Cohere
Enterprise NLP — Command R+ with retrieval-augmented generation
Cohere focuses on enterprise NLP use cases, particularly retrieval-augmented generation (RAG) and search. Command R+ is their flagship model, optimised for tool use and multi-step reasoning in enterprise workflows. Cohere also offers embedding and reranking models that pair well with their LLMs.
The only major LLM provider with a complete RAG stack — LLM, embeddings, and reranking — all from one API.
- Best-in-class RAG with native grounding and citations
- Embedding and reranking models for full search pipeline
- Enterprise SLAs and on-premise deployment options
- Command R+ optimised for multi-step tool use
- Strong multilingual support
- Intelligence scores below frontier leaders
- Less suitable for creative or general chat tasks
- Smaller developer community than OpenAI/Anthropic
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Full comparison tableCohere LLM pricing overview
Cohere is a proprietary AI lab that develops and hosts its own large language models. Cohere focuses on enterprise NLP use cases, particularly retrieval-augmented generation (RAG) and search. Command R+ is their flagship model, optimised for tool use and multi-step reasoning in enterprise workflows. Cohere also offers embedding and reranking models that pair well with their LLMs. Common use cases include RAG, Enterprise search, Embeddings, Tool use. Headquartered in Toronto, Canada, founded 2019. All models are accessible via a REST API compatible with standard OpenAI-style request formats, enabling drop-in integration with most LLM frameworks and orchestration tools.
Cohere vs other LLM providers
Cohere competes with OpenAI, Anthropic, Google, Groq, Together AI, Mistral, Cohere, and other inference API providers across dimensions of price, throughput, latency, context window, and model intelligence. The only major LLM provider with a complete RAG stack — LLM, embeddings, and reranking — all from one API. Use the LLM provider comparison tool to see Cohere token pricing, latency, and throughput side-by-side with any other provider. The full LLM pricing table shows all providers ranked by input token cost, output token cost, and throughput in a single sortable view.
Why choose Cohere?
Cohere's key strengths are: Best-in-class RAG with native grounding and citations; Embedding and reranking models for full search pipeline; Enterprise SLAs and on-premise deployment options. Limitations to consider: Intelligence scores below frontier leaders; Less suitable for creative or general chat tasks. For teams running high-volume inference workloads, prompt caching and batch API endpoints can reduce effective input token costs by 50–90% — check the context window cost guide for a full breakdown of caching economics.
Understanding Cohere token pricing
Cohere charges separately for input (prompt) and output (completion) tokens, priced per 1M tokens in USD. Output tokens are typically 3–5× more expensive than input tokens due to the compute cost of autoregressive generation. Prices shown are sourced from Cohere's public pricing page and updated daily. Need help estimating your spend? Read the LLM API cost calculator guide — it covers tokens, context windows, prompt caching, and batch discounts with worked examples for RAG, chat history, and document processing workloads.
Cohere context window and model capabilities
Context window size directly affects both capability and cost — every token in the context window is charged as an input token. For RAG and document processing workloads, longer context windows enable richer retrieval but increase per-call costs proportionally. Prompt caching — where supported — stores the KV state of repeated prefixes and charges 75–90% less for cache hits, making it the most impactful cost optimization for applications with consistent system prompts or retrieved documents. See the LLM context window cost guide for a full analysis of how context length affects your API bill.
Self-hosted vs managed inference: when Cohere makes sense
Managed inference APIs like Cohere eliminate infrastructure overhead — no GPU provisioning, driver management, or model serving stack to maintain. The trade-off is cost at scale: a single H100 at ~$2.50/hr can serve ~500K tokens/min of Llama 3.3 70B, which at Cohere API rates would cost significantly more per token. The break-even point depends on your request volume, latency requirements, and engineering capacity. For teams processing fewer than ~10M tokens/day, managed APIs are almost always cheaper when total cost of ownership is considered. Above that threshold, self-hosted inference on rented GPU compute typically wins on unit economics. Read the cheapest GPU cloud guide for a full break-even analysis. Historical Cohere token price data is available in the LLM price history charts.