Aya 23 35BvsCommand R
Cohere vs Cohere — Side-by-side model comparison
Head-to-Head Comparison
| Metric | Aya 23 35B | Command 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, developed by Cohere through the Cohere For AI research initiative, is an open-source multilingual model with 35 billion parameters and an 8K token context window supporting 23 languages. The model was developed with contributions from researchers worldwide, focusing on extending quality AI capabilities to lower-resource languages that mainstream models underserve. Aya 23 35B performs well on multilingual benchmarks, particularly for languages in Africa, South Asia, and Southeast Asia where few commercial alternatives exist. Free and open-source, it can be fine-tuned and deployed for language-specific applications without cost. The model represents Cohere's commitment to democratizing AI access globally, providing a foundation for researchers and developers working in languages outside the English-Chinese-European focus of most commercial models.
View Cohere profile →Command R
Command R, developed by Cohere, is an enterprise-grade model with 35 billion parameters and a 128K token context window optimized for retrieval-augmented generation. The model specializes in grounded generation, producing responses with accurate citations from provided source documents. It handles document search, summarization, information extraction, and conversational Q&A with built-in citation capabilities. Command R supports 10 languages and features production-ready RAG tooling that reduces hallucination on retrieval tasks. Priced at $0.15 per million input tokens and $0.60 per million output tokens, it offers cost-effective enterprise search capabilities. As an open-source model, it can also be self-hosted. Command R ranks #23 on the Chatbot Arena leaderboard, reflecting its specialized design for retrieval tasks rather than general-purpose benchmarks where broad models score higher.
View Cohere profile →Key Differences: Aya 23 35B vs Command R
Command R supports a larger context window (128K), allowing it to process longer documents in a single request.
Aya 23 35B has 35B parameters vs Command R's 35B, which affects inference speed and capability.
When to use Aya 23 35B
- +Your use case involves multilingual tasks, low-resource languages
When to use Command R
- +You need to process long documents (128K context)
- +Your use case involves cost-effective rag, summarization, chat
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: April 2026 · Data sourced from public benchmarks and official pricing pages