OptiLLM

What it does

OptiLLM automatically reduces LLM API costs by up to 50%+ without sacrificing quality. It routes each prompt to the cheapest capable model using ML classifiers, compresses tokens with LLMLingua-2, and caches semantically similar queries with FAISS vector search. Drop-in OpenAI-compatible proxy — no code changes needed. Includes evaluation tools, analytics dashboards, and custom router training to continuously optimize your cost-quality tradeoff.

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  LLM Pricing2024 · ailesson.io · ▲178

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  LLM StatsOct 2025 · llm-stats.com · ▲293

  I was overspending on GPT-4o. It was really hard to compare different models I could switch to, so I built this LLM comparison tool. It shows leaderboards, pricing, and performance data across 100+ LLMs (including all major providers and open-source models). Key features: - Live pricing comparisons - Benchmark Scores (MMLU, HumanEval, GPQA, etc.) - Context length vs cost analysis - Speed/throughput tests across providers - Quality vs price visualizations - Open source (all data verifiable) Try it out: https://llmstats.com I'd like to know your opinion :) Tech stack: Next.js,…

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  Frugon – Find which LLM calls a cheaper model could handle (local, MIT)Jul 2026 · github.com · ▲67

  I started leaning in on AI heavily this year, as I wanted to get more done autonomously, but then my token usage climbed dramatically to the point where my weekly quota would run out before the end of the week, sometimes a couple of days into the week. I realised I had to do something about it else I'd have to double my spend. So I decided to start tracking my cost per task type. This revealed that a lot of my spend went to searches/scans or simple things like scouting tasks. I then decided to turn this into a simple CLI tool that can be used to read your OpenAI-style logs locally, and…

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