Cohere
Command A and Command R7B — enterprise RAG, North platform, and on-premise deployment
Cohere focuses on enterprise NLP, particularly retrieval-augmented generation (RAG) and search. Command A is their 2025 flagship — a 111B-parameter model optimised for agentic and multi-step enterprise workflows. Command R7B is the efficient tier. The North platform provides a managed enterprise AI environment with private deployment, compliance controls, and a complete RAG stack (LLM + Embed 4 + Rerank 3). Check cohere.com/pricing for current rates.
The only major LLM provider with a complete enterprise RAG stack — Command A, Embed 4, and Rerank 3 — plus the North platform for private deployment and compliance-grade enterprise AI.
- Command A: 111B model optimised for agentic enterprise workflows
- Command R7B: efficient tier — cost-effective for high-volume RAG and classification
- Embed 4 + Rerank 3: strong RAG stack with native grounding and citations
- North platform: managed enterprise AI with private deployment and compliance controls
- On-premise and private cloud deployment options
- Strong multilingual support across 23+ languages
- Intelligence scores trail OpenAI and Anthropic at frontier tier
- Less suitable for creative or general chat tasks
- Smaller developer community than OpenAI/Anthropic
Cohere Models
| Model | Modality | Tier | Price | Cached /1M | TTFT | Throughput | Context | Intel. | Caps | |
|---|---|---|---|---|---|---|---|---|---|---|
command-a-vision-07-2025 Command A | Chat | advanced | — No current price available | — | 700ms | 90 t/s | 128K | 78 | ||
command-a-translate-08-2025 Command A | Chat | efficient | — No current price available | — | 300ms | 200 t/s | 8K | 70 | ||
command-a-reasoning-08-2025 Command A | Chat | advanced | — No current price available | — | 3.0s | 60 t/s | 256K | 80 | ||
rerank-v3.5 Cohere Rerank | Reranker | efficient | $2.00 per 1K searches EST Jul 2026 | — | 150ms | 0 t/s | 4K | 0 | ||
command-r7b-12-2024 Command R | Chat | budget | $0.04/ $0.15 in / out per 1M EST Jul 2026 | — | 200ms | 400 t/s | 128K | 42 | ||
embed-v4.0 Cohere Embed | Embedding | efficient | $0.10/ $0.00 in / out per 1M EST Jul 2026 | — | 90ms | 0 t/s | 128K | 0 | ||
command-r-08-2024 Command R | Chat | efficient | $0.15/ $0.60 in / out per 1M EST Jul 2026 | — | 380ms | 180 t/s | 128K | 55 | ||
command-r-plus-08-2024 Command R+ | Chat | advanced | $2.50/ $10.00 in / out per 1M EST Jul 2026 | — | 700ms | 100 t/s | 128K | 66 | ||
command-a-03-2025 Command A | Chat | advanced | $2.50/ $10.00 in / out per 1M EST Jul 2026 | — | 600ms | 110 t/s | 256K | 72 |
Cohere — Frequently Asked Questions (FAQs)
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Cohere is a proprietary AI lab that develops and hosts its own large language models. Cohere focuses on enterprise NLP, particularly retrieval-augmented generation (RAG) and search. Command A is their 2025 flagship — a 111B-parameter model optimised for agentic and multi-step enterprise workflows. Command R7B is the efficient tier. The North platform provides a managed enterprise AI environment with private deployment, compliance controls, and a complete RAG stack (LLM + Embed 4 + Rerank 3). Check cohere.com/pricing for current rates. Common use cases include RAG, Enterprise search, Embeddings, Agentic workflows. 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 enterprise RAG stack — Command A, Embed 4, and Rerank 3 — plus the North platform for private deployment and compliance-grade enterprise AI. 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 stands out because: The only major LLM provider with a complete enterprise RAG stack — Command A, Embed 4, and Rerank 3 — plus the North platform for private deployment and compliance-grade enterprise AI.. The API currently offers 9 models, spanning 4 advanced, 4 efficient, 1 budget tiers. 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. The cheapest {meta.name} input rate is currently $0.04/1M tokens. Prices shown are sourced from Cohere's public pricing page and updated every 15 minutes. 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. Cohere's largest context window is 256K tokens. 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.
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