Cohere Command R+ (08-2024)
Cohere's flagship RAG- and tool-optimized chat model. 128k context, refreshed August 2024.
Cohere Command R+ (08-2024) is text & chat AI model from Cohere, priced at €2.50 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 — just pass a string
const reply = await rw.run("command-r-plus-08-2024", "Hello! What can you do?");
console.log(reply);
// With message history
const reply2 = await rw.run("command-r-plus-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-plus-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 — 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. Cohere targets enterprise customers with deployment options spanning Cohere's hosted API, AWS Bedrock, Azure, Oracle Cloud and on-prem (Cohere North). Command R+ is Cohere's flagship enterprise LLM, scaling the Command R recipe up to 104B parameters and positioned against GPT-4, Claude 3 Opus and Mistral Large. The 08-2024 refresh shipped August 2024 with major gains on math, code, reasoning, citation accuracy and reduced refusals. Investors include Nvidia, Cisco, Salesforce and Oracle, with total funding above $1B and a 2024 valuation around $5.5B.
Visit Cohere →Command R+ 08-2024 is a 104B parameter dense decoder-only transformer — the larger sibling of Command R, scaled up with the same architectural recipe. It uses grouped-query attention (8 KV heads), RoPE positional embeddings (extended for 128K context), SwiGLU activations, RMSNorm and no biases, with the same 256,000-token multilingual BPE tokeniser as Command R and the Aya family. The 08-2024 refresh kept the architecture identical to the April 2024 release but delivered substantial improvements in math, code, reasoning, instruction following and structured outputs while reducing spurious refusals at unchanged pricing. Post-training emphasises citation-grounded RAG (structured `<co: 0>` citation tokens), parallel multi-step tool calling with chain-of-thought planning, and multilingual instruction following across 10+ business languages. Open weights released on Hugging Face under CC-BY-NC 4.0 for research; commercial production via Cohere API or marketplaces.
- Parameters
- 104B (dense)
- Context
- 128K tokens
- 104B dense parameters — Cohere's frontier enterprise model
- Top-tier RAG quality with structured inline citations
- Highly capable multi-step tool use with parallel calls and error recovery
- 128K context window
- Strong multilingual: 10+ business languages including Arabic, Japanese, Korean
- Open weights (CC-BY-NC) at 104B — largest open RAG-tuned model at release
- Hosted on Cohere API, AWS Bedrock, Azure, Oracle Cloud
- Best for: production enterprise RAG, complex tool-using agents, multilingual content ops, regulated-industry deployments.
Trained on trillions of tokens of multilingual web data, code, math, books, and Cohere-curated synthetic RAG and tool-use traces. Knowledge cutoff approximately early 2024. Post-training combines supervised fine-tuning with preference optimisation; specific methods and data are proprietary.
License: Cohere Terms of Service for the hosted API. Open weights on Hugging Face under CC-BY-NC 4.0 (research only). Commercial production via Cohere API, AWS Bedrock, Azure, Oracle Cloud, or licensed on-prem deployment under Cohere North.
Known limitations
- No vision modality
- Pricier and slower than GPT-4o-mini class models for simple tasks
- Open weights non-commercial — production requires hosted/licensed access
- Behind GPT-4o / Claude 3.5 Sonnet on the hardest reasoning and coding benchmarks
- Smaller community and tooling ecosystem than OpenAI/Anthropic
Frequently asked questions
Related Models
View all Text & ChatBio_ClinicalBERT
The original Bio_ClinicalBERT from Alsentzer et al., a BERT model initialized from BioBERT and further pretrained on all MIMIC-III clinical notes. Served as a fill-mask endpoint it predicts masked tokens in clinical text and produces clinical embeddings. It is the standard encoder backbone behind many downstream clinical NLP fine-tunes.
Biomedical NER (all entities)
Token-classification model from d4data that tags 84 biomedical entity types in clinical and medical text, including disease, sign, symptom, medication, dosage, lab value, body part and procedure. Trained on the Maccrobat clinical case corpus on a DistilBERT base, so it runs cheaply for high-volume tagging.
Claude Opus 4
Anthropic's most powerful model. Exceptional at complex analysis, agentic tasks, and extended reasoning.
Claude Opus 4.8
Anthropic's most capable Opus-tier model. State of the art on long-horizon agentic work, coding and knowledge tasks, with a 1M-token context window at standard pricing.
Start using Cohere Command R+ (08-2024) today
Get started with free credits. No credit card required. Access Cohere Command R+ (08-2024) and 100+ other models through a single API.