Battle of the Models

Compare specific LLM models, context windows, and capabilities.

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Command R7B (12-2024)

A-TIER

Cohere

Intelligence Score 87/100
Model Popularity 0 votes
Context Window 128K
Pricing Model Free / Open
View Provider Analysis →

Llama 3 8B Instruct

BentoML

Intelligence Score 71/100
Context Window 8K
Pricing Model Commercial / Paid
Model Popularity 0 votes
View Provider Analysis →
FINAL VERDICT

Command R7B (12-2024) Wins

With an intelligence score of 87/100 vs 71/100, Command R7B (12-2024) outperforms Llama 3 8B Instruct by 16 points.

Clear Winner: Significant performance advantage for Command R7B (12-2024).
HEAD-TO-HEAD

Detailed Comparison

Feature
Command R7B (12-2024)
Llama 3 8B Instruct
Context Window
128K 8K
Architecture
Transformer Transformer (Open Weight)
Est. MMLU Score
~80-84% ~65-69%
Release Date
2024 2024
Pricing Model
Free Tier Paid / Commercial
Rate Limit (RPM)
20 requests/minute Hardware dependent
Daily Limit
- Unlimited
Capabilities
No specific data
No specific data
Performance Tier
A-Tier (Excellent) C-Tier (Good)
Speed Estimate
⚡ Very Fast ⚡ Very Fast
Primary Use Case
General Purpose General Purpose
Model Size
7B 8B
Limitations
  • Monthly quota shared across all models
  • 1,000 API calls/month on free tier
  • Commercial use restricted on free plan
  • Learning curve for 'Bento' concept
  • Deployment requires cloud knowledge
  • Local serving is just step 1
Key Strengths
  • Retrieval Augmented Generation (RAG) built-in
  • Enterprise-grade models
  • Embeddings and reranking models
  • Unified Model Store
  • Distributed Runner Architecture
  • Deployment Agnostic

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