Model Comparison

Command R+ vs DeepSeek VL2 Small

Comparing Command R+ and DeepSeek VL2 Small across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

Command R+ and DeepSeek VL2 Small don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Human preference votes

Model Size

Parameter count comparison

88.0B diff

Command R+ has 88.0B more parameters than DeepSeek VL2 Small, making it 550.0% larger.

Cohere
Command R+
104.0Bparameters
DeepSeek
DeepSeek VL2 Small
16.0Bparameters
104.0B
Command R+
16.0B
DeepSeek VL2 Small

Context Window

Maximum input and output token capacity

Only Command R+ specifies input context (128,000 tokens). Only Command R+ specifies output context (128,000 tokens).

Cohere
Command R+
Input128,000 tokens
Output128,000 tokens
DeepSeek
DeepSeek VL2 Small
Input- tokens
Output- tokens
Sat May 02 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

DeepSeek VL2 Small supports multimodal inputs, whereas Command R+ does not.

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

Command R+

Text
Images
Audio
Video

DeepSeek VL2 Small

Text
Images
Audio
Video

License

Usage and distribution terms

Command R+ is licensed under CC BY-NC, while DeepSeek VL2 Small uses deepseek.

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

Command R+

CC BY-NC

Open weights

DeepSeek VL2 Small

deepseek

Open weights

Release Timeline

When each model was launched

Command R+ was released on 2024-08-30, while DeepSeek VL2 Small was released on 2024-12-13.

DeepSeek VL2 Small is 4 months newer than Command R+.

Command R+

Aug 30, 2024

1.7 years ago

DeepSeek VL2 Small

Dec 13, 2024

1.4 years ago

3mo 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

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

Cohere
Larger context window (128,000 tokens)
DeepSeek
Supports multimodal inputs

Detailed Comparison

AI Model Comparison Table
Feature
Cohere
Command R+
DeepSeek
DeepSeek VL2 Small

FAQ

Common questions about Command R+ vs DeepSeek VL2 Small.

Which is better, Command R+ or DeepSeek VL2 Small?

Command R+ (Cohere) and DeepSeek VL2 Small (DeepSeek) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.

How does Command R+ compare to DeepSeek VL2 Small in benchmarks?

Command R+ scores HellaSwag: 88.6%, Winogrande: 85.4%, MMLU: 75.7%, ARC-C: 71.0%, GSM8k: 70.7%. DeepSeek VL2 Small scores DocVQA: 92.3%, ChartQA: 84.5%, OCRBench: 83.4%, TextVQA: 83.4%, MMBench: 80.3%.

What are the context window sizes for Command R+ and DeepSeek VL2 Small?

Command R+ supports 128K tokens and DeepSeek VL2 Small supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between Command R+ and DeepSeek VL2 Small?

Key differences include multimodal support (no vs yes), licensing (CC BY-NC vs deepseek). See the full comparison above for benchmark-by-benchmark results.

Who makes Command R+ and DeepSeek VL2 Small?

Command R+ is developed by Cohere and DeepSeek VL2 Small is developed by DeepSeek.