Model Comparison

DeepSeek VL2 vs MiMo-V2-Flash

Comparing DeepSeek VL2 and MiMo-V2-Flash across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek VL2 and MiMo-V2-Flash don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Cost data unavailable.

Lowest available price from all providers
Thu Apr 16 2026 • llm-stats.com
DeepSeek
DeepSeek VL2
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
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

282.0B diff

MiMo-V2-Flash has 282.0B more parameters than DeepSeek VL2, making it 1044.4% larger.

DeepSeek
DeepSeek VL2
27.0Bparameters
Xiaomi
MiMo-V2-Flash
309.0Bparameters
27.0B
DeepSeek VL2
309.0B
MiMo-V2-Flash

Context Window

Maximum input and output token capacity

MiMo-V2-Flash accepts 256,000 input tokens compared to DeepSeek VL2's 129,280 tokens. DeepSeek VL2 can generate longer responses up to 129,280 tokens, while MiMo-V2-Flash is limited to 16,384 tokens.

DeepSeek
DeepSeek VL2
Input129,280 tokens
Output129,280 tokens
Xiaomi
MiMo-V2-Flash
Input256,000 tokens
Output16,384 tokens
Thu Apr 16 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

DeepSeek VL2 supports multimodal inputs, whereas MiMo-V2-Flash does not.

DeepSeek VL2 can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek VL2

Text
Images
Audio
Video

MiMo-V2-Flash

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek VL2 is licensed under deepseek, while MiMo-V2-Flash uses MIT.

License differences may affect how you can use these models in commercial or open-source projects.

DeepSeek VL2

deepseek

Open weights

MiMo-V2-Flash

MIT

Open weights

Release Timeline

When each model was launched

DeepSeek VL2 was released on 2024-12-13, while MiMo-V2-Flash was released on 2025-12-16.

MiMo-V2-Flash is 12 months newer than DeepSeek VL2.

DeepSeek VL2

Dec 13, 2024

1.3 years ago

MiMo-V2-Flash

Dec 16, 2025

4 months ago

1.0yr 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 VL2 is available from Replicate. MiMo-V2-Flash is available from Xiaomi.

DeepSeek VL2

replicate logo
Replicate

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
Supports multimodal inputs
Xiaomi
Larger context window (256,000 tokens)

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek VL2
Xiaomi
MiMo-V2-Flash

FAQ

Common questions about DeepSeek VL2 vs MiMo-V2-Flash

DeepSeek VL2 (DeepSeek) and MiMo-V2-Flash (Xiaomi) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.
DeepSeek VL2 scores DocVQA: 93.3%, ChartQA: 86.0%, TextVQA: 84.2%, AI2D: 81.4%, OCRBench: 81.1%. MiMo-V2-Flash scores AIME 2025: 94.1%, Arena-Hard v2: 86.2%, MMLU-Pro: 84.9%, HMMT 2025: 84.4%, GPQA: 83.7%.
DeepSeek VL2 supports 129K tokens and MiMo-V2-Flash supports 256K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include context window (129K vs 256K), multimodal support (yes vs no), licensing (deepseek vs MIT). See the full comparison above for benchmark-by-benchmark results.
DeepSeek VL2 is developed by DeepSeek and MiMo-V2-Flash is developed by Xiaomi.