Cohere vs SambaNova Cloud: Token Pricing, Speed & Intelligence
Full comparison of Cohere and SambaNova Cloud — live token pricing, latency, throughput, context window, strengths, weaknesses, and best use cases. Rates sourced from public APIs — verify before use.
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.
SambaNova Cloud
High-throughput inference on SambaNova RDU chips — gpt-oss-120b, GLM-5.3, Llama
SambaNova Cloud runs inference on SambaNova's proprietary Reconfigurable Dataflow Unit (RDU) chips, delivering very high throughput (800+ tokens/sec on gpt-oss-120b). The catalog covers gpt-oss-120b, GLM-5.3, MiniMax-M3, DeepSeek-V3.2, Meta-Llama-3.3-70B, and Gemma 4 31B. Pricing is PAYG with prompt caching on select models.
Key metric comparison
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Live token pricing
Strengths and weaknesses
Cohere
SambaNova Cloud
Key differentiators
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.
Proprietary RDU chips deliver industry-leading throughput on open-weight models at competitive per-token rates.
Frequently asked questions
Cohere FAQs
What is Command A?
Command A is Cohere's 2025 flagship model — a 111-billion-parameter model optimised for agentic and multi-step enterprise workflows, with a 256K token context window and strong tool-use performance. Check cohere.com/pricing for current rates.
What is Command R7B?
Command R7B is Cohere's efficient-tier model designed for high-volume RAG and classification workloads where cost efficiency is the priority. Check cohere.com/pricing for current rates.
What is the Cohere North platform?
North is Cohere's managed enterprise AI platform, providing a complete environment for building, deploying, and managing AI applications with private deployment, compliance controls, and integrated RAG capabilities. It is designed for enterprises with strict data residency requirements.
SambaNova Cloud FAQs
What makes SambaNova Cloud different?
SambaNova runs inference on its own Reconfigurable Dataflow Unit (RDU) chips rather than NVIDIA GPUs. This delivers very high throughput — over 800 tokens/sec on gpt-oss-120b — at competitive per-token rates.
What models does SambaNova Cloud offer?
As of October 2026: gpt-oss-120b ($0.22/$0.59), GLM-5.3 ($1.40/$4.40, cached $0.14), MiniMax-M3 ($0.60/$2.40, cached $0.06), DeepSeek-V3.2 ($3.00/$4.50), Meta-Llama-3.3-70B ($0.60/$1.20), and Gemma 4 31B ($0.38/$1.15). See cloud.sambanova.ai/plans/pricing for current rates.
Does SambaNova offer prompt caching?
Yes, on GLM-5.3 (cached input $0.14/1M vs $1.40/1M standard) and MiniMax-M3 (cached input $0.06/1M vs $0.60/1M standard).
Provider resources
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.
SambaNova Cloud — High-throughput inference on SambaNova RDU chips — gpt-oss-120b, GLM-5.3, Llama
SambaNova Cloud runs inference on SambaNova's proprietary Reconfigurable Dataflow Unit (RDU) chips, delivering very high throughput (800+ tokens/sec on gpt-oss-120b). The catalog covers gpt-oss-120b, GLM-5.3, MiniMax-M3, DeepSeek-V3.2, Meta-Llama-3.3-70B, and Gemma 4 31B. Pricing is PAYG with prompt caching on select models.
Proprietary RDU chips deliver industry-leading throughput on open-weight models at competitive per-token rates.
Key strengths compared
Cohere
- 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
- Intelligence scores trail OpenAI and Anthropic at frontier tier
- Less suitable for creative or general chat tasks
SambaNova Cloud
- Proprietary RDU chips deliver very high throughput — 800+ tokens/sec on gpt-oss-120b
- Competitive pricing on gpt-oss-120b ($0.22/$0.59 per 1M)
- Prompt caching on GLM-5.3 (cached input at $0.14/1M)
- Smaller model catalog vs OpenRouter or Together AI
- No proprietary frontier models — inference-only platform
Key weaknesses compared
Cohere
- Intelligence scores trail OpenAI and Anthropic at frontier tier
- Less suitable for creative or general chat tasks
- Smaller developer community than OpenAI/Anthropic
SambaNova Cloud
- Smaller model catalog vs OpenRouter or Together AI
- No proprietary frontier models — inference-only platform
- RDU architecture may behave differently from GPU inference for some workloads
Provider category context
Cohere is a frontier lab, founded in 2019. SambaNova Cloud is a inference api, founded in 2017. Cohere as a frontier lab trains and serves its own proprietary models. SambaNova Cloud 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 command a: 111b model optimised for agentic enterprise workflows. Choose SambaNova Cloud if you need High-throughput 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.