Cohere vs DeepSeek: Token Pricing, Speed & Intelligence

Full comparison of Cohere and DeepSeek — live token pricing, latency, throughput, context window, strengths, weaknesses, and best use cases. Updated July 2026.

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.

RAGEnterprise searchEmbeddingsTool useMultilingual

Proprietary models

DeepSeek

Chinese frontier lab — DeepSeek V3 and R1 at remarkably low prices

DeepSeek is a Chinese AI lab that has released highly capable open-weight models at prices far below Western competitors. DeepSeek V3 matches GPT-4 class performance at $0.27/1M input tokens, while DeepSeek R1 is a reasoning model competitive with o1 at a fraction of the cost. Both models are open-weight.

Cost-efficiencyReasoningCodingOpen-sourceSelf-hosting

Proprietary models

Key metrics

Cheapest input ($/1M)

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Cheapest output ($/1M)

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Peak throughput

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Best latency (TTFT)

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Intelligence score

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Context window

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Live token pricing

Strengths & weaknesses

Cohere

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

DeepSeek

DeepSeek V3 matches GPT-4 class at $0.27/1M input — 10× cheaper

R1 reasoning model competitive with o1 at a fraction of the cost

Both V3 and R1 are open-weight — can be self-hosted

Mixture-of-Experts architecture for efficient inference

Strong coding and math benchmarks

Data residency in China — may not meet compliance requirements

API reliability can lag Western providers during peak demand

Limited multimodal capability vs. Gemini or GPT-4o

Key differentiators

Cohere

The only major LLM provider with a complete RAG stack — LLM, embeddings, and reranking — all from one API.

DeepSeek

DeepSeek V3 delivers GPT-4 class intelligence at $0.27/1M input tokens — the most disruptive price-to-performance ratio in the LLM market.

Frequently asked questions

Cohere FAQs

What is Cohere best used for?

Cohere excels at retrieval-augmented generation (RAG), enterprise search, and document processing. Command R+ is optimised for grounded generation with citations, making it ideal for knowledge bases, customer support, and research tools.

Does Cohere offer embedding models?

Yes. Cohere's Embed models are among the best available for semantic search and RAG pipelines. Combined with their Rerank model, you can build a complete search stack using only Cohere's API.

How much does Cohere cost?

Command R+ pricing varies by use case. Cohere offers a free trial tier and enterprise pricing. Check their pricing page for current rates as they vary by model and volume.

DeepSeek FAQs

How much does DeepSeek cost?

DeepSeek V3 costs $0.27/1M input and $1.10/1M output tokens — roughly 10× cheaper than GPT-4o for comparable capability. DeepSeek R1 is $0.55/1M input and $2.19/1M output.

Is DeepSeek open-weight?

Yes. Both DeepSeek V3 and DeepSeek R1 are open-weight models available on Hugging Face. You can self-host them on your own GPU infrastructure, though they require significant compute (671B parameters for R1).

How does DeepSeek R1 compare to OpenAI o1?

DeepSeek R1 scores comparably to OpenAI o1 on math and coding benchmarks at a fraction of the cost. R1 is open-weight and can be self-hosted, while o1 is proprietary. R1 is available via multiple inference providers including Fireworks AI and Together AI.

Provider resources

Cohere

Provider page Website Pricing Docs

DeepSeek

Provider page Website Pricing Docs

Cohere — Enterprise NLP — Command R+ with retrieval-augmented generation

The only major LLM provider with a complete RAG stack — LLM, embeddings, and reranking — all from one API.

DeepSeek — Chinese frontier lab — DeepSeek V3 and R1 at remarkably low prices

DeepSeek V3 delivers GPT-4 class intelligence at $0.27/1M input tokens — the most disruptive price-to-performance ratio in the LLM market.

Key strengths compared

Cohere

▸Best-in-class RAG with native grounding and citations
▸Embedding and reranking models for full search pipeline
▸Enterprise SLAs and on-premise deployment options

DeepSeek

▸DeepSeek V3 matches GPT-4 class at $0.27/1M input — 10× cheaper
▸R1 reasoning model competitive with o1 at a fraction of the cost
▸Both V3 and R1 are open-weight — can be self-hosted

Provider category context

Cohere is a frontier lab, founded in 2019. DeepSeek is a frontier lab, founded in 2023. Both are frontier lab providers — the comparison is primarily about pricing, model selection, and feature differentiation within the same tier.

How to choose between them

Both Cohere and DeepSeek are frontier labs with proprietary models. Choose based on benchmark performance for your specific task: Cohere leads on best-in-class rag with native grounding and citations, while DeepSeek leads on deepseek v3 matches gpt-4 class at $0.27/1m input — 10× cheaper. For cost-sensitive workloads, compare the cheapest model tier from each provider in the pricing table above — the gap between efficient-tier models is often larger than between flagship models.