Aya 23 35BvsCohere Embed v4
Cohere vs Cohere — Side-by-side model comparison
Head-to-Head Comparison
| Metric | Aya 23 35B | Cohere Embed v4 |
|---|---|---|
| Provider | ||
| Arena Rank | — | — |
| Context Window | 8K | 128K |
| Input Pricing | Free (open)/1M tokens | $0.12/1M tokens |
| Output Pricing | Free (open)/1M tokens | $0.12/1M tokens |
| Parameters | 35B | Undisclosed |
| Open Source | Yes | No |
| Best For | Multilingual tasks, low-resource languages | Semantic search, RAG embeddings, document retrieval |
| Release Date | May 23, 2024 | Mar 1, 2025 |
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 →Cohere Embed v4
Cohere Embed v4 is Cohere's latest embedding model supporting multimodal inputs for the first time — processing both text and images into unified vector representations. It generates high-quality embeddings for semantic search, RAG pipelines, and clustering applications. The model supports 100+ languages and produces compact, efficient embeddings that work well with vector databases. It represents a significant upgrade over text-only embedding models for building modern search and retrieval systems.
View Cohere profile →Key Differences: Aya 23 35B vs Cohere Embed v4
Cohere Embed v4 supports a larger context window (128K), 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 v4 is proprietary (API-only access).
When to use Aya 23 35B
- +You need to self-host or fine-tune the model
- +Your use case involves multilingual tasks, low-resource languages
When to use Cohere Embed v4
- +You need to process long documents (128K context)
- +You prefer a managed API without infrastructure overhead
- +Your use case involves semantic search, rag embeddings, document retrieval
The Verdict
Cohere Embed v4 wins our head-to-head comparison with 3 out of 5 category wins. It's the stronger choice for semantic search, rag embeddings, document retrieval, 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