Cohere Embed v3vsAya 23 35B
Cohere vs Cohere — Side-by-side model comparison
Head-to-Head Comparison
| Metric | Cohere Embed v3 | Aya 23 35B |
|---|---|---|
| Provider | ||
| Arena Rank | — | — |
| Context Window | 512 tokens | 8K |
| Input Pricing | $0.10/1M tokens/1M tokens | Free (open)/1M tokens |
| Output Pricing | N/A (embeddings)/1M tokens | Free (open)/1M tokens |
| Parameters | Undisclosed | 35B |
| Open Source | No | Yes |
| Best For | Search, RAG, semantic similarity, clustering | Multilingual tasks, low-resource languages |
| Release Date | Nov 2, 2023 | May 23, 2024 |
Cohere Embed v3
Cohere Embed v3 is one of the leading text embedding models, supporting over 100 languages with state-of-the-art retrieval performance. It produces dense vector representations of text that power semantic search, RAG pipelines, and classification systems. The model offers specialized embedding types for search queries vs documents, optimizing retrieval accuracy for enterprise applications.
View Cohere profile →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.
View Cohere profile →Key Differences: Cohere Embed v3 vs Aya 23 35B
Aya 23 35B supports a larger context window (8K), allowing it to process longer documents in a single request.
Aya 23 35B is open-source (free to self-host and fine-tune) while Cohere Embed v3 is proprietary (API-only access).
When to use Cohere Embed v3
- +You prefer a managed API without infrastructure overhead
- +Your use case involves search, rag, semantic similarity, clustering
When to use Aya 23 35B
- +You need to process long documents (8K context)
- +You need to self-host or fine-tune the model
- +Your use case involves multilingual tasks, low-resource languages
The Verdict
Aya 23 35B wins our head-to-head comparison with 2 out of 5 category wins. It's the stronger choice for multilingual tasks, low-resource languages, though Cohere Embed v3 holds an edge in search, rag, semantic similarity, clustering.
Last compared: March 2026 · Data sourced from public benchmarks and official pricing pages