Model Comparison

Command R+ vs DeepSeek VL2Which is better in 2026?

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

Verdict: Command R+ vs DeepSeek VL2 — which is better?

Command R+ (by Cohere) and DeepSeek VL2 (by DeepSeek) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.

DeepSeek VL2 also accepts a larger context window (129,280 input tokens), making it the stronger choice for long documents and large codebases.

Choose Command R+ if…

  • you want predictable pricing at $0.25/M input and $1.00/M output

Choose DeepSeek VL2 if…

  • you process long inputs — it offers a 129,280 token context window
  • you want the most recent training data — it shipped Dec 2024

Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

Command R+ and DeepSeek VL2don'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

77.0B diff

Command R+ has 77.0B more parameters than DeepSeek VL2, making it 285.2% larger.

Cohere
Command R+
104.0Bparameters
DeepSeek
DeepSeek VL2
27.0Bparameters
104.0B
Command R+
27.0B
DeepSeek VL2

Context Window

Maximum input and output token capacity

DeepSeek VL2 accepts 129,280 input tokens compared to Command R+'s 128,000 tokens. DeepSeek VL2 can generate longer responses up to 129,280 tokens, while Command R+ is limited to 128,000 tokens.

Cohere
Command R+
Input128,000 tokens
Output128,000 tokens
DeepSeek
DeepSeek VL2
Input129,280 tokens
Output129,280 tokens
Thu Jul 09 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

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

DeepSeek VL2 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

Text
Images
Audio
Video

License

Usage and distribution terms

Command R+ is licensed under CC BY-NC, while DeepSeek VL2 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

deepseek

Open weights

Release Timeline

When each model was launched

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

DeepSeek VL2 is 4 months newer than Command R+.

Command R+

Aug 30, 2024

1.9 years ago

DeepSeek VL2

Dec 13, 2024

1.6 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

Provider Availability

Command R+ is available from Cohere, Bedrock. DeepSeek VL2 is available from Replicate.

Command R+

cohere logo
Cohere
Input Price:Input: $0.25/1MOutput Price:Output: $1.00/1M
bedrock logo
AWS Bedrock
Input Price:Input: $3.00/1MOutput Price:Output: $15.00/1M

DeepSeek VL2

replicate logo
Replicate
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

Cohere

No standout differentiators in the data we have for this pair.

DeepSeek
Larger context window (129,280 tokens)
Supports multimodal inputs

Detailed Comparison

Interactive Arena

Judge for yourself.

Run your own prompts against Command R+ and DeepSeek VL2 side-by-side, then vote on the output you prefer.

Command R+
✓ Preferred
DeepSeek VL2
Open in Playground
AI Model Comparison Table
Feature
Cohere
Command R+
DeepSeek
DeepSeek VL2

FAQ

Common questions about Command R+ vs DeepSeek VL2.

Which is better, Command R+ or DeepSeek VL2?

Command R+ (Cohere) and DeepSeek VL2 (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 in benchmarks?

Command R+ scores HellaSwag: 88.6%, Winogrande: 85.4%, MMLU: 75.7%, ARC-C: 71.0%, GSM8k: 70.7%. DeepSeek VL2 scores DocVQA: 93.3%, ChartQA: 86.0%, TextVQA: 84.2%, AI2D: 81.4%, OCRBench: 81.1%.

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

Command R+ supports 128K tokens and DeepSeek VL2 supports 129K 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?

Key differences include context window (128K vs 129K), 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?

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

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