Battle of the Models

Compare specific LLM models, context windows, and capabilities.

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mistralai/mistral-7b-instruct-v0.2

Replicate

Intelligence Score 76/100
Model Popularity 0 votes
Context Window 32K tokens
Pricing Model Commercial / Paid
View Provider Analysis →

Llama 3.1 70B (via routing)

A-TIER

Requesty

Intelligence Score 87/100
Context Window 128K
Pricing Model Commercial / Paid
Model Popularity 0 votes
View Provider Analysis →
FINAL VERDICT

Llama 3.1 70B (via routing) Wins

With an intelligence score of 87/100 vs 76/100, Llama 3.1 70B (via routing) outperforms mistralai/mistral-7b-instruct-v0.2 by 11 points.

HEAD-TO-HEAD

Detailed Comparison

Feature
mistralai/mistral-7b-instruct-v0.2
Llama 3.1 70B (via routing)
Context Window
32K tokens 128K
Architecture
Transformer (Open Weight) Transformer (Open Weight)
Est. MMLU Score
~70-74% ~80-84%
Release Date
2024 Jul 2024
Pricing Model
Paid / Commercial Paid / Commercial
Rate Limit (RPM)
Varies by model 60 RPM
Daily Limit
Credit-based Credit-based
Capabilities
No specific data
No specific data
Performance Tier
B-Tier (Strong) A-Tier (Excellent)
Speed Estimate
⚡ Very Fast ⚡ Fast
Primary Use Case
General Purpose General Purpose
Model Size
7b 70B
Limitations
  • Pay-per-second billing (can be expensive)
  • Cold starts for less popular models
  • Trial credits are minimal
  • Requires underlying provider API keys
  • Free credit amount is limited
  • Routing adds minimal latency
Key Strengths
  • Run any public model with an API
  • Fine-tune existing models easily
  • Cold boots can be slow for unpopular models
  • AI Router: automatic provider failover
  • Prompt caching for cost savings
  • Multi-provider load balancing

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