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CoherevsGroq

Cohere vs Groq: Token Pricing, Speed & Intelligence

Full comparison of Cohere and Groq — 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

Groq

LPU-powered inference — the fastest tokens per second available

Groq runs custom Language Processing Units (LPUs) that deliver dramatically higher throughput than GPU-based inference — Llama 3.3 70B reaches 750+ tokens/second on Groq, versus 100–200 on typical GPU providers. Ideal for latency-sensitive applications, real-time chat, and high-volume batch workloads.

SpeedReal-time chatVoice AICost-efficiencyBatch processing
Open-weight hostHosts open weights

Key metrics

Cohere
Groq
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

Groq

750+ tokens/sec on Llama 3.3 70B — fastest GPU-class inference
Sub-100ms time-to-first-token for real-time applications
Very competitive pricing on open-weight models
OpenAI-compatible API
Free tier available
Limited model selection vs. Together AI or Fireworks
No vision model support on most models
No fine-tuning capability

Key differentiators

Cohere

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

Groq

Groq's custom LPU chips deliver 750+ tokens/sec on Llama 3.3 70B — 4–5× faster than any GPU-based provider.

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.

Groq FAQs

How fast is Groq inference?

Groq delivers 750+ tokens/second on Llama 3.3 70B and 1,200+ tokens/second on Llama 3.1 8B. This is 4–5× faster than typical GPU-based providers, making it ideal for real-time applications.

How much does Groq cost?

Llama 3.3 70B costs $0.59/1M input and $0.79/1M output tokens. Llama 3.1 8B is just $0.05/$0.08 per 1M tokens — among the cheapest options for a capable open-weight model.

What is a Groq LPU?

A Language Processing Unit (LPU) is Groq's custom silicon designed specifically for sequential token generation. Unlike GPUs which are optimised for parallel matrix operations, LPUs excel at the autoregressive decoding step that dominates LLM inference latency.

Provider resources

Cohere

Provider pageWebsitePricingDocs

Groq

Provider pageWebsitePricingDocs

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.

Groq — LPU-powered inference — the fastest tokens per second available

Groq runs custom Language Processing Units (LPUs) that deliver dramatically higher throughput than GPU-based inference — Llama 3.3 70B reaches 750+ tokens/second on Groq, versus 100–200 on typical GPU providers. Ideal for latency-sensitive applications, real-time chat, and high-volume batch workloads.

Groq's custom LPU chips deliver 750+ tokens/sec on Llama 3.3 70B — 4–5× faster than any GPU-based provider.

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

Groq

  • ▸750+ tokens/sec on Llama 3.3 70B — fastest GPU-class inference
  • ▸Sub-100ms time-to-first-token for real-time applications
  • ▸Very competitive pricing on open-weight models

Provider category context

Cohere is a frontier lab, founded in 2019. Groq is a inference api, founded in 2016. Cohere as a frontier lab trains and serves its own proprietary models. Groq as an inference API provider hosts open-weight models — typically offering lower prices for equivalent capability tiers but without access to proprietary frontier models.

How to choose between them

Choose Cohere if you need best-in-class rag with native grounding and citations. Choose Groq if you need 750+ tokens/sec on llama 3.3 70b — fastest gpu-class inference. For high-volume production workloads, run a cost comparison using the token pricing table above with your actual prompt/completion token ratio — the cheapest provider depends heavily on your input-to-output token ratio.