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Aya 23 35BvsCommand R

Cohere vs Cohere — Side-by-side model comparison

Command R leads 4/5 categories

Head-to-Head Comparison

MetricAya 23 35BCommand R
Provider
Arena Rank
#23
Context Window
8K
128K
Input Pricing
Free (open)/1M tokens
$0.15/1M tokens
Output Pricing
Free (open)/1M tokens
$0.60/1M tokens
Parameters
35B
35B
Open Source
Yes
Yes
Best For
Multilingual tasks, low-resource languages
Cost-effective RAG, summarization, chat
Release Date
May 23, 2024
Mar 11, 2024

Aya 23 35B

Aya 23 35B is Cohere's open-source multilingual model supporting 23 languages, with particular strength in underserved and low-resource languages. Developed through a massive community research effort involving thousands of contributors worldwide, Aya represents a democratizing force in AI, ensuring language model capabilities extend beyond English and a handful of high-resource languages.

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Command R

Command R is Cohere's efficient model optimized for RAG workloads at scale. At 35 billion parameters with a 128K context window, it delivers strong retrieval-augmented generation performance at a significantly lower cost than Command R+. It supports 10 languages and excels at summarization, document Q&A, and conversational tasks, making it ideal for high-volume enterprise applications where cost efficiency is critical.

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Key Differences: Aya 23 35B vs Command R

1

Command R supports a larger context window (128K), allowing it to process longer documents in a single request.

2

Aya 23 35B has 35B parameters vs Command R's 35B, which affects inference speed and capability.

A

When to use Aya 23 35B

  • +Your use case involves multilingual tasks, low-resource languages
View full Aya 23 35B specs →
C

When to use Command R

  • +You need to process long documents (128K context)
  • +Your use case involves cost-effective rag, summarization, chat
View full Command R specs →

The Verdict

Command R wins our head-to-head comparison with 4 out of 5 category wins. It's the stronger choice for cost-effective rag, summarization, chat, though Aya 23 35B holds an edge in multilingual tasks, low-resource languages.

Last compared: March 2026 · Data sourced from public benchmarks and official pricing pages

Frequently Asked Questions

Which is better, Aya 23 35B or Command R?
In our head-to-head comparison, Command R leads in 4 out of 5 categories (arena rank, context window, input pricing, output pricing, and parameters). Command R excels at cost-effective rag, summarization, chat, while Aya 23 35B is better suited for multilingual tasks, low-resource languages. The best choice depends on your specific requirements, budget, and use case.
How does Aya 23 35B pricing compare to Command R?
Aya 23 35B charges Free (open) per 1M input tokens and Free (open) per 1M output tokens. Command R charges $0.15 per 1M input tokens and $0.60 per 1M output tokens. For high-volume production workloads, the pricing difference can significantly impact total cost of ownership.
What is the context window difference between Aya 23 35B and Command R?
Aya 23 35B supports a 8K token context window, while Command R supports 128K tokens. Command R can process longer documents, codebases, and conversations in a single request. Context window size matters most for tasks involving long documents, large codebases, or extended conversations.
Can I use Aya 23 35B or Command R for free?
Aya 23 35B is a paid API model starting at Free (open) per 1M input tokens. Command R is a paid API model starting at $0.15 per 1M input tokens. Open-source models can be self-hosted for free but require your own GPU infrastructure.
Which model has better benchmarks, Aya 23 35B or Command R?
Aya 23 35B's arena rank is not yet available, while Command R holds rank #23. Note that benchmarks don't capture every use case — we recommend testing both models on your specific tasks.
Is Aya 23 35B or Command R better for coding?
Aya 23 35B's primary strength is multilingual tasks, low-resource languages. Command R's primary strength is cost-effective rag, summarization, chat. For coding specifically, arena rank and code-specific benchmarks are the best indicators of performance.