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Command A
C

Command A

Advanced model in Cohere's Command series, designed for instruction-following and enterprise applications. Features strong instruction understanding and execution capabilities, supports production-grade deployment. Particularly excels in enterprise-level conversation, document processing, and complex instruction execution tasks. Designed with enterprise environment needs in mind, providing reliable stable AI services for customer service and content generation.
Intelligence(Relatively Weak)
Speed(Relatively Fast)
Input Supported Modalities
Yes
Is Reasoning Model
256,000
Context Window
-
Maximum Output Tokens
-
Knowledge Cutoff
Go Compare

Pricing

- /M tokens
Input
- /M tokens
Output
￥31.5 /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 ATechnical Parameters
Parameter Count
Not Announced
Context Length
256.00k tokens
Training Data Cutoff
Open Source Category
Open Weights (License Required for Commercial Use)
Multimodal Support
Text Only
Throughput
Release Date
2025-03-13
Response Speed
169.6,813 tokens/s

Benchmark Scores

Below is the performance of Command A in various standard benchmark tests. These tests evaluate the model's capabilities in different tasks and domains.
Intelligence Index
39.95
Large Language Model Intelligence Level
Coding Index
28.4
Indicator of AI model performance on coding tasks
Math Index
45.77
Capability indicator in solving mathematical problems, mathematical reasoning, or performing math-related tasks
MMLU Pro
71.2
Massive Multitask Multimodal Understanding - Testing understanding of text, images, audio, and video
GPQA
52.7
Graduate Physics Questions Assessment - Testing advanced physics knowledge with diamond science-level questions
HLE
4.6
The model's comprehensive average score on the Hugging Face Open LLM Leaderboard
LiveCodeBench
28.7
Specific evaluation focused on assessing large language models' ability in real-world code writing and solving programming competition problems
SciCode
28.1
The model's capability in code generation for scientific computing or specific scientific domains
HumanEval
81.5
Score achieved by the AI model on the specific HumanEval benchmark test set
Math 500 Score
81.9
Score on the first 500 larger, more well-known mathematical benchmark tests
AIME Score
9.7
An indicator measuring an AI model's ability to solve high-difficulty mathematical competition problems (specifically AIME level)
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