Compare · ModelsLive · 2 picked · head to head
DeepSeek V3.2 vs MiMo-V2-Flash
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
DeepSeek V3.2 wins on 10/11 benchmarks
DeepSeek V3.2 wins 10 of 11 shared benchmarks. Leads in speed · math · knowledge.
Category leads
speed·DeepSeek V3.2arena·MiMo-V2-Flashmath·DeepSeek V3.2knowledge·DeepSeek V3.2language·DeepSeek V3.2coding·DeepSeek V3.2
Hype vs Reality
Attention vs performance
DeepSeek V3.2
#84 by perf·no signal
MiMo-V2-Flash
#11 by perf·#12 by attention
Best value
MiMo-V2-Flash
2.3x better value than DeepSeek V3.2
DeepSeek V3.2
168.3 pts/$
$0.32/M
MiMo-V2-Flash
385.8 pts/$
$0.19/M
Vendor risk
Mixed exposure
One or more vendors flagged
DeepSeek
$3.4B·Tier 1
xiaomi
private · undisclosed
Head to head
11 benchmarks · 2 models
DeepSeek V3.2MiMo-V2-Flash
Artificial Analysis · Agentic Index
DeepSeek V3.2 leads by +4.2
Artificial Analysis Agentic Index · a composite score measuring how well a model performs in agentic workflows · multi-step tool use, planning, error recovery, and autonomous task completion. Aggregates results from multiple agentic benchmarks including SWE-bench, tool-use tests, and planning evaluations. The canonical single-number metric for "how good is this model as an agent?"
DeepSeek V3.2
52.9
MiMo-V2-Flash
48.8
Artificial Analysis · Coding Index
DeepSeek V3.2 leads by +3.2
Artificial Analysis Coding Index · a composite score that aggregates performance across multiple coding benchmarks into a single index. Tracks code generation quality, debugging ability, multi-language competence, and real-world software engineering tasks. Used by Artificial Analysis to rank model coding capability in a normalized, comparable format. Useful for developers choosing between models for coding-heavy workloads.
DeepSeek V3.2
36.7
MiMo-V2-Flash
33.5
Artificial Analysis · Quality Index
DeepSeek V3.2 leads by +0.3
DeepSeek V3.2
41.7
MiMo-V2-Flash
41.5
Chatbot Arena Elo · Coding
MiMo-V2-Flash leads by +9.7
DeepSeek V3.2
1326.9
MiMo-V2-Flash
1336.5
Chatbot Arena Elo · Overall
DeepSeek V3.2 leads by +32.3
DeepSeek V3.2
1424.4
MiMo-V2-Flash
1392.1
OpenCompass · AIME2025
DeepSeek V3.2 leads by +0.1
DeepSeek V3.2
93.0
MiMo-V2-Flash
92.9
OpenCompass · GPQA-Diamond
DeepSeek V3.2 leads by +2.5
DeepSeek V3.2
84.6
MiMo-V2-Flash
82.1
OpenCompass · HLE
DeepSeek V3.2 leads by +2.7
DeepSeek V3.2
23.2
MiMo-V2-Flash
20.5
OpenCompass · IFEval
DeepSeek V3.2 leads by +0.2
DeepSeek V3.2
89.7
MiMo-V2-Flash
89.5
OpenCompass · LiveCodeBenchV6
DeepSeek V3.2 leads by +3.6
DeepSeek V3.2
75.4
MiMo-V2-Flash
71.8
OpenCompass · MMLU-Pro
DeepSeek V3.2 leads by +2.7
DeepSeek V3.2
85.8
MiMo-V2-Flash
83.1
Full benchmark table
| Benchmark | DeepSeek V3.2 | MiMo-V2-Flash |
|---|---|---|
Artificial Analysis · Agentic Index Artificial Analysis Agentic Index · a composite score measuring how well a model performs in agentic workflows · multi-step tool use, planning, error recovery, and autonomous task completion. Aggregates results from multiple agentic benchmarks including SWE-bench, tool-use tests, and planning evaluations. The canonical single-number metric for "how good is this model as an agent?" | 52.9 | 48.8 |
Artificial Analysis · Coding Index Artificial Analysis Coding Index · a composite score that aggregates performance across multiple coding benchmarks into a single index. Tracks code generation quality, debugging ability, multi-language competence, and real-world software engineering tasks. Used by Artificial Analysis to rank model coding capability in a normalized, comparable format. Useful for developers choosing between models for coding-heavy workloads. | 36.7 | 33.5 |
Artificial Analysis · Quality Index | 41.7 | 41.5 |
Chatbot Arena Elo · Coding | 1326.9 | 1336.5 |
Chatbot Arena Elo · Overall | 1424.4 | 1392.1 |
OpenCompass · AIME2025 | 93.0 | 92.9 |
OpenCompass · GPQA-Diamond | 84.6 | 82.1 |
OpenCompass · HLE | 23.2 | 20.5 |
OpenCompass · IFEval | 89.7 | 89.5 |
OpenCompass · LiveCodeBenchV6 | 75.4 | 71.8 |
OpenCompass · MMLU-Pro | 85.8 | 83.1 |
Pricing · per 1M tokens · projected $/mo at 10M tokens
| Model | Input | Output | Context | Projected $/mo |
|---|---|---|---|---|
| $0.25 | $0.38 | 131K tokens (~66 books) | $2.83 | |
| $0.09 | $0.29 | 262K tokens (~131 books) | $1.40 |