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Command R (Mar '24)
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Command R (Mar '24)

March 2024 early version of Cohere's Command-R, laying foundation for enterprise RAG applications. Specifically designed for retrieval-augmented generation tasks with excellent document understanding and information integration capabilities. Accurately understands query intent and extracts relevant information from large document collections. This version provided important foundation for subsequent Command-R series development, suitable for enterprise knowledge management and intelligent Q&A systems.
Intelligence(Weak)
Speed(Relatively Fast)
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
￥5.4 /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 (Mar '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
2024-03-12
Response Speed
166.27,155 tokens/s

Benchmark Scores

Below is the performance of Command-R (Mar '24) in various standard benchmark tests. These tests evaluate the model's capabilities in different tasks and domains.
Intelligence Index
14.68
Large Language Model Intelligence Level
Coding Index
5.51
Indicator of AI model performance on coding tasks
Math Index
8.53
Capability indicator in solving mathematical problems, mathematical reasoning, or performing math-related tasks
MMLU Pro
33.8
Massive Multitask Multimodal Understanding - Testing understanding of text, images, audio, and video
GPQA
28.4
Graduate Physics Questions Assessment - Testing advanced physics knowledge with diamond science-level questions
HLE
4.8
The model's comprehensive average score on the Hugging Face Open LLM Leaderboard
LiveCodeBench
4.8
Specific evaluation focused on assessing large language models' ability in real-world code writing and solving programming competition problems
SciCode
6.2
The model's capability in code generation for scientific computing or specific scientific domains
HumanEval
40
Score achieved by the AI model on the specific HumanEval benchmark test set
Math 500 Score
16.4
Score on the first 500 larger, more well-known mathematical benchmark tests
AIME Score
0.7
An indicator measuring an AI model's ability to solve high-difficulty mathematical competition problems (specifically AIME level)
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