Battle of the Models

Compare specific LLM models, context windows, and capabilities.

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VS
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LLaVA 1.5

A-TIER

llamafile

Intelligence Score 81/100
Model Popularity 0 votes
Context Window Local
Pricing Model Free / Open

DeepSeek Coder V2

A-TIER

Ollama

Intelligence Score 85/100
Context Window 64K tokens
Pricing Model Free / Open
Model Popularity 0 votes
FINAL VERDICT

DeepSeek Coder V2 Wins

With an intelligence score of 85/100 vs 81/100, DeepSeek Coder V2 outperforms LLaVA 1.5 by 4 points.

Close Match: The difference is minimal. Consider other factors like pricing and features.
HEAD-TO-HEAD

Detailed Comparison

Feature
LLaVA 1.5
DeepSeek Coder V2
Context Window
Local 64K tokens
Architecture
Transformer Dense Transformer
Est. MMLU Score
~75-79% ~80-84%
Release Date
2024 2024
Pricing Model
Free Tier Free Tier
Rate Limit (RPM)
Hardware dependent Hardware limited
Daily Limit
Unlimited Unlimited
Capabilities
Vision
No specific data
Performance Tier
B-Tier (Strong) A-Tier (Excellent)
Speed Estimate
Medium Medium
Primary Use Case
General Purpose 💻 Code Generation
Model Size
Undisclosed Undisclosed
Limitations
  • File sizes are large (contain weights)
  • CLI usage often required
  • Windows requires appending .exe
  • Depends on your RAM/GPU
  • Laptop fans will spin up
  • Large models (70B+) need heavy hardware
Key Strengths
  • Executable weight files (multi-OS)
  • Integrated Web UI
  • OpenAI Compatible API server
  • Local Inference: Data never leaves your device
  • Modelfiles: Script your own system prompts
  • API: Local REST API for app integration

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