Battle of the Models

Compare specific LLM models, context windows, and capabilities.

DeepSeek-V4 Pro

A-TIER

DeepSeek

Intelligence Score 80/100

Model Popularity 0 votes

Context Window 128K

Pricing Model Commercial / Paid

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A-TIER

Ollama

Intelligence Score 84/100

Context Window 32K tokens

Pricing Model Free / Open

Model Popularity 0 votes

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FINAL VERDICT

With an intelligence score of 84/100 vs 80/100, Mistral Nemo 12B outperforms DeepSeek-V4 Pro by 4 points.

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

HEAD-TO-HEAD

Feature	DeepSeek-V4 Pro	Mistral Nemo 12B
Context Window	128K	32K tokens
Architecture	Dense Transformer	Transformer (Open Weight)
Est. MMLU Score	~75-79%	~75-79%
Release Date	2024	2024
Pricing Model	Paid / Commercial	Free Tier
Rate Limit (RPM)	60 RPM	Hardware limited
Daily Limit	Credit-based	Unlimited
Capabilities	No specific data	Multilingual
Performance Tier	B-Tier (Strong)	B-Tier (Strong)
Speed Estimate	Medium	Medium
Primary Use Case	General Purpose	General Purpose
Model Size	Undisclosed	12B
Limitations	10M tokens is one-time only API can be slow during peak hours (Chinese business hours) Rate limiting during high demand periods	Depends on your RAM/GPU Laptop fans will spin up Large models (70B+) need heavy hardware
Key Strengths	DeepSeek-R1: OpenAI o1-level reasoning (open-source) Mixture-of-Experts architecture for efficiency OpenAI-compatible API (drop-in replacement)	Local Inference: Data never leaves your device Modelfiles: Script your own system prompts API: Local REST API for app integration