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Command R+ (Apr '24)
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Command R+ (Apr '24)

The C4AI Command R+ is a large model with 104 billion parameters, possessing advanced capabilities, including Retrieval Augmented Generation (RAG) and multi-step tool usage, and optimized for multilingual tasks.
Intelligence(Weak)
Speed(Relatively Slow)
Input Supported Modalities
No
Is Reasoning Model
128,000
Context Window
128,000
Maximum Output Tokens
2023-02-01
Knowledge Cutoff
Go Compare

Pricing

￥1.8 /M tokens
Input
￥7.2 /M tokens
Output
￥43.2 /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+ (Apr '24)Technical Parameters
Parameter Count
104,000.0M
Context Length
128.00k tokens
Training Data Cutoff
2023-02-01
Open Source Category
Open Weights (License Required for Commercial Use)
Multimodal Support
Text Only
Throughput
100
Release Date
2024-04-04
Response Speed
74.3,998 tokens/s

Benchmark Scores

Below is the performance of Command-R+ (Apr '24) in various standard benchmark tests. These tests evaluate the model's capabilities in different tasks and domains.
Intelligence Index
19.9
Large Language Model Intelligence Level
Coding Index
11.99
Indicator of AI model performance on coding tasks
Math Index
14.27
Capability indicator in solving mathematical problems, mathematical reasoning, or performing math-related tasks
MMLU Pro
43.2
Massive Multitask Multimodal Understanding - Testing understanding of text, images, audio, and video
GPQA
32.3
Graduate Physics Questions Assessment - Testing advanced physics knowledge with diamond science-level questions
HLE
4.5
The model's comprehensive average score on the Hugging Face Open LLM Leaderboard
LiveCodeBench
12.2
Specific evaluation focused on assessing large language models' ability in real-world code writing and solving programming competition problems
SciCode
11.8
The model's capability in code generation for scientific computing or specific scientific domains
HumanEval
63.7
Score achieved by the AI model on the specific HumanEval benchmark test set
Math 500 Score
27.9
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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