Cohere Command R (08-2024)
Cohere's mid-tier RAG/tool model. Cost-efficient sibling of Command R+ with 128k context.
Cohere Command R (08-2024) is text & chat AI model from Cohere, priced at β¬0.150 per 1M input tokens with a 128K tokens context window.
0.7
Pricing
API Integration
Use our OpenAI-compatible API to integrate Cohere Command R (08-2024) into your application.
npm install railwailimport railwail from "railwail";
const rw = railwail("YOUR_API_KEY");
// Simple β just pass a string
const reply = await rw.run("command-r-08-2024", "Hello! What can you do?");
console.log(reply);
// With message history
const reply2 = await rw.run("command-r-08-2024", [
{ role: "system", content: "You are a helpful assistant." },
{ role: "user", content: "Explain quantum computing simply." },
]);
console.log(reply2);
// Full response with usage info
const res = await rw.chat("command-r-08-2024", [
{ role: "user", content: "Hello!" },
], { temperature: 0.7, max_tokens: 500 });
console.log(res.choices[0].message.content);
console.log(res.usage);Deep dive β Cohere's Cohere Command R (08-2024)
Cohere is an enterprise-focused LLM lab founded in 2019 by Aidan Gomez (co-author of 'Attention Is All You Need'), Ivan Zhang and Nick Frosst. Headquartered in Toronto with offices in San Francisco and London, Cohere targets enterprise customers β banks, telcos, government β with deployment options spanning Cohere's hosted API, AWS Bedrock, Azure, Oracle Cloud and on-prem (Cohere North). The Command R family launched March 2024 as Cohere's RAG-and-tool-use flagship line, refreshed as command-r-08-2024 in August 2024 with substantial gains on math, code, reasoning and reduced spurious refusals. Investors include Nvidia, Cisco, Salesforce and Oracle, with total funding above $1B and a 2024 valuation around $5.5B.
Visit Cohere βCommand R 08-2024 is a 35B dense decoder-only transformer designed specifically for retrieval-augmented generation and multi-step tool use. The architecture uses grouped-query attention (8 KV heads), RoPE positional embeddings (extended for 128K context), SwiGLU activations, RMSNorm and no biases, with a 256,000-token multilingual BPE tokeniser shared with the Aya line. The 08-2024 refresh kept the architecture and tokeniser identical to the March 2024 release but substantially improved math, code, reasoning, structured output and reduced 'I cannot answer' refusals. Post-training emphasises grounded citation generation (Cohere's structured `<co: 0>` citation tokens) and parallel multi-step tool calling. Open weights are released under CC-BY-NC 4.0 for research; commercial use goes through the hosted Cohere API, AWS Bedrock, Azure, Oracle marketplaces or licensed on-prem deployment.
- Parameters
- 35B (dense)
- Context
- 128K tokens
- Excellent retrieval-augmented generation with structured inline citations
- Native multi-step tool/function calling with parallel-tool support
- 128K context window for long-document analysis
- Strong multilingual quality across 10+ business languages
- Improved math, code and reasoning in the 08-2024 refresh
- Open weights (CC-BY-NC) for research evaluation
- Hosted on Cohere API, AWS Bedrock, Azure, Oracle Cloud
- Best for: enterprise RAG, multilingual support agents, mid-tier tool-using assistants.
Trained on trillions of tokens of multilingual web data, code, math and Cohere-curated synthetic RAG and tool-use traces. Knowledge cutoff approximately early 2024. Post-training is supervised fine-tuning followed by preference optimisation, with specific data and methods proprietary to Cohere.
License: Cohere Terms of Service for the hosted API. Open weights on Hugging Face under CC-BY-NC 4.0 (research-only). Commercial production access via Cohere API, AWS Bedrock, Azure, Oracle Cloud or licensed on-prem deployments.
Known limitations
- Open weights are non-commercial β production needs hosted/licensed access
- Smaller and less capable than Command R+ on hard reasoning
- No vision modality
- Cohere tool-use schema differs from OpenAI β adapter work required
- Latency higher than smaller tiers for simple short-text tasks
Frequently asked questions
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