AI21 Labs vs Cohere: Token Pricing, Speed & Intelligence
Full comparison of AI21 Labs and Cohere — live token pricing, latency, throughput, context window, strengths, weaknesses, and best use cases. Rates sourced from public APIs — verify before use.
AI21 Labs
Jamba 1.6 — hybrid SSM-Transformer models with 256K context
AI21 Labs develops the Jamba model family, which uses a hybrid SSM (Mamba) and Transformer architecture. Jamba 1.6 Large and Mini offer 256K token context windows at competitive prices. The hybrid architecture delivers faster inference and lower memory usage than pure-Transformer models of comparable size.
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.
Key metric comparison
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Live token pricing
Strengths and weaknesses
AI21 Labs
Cohere
Key differentiators
Jamba's hybrid SSM-Transformer architecture enables 256K context at lower memory and latency cost than pure-Transformer models — with Jamba 1.6 Mini among the cheapest 256K-context options available.
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.
Frequently asked questions
AI21 Labs FAQs
What is the Jamba architecture?
Jamba uses a hybrid of Mamba (SSM) and Transformer layers. This reduces memory usage and improves throughput on long sequences compared to pure-Transformer models, while maintaining strong reasoning performance.
How much does AI21 Labs API cost?
Jamba 1.6 Large is $2.00/$8.00 per 1M input/output tokens. Jamba 1.6 Mini is $0.20/$0.40 per 1M tokens. Both support 256K context. See ai21.com/pricing for current rates.
What context window does Jamba 1.6 support?
Both Jamba 1.6 Large and Jamba 1.6 Mini support 256K token context windows, making them suitable for long-document analysis and extended conversations.
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.
Provider resources
AI21 Labs — Jamba 1.6 — hybrid SSM-Transformer models with 256K context
AI21 Labs develops the Jamba model family, which uses a hybrid SSM (Mamba) and Transformer architecture. Jamba 1.6 Large and Mini offer 256K token context windows at competitive prices. The hybrid architecture delivers faster inference and lower memory usage than pure-Transformer models of comparable size.
Jamba's hybrid SSM-Transformer architecture enables 256K context at lower memory and latency cost than pure-Transformer models — with Jamba 1.6 Mini among the cheapest 256K-context options available.
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.
Key strengths compared
AI21 Labs
- Hybrid SSM-Transformer architecture delivers faster inference than pure-Transformer models
- 256K token context window on both Jamba 1.6 Large and Mini
- Jamba 1.6 Mini at $0.20/$0.40 per 1M tokens is highly cost-competitive
- Smaller model family than frontier labs — two models vs dozens at OpenAI or Anthropic
- Less ecosystem support than OpenAI-compatible providers
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
Key weaknesses compared
AI21 Labs
- Smaller model family than frontier labs — two models vs dozens at OpenAI or Anthropic
- Less ecosystem support than OpenAI-compatible providers
- No image, audio, or multimodal models
Cohere
- Intelligence scores trail OpenAI and Anthropic at frontier tier
- Less suitable for creative or general chat tasks
- Smaller developer community than OpenAI/Anthropic
Provider category context
AI21 Labs is a frontier lab, founded in 2017. Cohere is a frontier lab, founded in 2019. 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 AI21 Labs and Cohere are frontier labs with proprietary models. Choose based on benchmark performance for your specific task: AI21 Labs leads on hybrid ssm-transformer architecture delivers faster inference than pure-transformer models, while Cohere leads on command a: 111b model optimised for agentic enterprise workflows. 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.