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Cohere vs Together AI: Token Pricing, Speed & Intelligence

Full comparison of Cohere and Together AI — 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

Together AI

Open-source model hosting with competitive inference pricing

Together AI specialises in hosting open-weight models including the full Llama family, Mixtral, and DeepSeek variants. They offer live pricing via their public API and support fine-tuning workflows. A popular choice for teams that want open-source flexibility without managing their own GPU infrastructure.

Open-sourceFine-tuningCodingChatCost-efficiency
Open-weight hostHosts open weights

Key metrics

Cheapest input ($/1M)

Cheapest output ($/1M)

Peak throughput

Best latency (TTFT)

Intelligence score

Context window

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

Together AI

Largest selection of open-weight models
Fine-tuning support for custom model training
OpenAI-compatible API — easy migration
Competitive pricing on Llama 3.x models
Supports 405B parameter models
No proprietary frontier models
Throughput lower than Groq/Cerebras for speed-critical apps
Fine-tuning adds complexity vs. pure inference providers

Key differentiators

Cohere

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

Together AI

The broadest open-weight model catalog with fine-tuning support — ideal for teams that need model customisation without self-hosting.

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.

Together AI FAQs

What models does Together AI support?

Together AI hosts 100+ open-weight models including the full Llama 3.x family (8B, 70B, 405B), Mixtral, DeepSeek R1, Qwen, and many others. They also support custom fine-tuned model deployment.

How much does Together AI cost?

Llama 3.3 70B costs $0.88/1M tokens (input and output). Llama 3.1 405B is $3.50/1M tokens. Smaller models like Llama 3.2 11B Vision start at $0.18/1M tokens.

Does Together AI support fine-tuning?

Yes. Together AI offers supervised fine-tuning for Llama and other open-weight models. You can upload training data, run fine-tuning jobs, and deploy the resulting model via their inference API.

Provider resources

CohereEnterprise 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.

Together AIOpen-source model hosting with competitive inference pricing

Together AI specialises in hosting open-weight models including the full Llama family, Mixtral, and DeepSeek variants. They offer live pricing via their public API and support fine-tuning workflows. A popular choice for teams that want open-source flexibility without managing their own GPU infrastructure.

The broadest open-weight model catalog with fine-tuning support — ideal for teams that need model customisation without self-hosting.

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

Together AI

  • Largest selection of open-weight models
  • Fine-tuning support for custom model training
  • OpenAI-compatible API — easy migration

Provider category context

Cohere is a frontier lab, founded in 2019. Together AI is a inference api, founded in 2022. Cohere as a frontier lab trains and serves its own proprietary models. Together AI 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 Together AI if you need largest selection of open-weight models. 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.