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LLM Inference Pricing Compared Across 7 Providers, Highlighting Caching Costs

A user compiled a spreadsheet comparing LLM inference pricing across seven providers, including OpenAI, Anthropic, Cohere, and Mistral AI. The comparison focuses on input/output token pricing, context windows, and cached input costs, rather than performance benchmarks. A key finding is the significant variation in cached input pricing, which can be tens of times cheaper than non-cached inputs, making it a crucial factor for applications like agents and RAG pipelines. AI

IMPACT Highlights the importance of caching costs for LLM inference, potentially influencing application design and provider selection.

RANK_REASON User-generated comparison of LLM inference pricing and features. [lever_c_demoted from research: ic=1 ai=0.7]

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AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM Inference Pricing Compared Across 7 Providers, Highlighting Caching Costs

COVERAGE [1]

  1. r/MachineLearning TIER_1 English(EN) · /u/Technomadlyf ·

    I compiled LLM inference pricing across 7 providers — the caching numbers are surprising(spreadsheet included) [R]

    <table> <tr><td> <a href="https://www.reddit.com/r/MachineLearning/comments/1ueavxn/i_compiled_llm_inference_pricing_across_7/"> <img alt="I compiled LLM inference pricing across 7 providers — the caching numbers are surprising(spreadsheet included) [R]" src="https://preview.redd…