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Large Language ModelText GenerationCohere Command FamilyEnriched

Cohere Command R+

Cohere Command R+ is a production-grade large language model optimized for enterprise workloads, retrieval-augmented generation (RAG), and tool use. It is designed for long-context reasoning, multilingual understanding, and efficient deployment on Cohere's managed API and partner clouds.

by CohereReleased 2024-03-07Proprietary
Context Window
128K
MMLU
88.2%
HumanEval
71.4%
API Access
Available
Fine-tuning
Supported

Key Capabilities

  • +Long-context understanding up to 128K tokens
  • +Optimized for retrieval-augmented generation (RAG) with native rerank integration
  • +Strong multilingual performance across major languages
  • +Tool use and function calling for workflow automation
  • +Enterprise features including data residency and privacy controls
  • +Efficient inference for production-scale deployments

Limitations

  • -Proprietary model with no access to weights
  • -Exact parameter count and training data details are not publicly disclosed
  • -May hallucinate or produce incorrect answers without proper grounding
  • -Fine-tuning options are more limited than fully open-source models
  • -Public standardized benchmark coverage is thinner than some research models

Benchmark Performance

conversation

conversation

Chatbot Arena Elo

1145.0Elo

reasoning

reasoning

Massive Multitask Language Understanding

75.7%
reasoning

Massive Multitask Language Understanding

88.2%

math

mathsource

Grade School Math 8K

66.9%
math

Grade School Math 8K

70.7%

coding

coding

HumanEval

71.4%
codingsource

HumanEval

71.4%

Alternatives & Comparisons

GPT-4oLLM

Frontier-level general performance and multimodal capabilities via OpenAI API.

Strengths
  • + Very strong reasoning and coding
  • + Rich ecosystem and tooling
Weaknesses
  • - Proprietary and closed weights
  • - Region and data-control constraints for some enterprises
Claude 3.5 SonnetLLM

Strong reasoning and writing quality with enterprise-friendly privacy posture.

Strengths
  • + High-quality long-form writing and analysis
  • + Competitive coding and reasoning
Weaknesses
  • - Proprietary and closed weights
  • - Availability and pricing vary by region and partner
Llama 3.1 70B InstructLLM

Open-weight alternative suitable for self-hosting with strong general performance.

Strengths
  • + Open weights and flexible deployment
  • + Strong general and coding performance
Weaknesses
  • - Requires significant infrastructure to self-host at scale
  • - Enterprise features and SLAs depend on chosen provider

Sources

cohere.comdocs.cohere.comdocs.cohere.comanakin.aillm-stats.comobot.ai