Model comparison
DeepSeek-V3.2-Exp vs MiMo-V2.6-Pro
MiMo-V2.6-Pro is the stronger model overall, scoring 46.7 to 44.9 on the Noometry Index. DeepSeek-V3.2-Exp costs 1.9× less per token, which makes it the better buy when MiMo-V2.6-Pro's lead doesn't matter for your workload.
Last verified . 13 shared benchmarks.
DeepSeekSummary
- They share 13 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 3 categories and MiMo-V2.6-Pro in 5 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where DeepSeek-V3.2-Exp leads 51.6 to 43.0.
- DeepSeek-V3.2-Exp is cheaper at $0.26 / $0.38 per million input/output tokens, against $0.43 / $0.87 for MiMo-V2.6-Pro.
- MiMo-V2.6-Pro accepts more context: 1.05M tokens versus 164K.
Side by side
| DeepSeek-V3.2-Exp | MiMo-V2.6-Pro | |
|---|---|---|
| Provider | DeepSeek | Xiaomi |
| Noometry Index | 44.9 | 46.7 |
| Released | 2025-09-29 | 2026-09-22 |
| Weights | Open | Open |
| Context window | 164K | 1.05M |
| Max output | 66K | 131K |
| Input $ / M tokens | $0.26 | $0.43 |
| Output $ / M tokens | $0.38 | $0.87 |
| Results tracked | 48 | 14 |
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Category by category
Coding MiMo-V2.6-Pro leads
DeepSeek-V3.2-Exp: 46.4 (#61), MiMo-V2.6-Pro: 53.2 (#27)
| Benchmark | DeepSeek-V3.2-Exp | MiMo-V2.6-Pro |
|---|---|---|
| LMArena WebDev | 1362 | 1618 |
| LMArena Coding | 1454 | 1538 |
| SWE-bench Verified (bash only) | 70% | — |
| Aider Polyglot | 74.2% | — |
| SWE-bench Multilingual | 59% | — |
| SciCode | 38.9% | — |
| WeirdML | 39.5% | — |
Agentic & Tool Use Not comparable
DeepSeek-V3.2-Exp: 37.9 (#31), MiMo-V2.6-Pro: —
| Benchmark | DeepSeek-V3.2-Exp | MiMo-V2.6-Pro |
|---|---|---|
| Terminal-Bench | 39.6% | — |
| Berkeley Function Calling Leaderboard | 56.7% | — |
| TheAgentCompany | 42.9% | — |
| Vending-Bench 2 | 1,034 | — |
Reasoning MiMo-V2.6-Pro leads
DeepSeek-V3.2-Exp: 22.0 (#185), MiMo-V2.6-Pro: 30.4 (#76)
| Benchmark | DeepSeek-V3.2-Exp | MiMo-V2.6-Pro |
|---|---|---|
| LMArena Hard Prompts | 1433 | 1511 |
| ARC-AGI-2 | 4% | — |
| Kagi LLM Benchmark | 52.2% | — |
| NYT Connections (extended) | 36.7% | — |
| ARC-AGI-1 | 57% | — |
| CritPt | 2.9% | — |
| Chess Puzzles | 14% | — |
| Thematic Generalization | 65% | — |
| DTBench | 87.7% | — |
| LMCA | 29.1% | — |
| Epoch Capabilities Index | 146.28 | — |
Math DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 40.8 (#75), MiMo-V2.6-Pro: 37.9 (#91)
| Benchmark | DeepSeek-V3.2-Exp | MiMo-V2.6-Pro |
|---|---|---|
| LMArena Math | 1435 | 1497 |
| MathArena Final-Answer Competitions | 57.7% | — |
| OTIS Mock AIME 2024-2025 | 87.8% | — |
| ProofBench | 8% | — |
| FrontierMath (Feb 2025 set) | 22.1% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 51.6 (#61), MiMo-V2.6-Pro: 43.0 (#85)
| Benchmark | DeepSeek-V3.2-Exp | MiMo-V2.6-Pro |
|---|---|---|
| LMArena Expert | 1436 | 1548 |
| GPQA Diamond | 83.4% | — |
| Vectara Hallucination Rate | 5.3% | — |
Multimodal Not comparable
DeepSeek-V3.2-Exp: —, MiMo-V2.6-Pro: 40.2 (#40)
| Benchmark | DeepSeek-V3.2-Exp | MiMo-V2.6-Pro |
|---|---|---|
| LMArena Vision | — | 1265 |
Multilingual MiMo-V2.6-Pro leads
DeepSeek-V3.2-Exp: 52.4 (#85), MiMo-V2.6-Pro: 57.0 (#15)
| Benchmark | DeepSeek-V3.2-Exp | MiMo-V2.6-Pro |
|---|---|---|
| LMArena Non-English | 1409 | 1474 |
| LMArena Chinese | 1461 | 1532 |
| LMArena Russian | 1423 | 1481 |
| LMArena French | 1435 | — |
| LMArena German | 1440 | — |
| LMArena Japanese | 1377 | — |
| LMArena Korean | 1372 | — |
| LMArena Spanish | 1441 | — |
Instruction Following MiMo-V2.6-Pro leads
DeepSeek-V3.2-Exp: 74.7 (#89), MiMo-V2.6-Pro: 78.2 (#10)
| Benchmark | DeepSeek-V3.2-Exp | MiMo-V2.6-Pro |
|---|---|---|
| LMArena Instruction Following | 1413 | 1490 |
Long Context DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 47.6 (#15), MiMo-V2.6-Pro: 46.0 (#26)
| Benchmark | DeepSeek-V3.2-Exp | MiMo-V2.6-Pro |
|---|---|---|
| LMArena Longer Query | 1428 | 1500 |
| Fiction.LiveBench | 83.3% | — |
| CL-bench | 13.2% | — |
| CL-bench Life | 9.5% | — |
Writing & Preference MiMo-V2.6-Pro leads
DeepSeek-V3.2-Exp: 62.5 (#73), MiMo-V2.6-Pro: 66.9 (#32)
| Benchmark | DeepSeek-V3.2-Exp | MiMo-V2.6-Pro |
|---|---|---|
| LMArena Text | 1425 | 1491 |
| LMArena Creative Writing | 1403 | 1465 |
| LMArena Multi-Turn | 1427 | 1463 |
| EQ-Bench Creative Writing | 1515 | — |
Frequently asked questions
Is DeepSeek-V3.2-Exp better than MiMo-V2.6-Pro?
MiMo-V2.6-Pro is the stronger model overall, scoring 46.7 to 44.9 on the Noometry Index. DeepSeek-V3.2-Exp costs 1.9× less per token, which makes it the better buy when MiMo-V2.6-Pro's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-V3.2-Exp or MiMo-V2.6-Pro?
DeepSeek-V3.2-Exp is cheaper. It lists at $0.26 per million input tokens and $0.38 per million output tokens; MiMo-V2.6-Pro lists at $0.43 and $0.87.
Is DeepSeek-V3.2-Exp or MiMo-V2.6-Pro better for coding?
MiMo-V2.6-Pro scores higher on coding benchmarks: 53.2 versus 46.4 in the Noometry coding category.
Which has the bigger context window?
MiMo-V2.6-Pro does, with 1.05M tokens against 164K.
How many benchmarks do DeepSeek-V3.2-Exp and MiMo-V2.6-Pro share?
13 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 48 scored results on Noometry and MiMo-V2.6-Pro has 14.