Model comparison
DeepSeek V3.2 vs MiMo-V2.5-Pro
Head-to-head evidence from 14 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: DeepSeek V3.2 #82 (Supported); MiMo-V2.5-Pro #14 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. DeepSeek V3.2 and MiMo-V2.5-Pro share 14 comparable benchmark results. 1 of 8 categories are comparable. 5 results are unique to DeepSeek V3.2; 17 to MiMo-V2.5-Pro.
Updated July 21, 2026- Shared results
- 14
- DeepSeek V3.2 only
- 5
- MiMo-V2.5-Pro only
- 17
- Comparable categories
- 1 / 8
Pick MiMo-V2.5-Pro if you want the stronger benchmark profile. DeepSeek V3.2 only becomes the better choice if coding is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Confidence note. This is a partial-evidence comparison with 14 shared benchmark results across 6 evidence categories; 1 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
MiMo-V2.5-Pro is clearly ahead on the BenchAlign aggregate, 70.19 to 55.4. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
MiMo-V2.5-Pro is the reasoning model in the pair, while DeepSeek V3.2 is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. MiMo-V2.5-Pro gives you the larger context window at 1M, compared with 128K for DeepSeek V3.2.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
| Category | DeepSeek V3.2 | Δ | MiMo-V2.5-Pro |
|---|---|---|---|
| Coding | DeepSeek V3.260.9 | Margin← 3.7 | MiMo-V2.5-Pro57.2 |
| Agentic | DeepSeek V3.2Not measured | MarginNo overlap | MiMo-V2.5-Pro68.4 |
| Knowledge | DeepSeek V3.2Not measured | MarginNo overlap | MiMo-V2.5-Pro48.0 |
| Math | DeepSeek V3.217.1 | MarginNo overlap | MiMo-V2.5-ProNot measured |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | DeepSeek V3.2 | MiMo-V2.5-Pro | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V3.2$0.28 input / $0.42 output | MiMo-V2.5-ProNot available | A complete price comparison is not available. |
| Generation speedtokens per second | DeepSeek V3.235 tok/s | MiMo-V2.5-ProNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V3.23.75 s | MiMo-V2.5-ProNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V3.2128K | MiMo-V2.5-Pro1M | MiMo-V2.5-Pro lists the larger context window. |
Benchmark Deep Dive
Agentic15 benchmarks
| Benchmark | DeepSeek V3.2 | MiMo-V2.5-Pro | Result |
|---|---|---|---|
| Claw-EvalSource | 40.2% | 63.8% | MiMo-V2.5-Pro leads |
| VITA-BenchSource | 18.5% | — | Not comparable |
| τ²-bench resultsSource | 78.9% | 94.2% | MiMo-V2.5-Pro leads |
| Gert LabsSource | 29.57% | 62.70% | MiMo-V2.5-Pro leads |
| GDPval-AASource | — | 1265 | Not comparable |
| τ³-bench resultsSource | — | 72.9% | Not comparable |
| Terminal-Bench 2.0Source | — | 68.4% | Not comparable |
| AA Agentic IndexSource | — | 29.1% | Not comparable |
| GDPval-AASource | — | 38.3% | Not comparable |
| APEX-Agents-AASource | — | 2.4% | Not comparable |
| AA BriefcaseSource | — | 873 | Not comparable |
| AA ITBenchSource | — | 38.2% | Not comparable |
| terminalBenchHardSource | — | 43.2% | Not comparable |
| aaTerminalBench21Source | — | 65.2% | Not comparable |
| AA Harvey LABSource | — | 73.3% | Not comparable |
CodingDeepSeek V3.2 wins6 benchmarks
Reasoning2 benchmarks
Knowledge9 benchmarks
| Benchmark | DeepSeek V3.2 | MiMo-V2.5-Pro | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 24.7% | 42.2% | MiMo-V2.5-Pro leads |
| AA-GPQA DiamondSource | 75.1% | 86.6% | MiMo-V2.5-Pro leads |
| AA-HLESource | 10.5% | 33.8% | MiMo-V2.5-Pro leads |
| AA-Omniscience IndexSource | -46.7% | 3.6% | MiMo-V2.5-Pro leads |
| AA-Omniscience AccuracySource | 24.2% | 22.6% | DeepSeek V3.2 leads |
| AA-Omniscience Hallucination RateSource | 93.5% | 24.5% | MiMo-V2.5-Pro leads |
| HLESource | — | 48% | Not comparable |
| HLE w/o toolsSource | — | 34% | Not comparable |
| AA Openness IndexSource | — | 38.9% | Not comparable |
Math2 benchmarks
Multimodal1 benchmarks
| Benchmark | DeepSeek V3.2 | MiMo-V2.5-Pro | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1204 | 1298 | MiMo-V2.5-Pro leads |
Inst. Following1 benchmarks
| Benchmark | DeepSeek V3.2 | MiMo-V2.5-Pro | Result |
|---|---|---|---|
| AA-IFBenchSource | 49.0% | 79.9% | MiMo-V2.5-Pro leads |
Frequently Asked Questions (2)
Which is better, DeepSeek V3.2 or MiMo-V2.5-Pro?
MiMo-V2.5-Pro is ahead on BenchLM's BenchAlign leaderboard, 70.19 to 55.4.
Which is better for coding, DeepSeek V3.2 or MiMo-V2.5-Pro?
DeepSeek V3.2 has the edge for coding in this comparison, averaging 60.9 versus 57.2. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
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