Model Comparison

DeepSeek-V3.2 (Thinking) vs MiMo-V2-Flash

MiMo-V2-Flash has a slight edge in benchmark performance. MiMo-V2-Flash is 2.1x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

9 benchmarks

DeepSeek-V3.2 (Thinking) outperforms in 4 benchmarks (HMMT 2025, Humanity's Last Exam, MMLU-Pro, Terminal-Bench 2.0), while MiMo-V2-Flash is better at 5 benchmarks (AIME 2025, BrowseComp, GPQA, SWE-bench Multilingual, SWE-Bench Verified).

MiMo-V2-Flash has a slight edge in benchmark performance.

Tue Jun 02 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

MiMo-V2-Flash costs less

For input processing, DeepSeek-V3.2 (Thinking) ($0.28/1M tokens) is 2.8x more expensive than MiMo-V2-Flash ($0.10/1M tokens).

For output processing, DeepSeek-V3.2 (Thinking) ($0.42/1M tokens) is 1.4x more expensive than MiMo-V2-Flash ($0.30/1M tokens).

In conclusion, DeepSeek-V3.2 (Thinking) is more expensive than MiMo-V2-Flash.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Tue Jun 02 2026 • llm-stats.com
DeepSeek
DeepSeek-V3.2 (Thinking)
Input tokens$0.28
Output tokens$0.42
Best providerDeepSeek
Xiaomi
MiMo-V2-Flash
Input tokens$0.10
Output tokens$0.30
Best providerXiaomi
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

376.0B diff

DeepSeek-V3.2 (Thinking) has 376.0B more parameters than MiMo-V2-Flash, making it 121.7% larger.

DeepSeek
DeepSeek-V3.2 (Thinking)
685.0Bparameters
Xiaomi
MiMo-V2-Flash
309.0Bparameters
685.0B
DeepSeek-V3.2 (Thinking)
309.0B
MiMo-V2-Flash

Context Window

Maximum input and output token capacity

MiMo-V2-Flash accepts 256,000 input tokens compared to DeepSeek-V3.2 (Thinking)'s 131,072 tokens. DeepSeek-V3.2 (Thinking) can generate longer responses up to 65,536 tokens, while MiMo-V2-Flash is limited to 16,384 tokens.

DeepSeek
DeepSeek-V3.2 (Thinking)
Input131,072 tokens
Output65,536 tokens
Xiaomi
MiMo-V2-Flash
Input256,000 tokens
Output16,384 tokens
Tue Jun 02 2026 • llm-stats.com

License

Usage and distribution terms

Both models are licensed under MIT.

Both models share the same licensing terms, providing consistent usage rights.

DeepSeek-V3.2 (Thinking)

MIT

Open weights

MiMo-V2-Flash

MIT

Open weights

Release Timeline

When each model was launched

DeepSeek-V3.2 (Thinking) was released on 2025-12-01, while MiMo-V2-Flash was released on 2025-12-16.

MiMo-V2-Flash is 1 month newer than DeepSeek-V3.2 (Thinking).

DeepSeek-V3.2 (Thinking)

Dec 1, 2025

6 months ago

MiMo-V2-Flash

Dec 16, 2025

5 months ago

2w newer

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Provider Availability

DeepSeek-V3.2 (Thinking) is available from DeepSeek. MiMo-V2-Flash is available from Xiaomi.

DeepSeek-V3.2 (Thinking)

deepseek logo
DeepSeek
Input Price:Input: $0.28/1MOutput Price:Output: $0.42/1M

MiMo-V2-Flash

xiaomi logo
Xiaomi
Input Price:Input: $0.10/1MOutput Price:Output: $0.30/1M
* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

DeepSeek
Higher HMMT 2025 score (90.2% vs 84.4%)
Higher Humanity's Last Exam score (25.1% vs 22.1%)
Higher MMLU-Pro score (85.0% vs 84.9%)
Higher Terminal-Bench 2.0 score (46.4% vs 38.5%)
Xiaomi
Larger context window (256,000 tokens)
Less expensive input tokens
Less expensive output tokens
Higher AIME 2025 score (94.1% vs 93.1%)
Higher BrowseComp score (58.3% vs 51.4%)
Higher GPQA score (83.7% vs 82.4%)
Higher SWE-bench Multilingual score (71.7% vs 70.2%)
Higher SWE-Bench Verified score (73.4% vs 73.1%)

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V3.2 (Thinking)
Xiaomi
MiMo-V2-Flash

FAQ

Common questions about DeepSeek-V3.2 (Thinking) vs MiMo-V2-Flash.

Which is better, DeepSeek-V3.2 (Thinking) or MiMo-V2-Flash?

MiMo-V2-Flash has a slight edge in benchmark performance. DeepSeek-V3.2 (Thinking) is made by DeepSeek and MiMo-V2-Flash is made by Xiaomi. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does DeepSeek-V3.2 (Thinking) compare to MiMo-V2-Flash in benchmarks?

DeepSeek-V3.2 (Thinking) scores AIME 2025: 93.1%, HMMT 2025: 90.2%, MMLU-Pro: 85.0%, LiveCodeBench: 83.3%, GPQA: 82.4%. MiMo-V2-Flash scores AIME 2025: 94.1%, Arena-Hard v2: 86.2%, MMLU-Pro: 84.9%, HMMT 2025: 84.4%, GPQA: 83.7%.

Is DeepSeek-V3.2 (Thinking) cheaper than MiMo-V2-Flash?

MiMo-V2-Flash is 2.8x cheaper for input tokens. DeepSeek-V3.2 (Thinking) costs $0.28/M input and $0.42/M output via deepseek. MiMo-V2-Flash costs $0.10/M input and $0.30/M output via xiaomi.

What are the context window sizes for DeepSeek-V3.2 (Thinking) and MiMo-V2-Flash?

DeepSeek-V3.2 (Thinking) supports 131K tokens and MiMo-V2-Flash supports 256K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between DeepSeek-V3.2 (Thinking) and MiMo-V2-Flash?

Key differences include context window (131K vs 256K), input pricing ($0.28 vs $0.10/M). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V3.2 (Thinking) and MiMo-V2-Flash?

DeepSeek-V3.2 (Thinking) is developed by DeepSeek and MiMo-V2-Flash is developed by Xiaomi.