Command R (Aug '24)

August 2024 release of Cohere's Command-R model, optimized for enterprise RAG (Retrieval-Augmented Generation) applications. Features powerful document understanding and information retrieval capabilities, accurately extracting and integrating information from large document collections. Particularly suitable for enterprise knowledge base Q&A, document analysis, and complex query tasks combining external knowledge sources, providing reliable knowledge processing for enterprise AI.

Intelligence(Weak)

Speed(Relatively Slow)

Input Supported Modalities

Yes

Is Reasoning Model

128,000

Context Window

Maximum Output Tokens

Knowledge Cutoff

Go Compare

Pricing

- /M tokens

Input

- /M tokens

Output

￥1.89 /M tokens

Blended Price

Quick Simple Comparison

Input

Output

Command A

Command-R+ (Apr '24)

￥0.25

Command-R+ (Aug '24)

￥0.25

Basic Parameters

Command-R (Aug '24)Technical Parameters

Parameter Count

Not Announced

Context Length

128.00k tokens

Training Data Cutoff

Open Source Category

Open Weights (License Required for Commercial Use)

Multimodal Support

Text Only

Throughput

Release Date

2024-08-30

Response Speed

69.0,383 tokens/s

Benchmark Scores

Below is the performance of Command-R (Aug '24) in various standard benchmark tests. These tests evaluate the model's capabilities in different tasks and domains.

Intelligence Index

Large Language Model Intelligence Level

14.83

Large Language Model Intelligence Level

Coding Index

Indicator of AI model performance on coding tasks

6.56

Indicator of AI model performance on coding tasks

Math Index

Capability indicator in solving mathematical problems, mathematical reasoning, or performing math-related tasks

7.63

Capability indicator in solving mathematical problems, mathematical reasoning, or performing math-related tasks

MMLU Pro

Massive Multitask Multimodal Understanding - Testing understanding of text, images, audio, and video

33.7

Massive Multitask Multimodal Understanding - Testing understanding of text, images, audio, and video

GPQA

Graduate Physics Questions Assessment - Testing advanced physics knowledge with diamond science-level questions

28.9

Graduate Physics Questions Assessment - Testing advanced physics knowledge with diamond science-level questions

HLE

The model's comprehensive average score on the Hugging Face Open LLM Leaderboard

5.1

The model's comprehensive average score on the Hugging Face Open LLM Leaderboard

LiveCodeBench

Specific evaluation focused on assessing large language models' ability in real-world code writing and solving programming competition problems

4.4

Specific evaluation focused on assessing large language models' ability in real-world code writing and solving programming competition problems

SciCode

The model's capability in code generation for scientific computing or specific scientific domains

8.7

The model's capability in code generation for scientific computing or specific scientific domains

HumanEval

Score achieved by the AI model on the specific HumanEval benchmark test set

41.5

Score achieved by the AI model on the specific HumanEval benchmark test set

Math 500 Score

Score on the first 500 larger, more well-known mathematical benchmark tests

14.9

Score on the first 500 larger, more well-known mathematical benchmark tests

AIME Score

An indicator measuring an AI model's ability to solve high-difficulty mathematical competition problems (specifically AIME level)

0.3

An indicator measuring an AI model's ability to solve high-difficulty mathematical competition problems (specifically AIME level)

Output tokens/million

Output tokens/million

Output tokens/million

Output tokens/million

Output tokens/million

131k

Context Length

Gemini 2.0 Flash Lite (Preview)

Output tokens/million

Output tokens/million

33k

Context Length

Qwen2.5 Coder Instruct 32B

Output tokens/million

131k

Context Length

Empowering the Future, Your AI Solution Knowledge Base

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