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LLM Pricing & Performance Comparison

The definitive, monthly-updated pricing matrix comparing commercial and open-weights foundation models. Compare input token costs, output token costs, context windows, and latency SLAs across Claude 3.5 Sonnet, GPT-4o, and Gemini 1.5 Pro.

Verified & Updated: 2026-08-13

Commercial & Open-Weights Matrix

5 Major Providers
Model NameClassification TagInput Price / 1M TokensOutput Price / 1M TokensContext Window
Anthropic Claude 3.5 SonnetRecommended Enterprise Model$3.00$15.00200k tokens
OpenAI GPT-4oStandard Multi-Modal$2.50$10.00128k tokens
OpenAI GPT-4o MiniHigh-Efficiency API$0.15$0.60128k tokens
Anthropic Claude 3 HaikuFast Lightweight$0.25$1.25200k tokens
Google Gemini 1.5 ProUltra-Long Context$3.50$10.502M tokens
Price-to-Performance Analysis

How to Evaluate LLM Price-to-Performance Ratios

Selecting a foundation model requires evaluating more than just raw token cost. While sub-dollar API options like GPT-4o Mini ($0.15/M input) offer incredible cost efficiency for basic classification, complex multi-step reasoning in LangGraph agents often requires Claude 3.5 Sonnet ($3.00/M input) to prevent state loops.

The Hidden Overhead of Context Window Scaling

Models offering ultra-long context windows (such as Gemini 1.5 Pro's 2-million-token window) enable ingesting entire codebases in a single prompt, but long-context API calls incur exponential time-to-first-token latency penalties. Utilizing a RAG vector database (pgvector or Pinecone) alongside a 128k context model yields faster sub-50ms responses at 10% of the token cost.

Buyer FAQ

Frequently Asked Questions

How frequently is this LLM pricing comparison table updated?↓

This page is maintained directly by Founder & Principal AI Architect Umar Abbas and updated monthly. Last updated: 2026-08-13.

Are open-weights models included in the pricing comparison?↓

Yes. For open-weights models like Llama 3 or Qwen 2.5, we compute hosting costs based on dedicated vLLM instance VRAM requirements.