |
| ARC-AGI-1 | 90.5% | — |
| CritPt | 18% | — |
| Chess Puzzles | 47% | — |
| 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-Omni: 39.1 (#115)
| Benchmark | DeepSeek V4 Pro | MiMo-V2-Omni |
|---|---|---|
| LMArena Math | 1455 | 1430 |
| 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-Omni: 40.5 (#118)
| Benchmark | DeepSeek V4 Pro | MiMo-V2-Omni |
|---|---|---|
| LMArena Expert | 1464 | 1449 |
| GPQA Diamond | 91.7% | — |
| SimpleQA Verified | 52.9% | — |
| Vectara Hallucination Rate | 8.6% | — |
Multimodal Not comparable
DeepSeek V4 Pro: —, MiMo-V2-Omni: 38.6 (#63)
| Benchmark | DeepSeek V4 Pro | MiMo-V2-Omni |
|---|---|---|
| LMArena Vision | — | 1228 |
Multilingual DeepSeek V4 Pro leads
DeepSeek V4 Pro: 54.4 (#45), MiMo-V2-Omni: 51.8 (#102)
| Benchmark | DeepSeek V4 Pro | MiMo-V2-Omni |
|---|---|---|
| LMArena Non-English | 1439 | 1404 |
| LMArena Chinese | 1486 | 1465 |
| LMArena French | 1472 | 1447 |
| LMArena German | 1458 | 1399 |
| LMArena Japanese | 1445 | 1317 |
| LMArena Korean | 1447 | 1355 |
| LMArena Russian | 1453 | 1412 |
| LMArena Spanish | 1458 | 1434 |
Instruction Following Too close to call
DeepSeek V4 Pro: 76.1 (#47), MiMo-V2-Omni: 75.2 (#66)
| Benchmark | DeepSeek V4 Pro | MiMo-V2-Omni |
|---|---|---|
| LMArena Instruction Following | 1448 | 1428 |
Long Context Too close to call
DeepSeek V4 Pro: 45.0 (#51), MiMo-V2-Omni: 44.1 (#76)
| Benchmark | DeepSeek V4 Pro | MiMo-V2-Omni |
|---|---|---|
| LMArena Longer Query | 1458 | 1442 |
| CL-bench Life | 13.5% | — |
Writing & Preference DeepSeek V4 Pro leads
DeepSeek V4 Pro: 65.5 (#46), MiMo-V2-Omni: 61.4 (#87)
| Benchmark | DeepSeek V4 Pro | MiMo-V2-Omni |
|---|---|---|
| LMArena Text | 1451 | 1423 |
| LMArena Creative Writing | 1446 | 1392 |
| LMArena Multi-Turn | 1467 | 1445 |
| EQ-Bench Creative Writing | 1553 | — |
| EQ-Bench 4 | 1166 | — |
Frequently asked questions
Is DeepSeek V4 Pro better than MiMo-V2-Omni?
DeepSeek V4 Pro is the stronger model overall, scoring 54.3 to 43.6 on the Noometry Index. MiMo-V2-Omni costs 5.7× 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-Omni?
MiMo-V2-Omni is cheaper. It lists at $0.14 per million input tokens and $0.28 per million output tokens; DeepSeek V4 Pro lists at $0.66 and $1.98.
Is DeepSeek V4 Pro or MiMo-V2-Omni better for coding?
DeepSeek V4 Pro scores higher on coding benchmarks: 52.4 versus 43.3 in the Noometry coding category.
Which has the bigger context window?
DeepSeek V4 Pro does, with 1M tokens against 262K.
How many benchmarks do DeepSeek V4 Pro and MiMo-V2-Omni share?
17 benchmarks have published results for both models. DeepSeek V4 Pro has 48 scored results on Noometry and MiMo-V2-Omni has 18.