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MMT Token Optimizer reduces unnecessary prompt and context usage across AI agents, RAG and API workloads while protecting required facts, instructions and outcomes. Published lab tests across 7 provider routes show measured input-token reductions from 6.54% to 22.17%, with before/after evidence and semantic outcome guards.
Hi Product Hunt 👋
I’m Bakker, founder of MMTNEXUS.
We built MMT Token Optimizer around one question:
**Can we reduce LLM prompt and context usage without removing the facts, instructions, and outcomes the model actually needs?**
Instead of treating a shorter prompt as success, the optimizer follows a guarded flow:
**Analyze → Reduce → Validate → Run → Measure**
Our published lab cases across 7 provider routes currently show measured input-token reductions ranging from **6.54% to 22.17%**, including:
• Anthropic: 22.17%
• Google Gemini: 18.21%
• OpenAI: 17.91%
• OpenRouter: 17.91%
• DeepSeek: 17.14%
• Mistral: 17.04%
• xAI: 6.54%
These are measured test cases, not guaranteed savings for every workload.
The area I’m most interested in is **AI agents, RAG, long conversation history, and high-volume API workflows**, where context can grow quickly.
For perspective, an agent workload processing 1M input tokens/day would avoid about **5.37M input tokens/month** at a 17.91% reduction.
We’ve opened the product as a limited free public beta so developers can test it with their own workloads.
I’d especially appreciate feedback on:
• workloads where optimization works well
• cases where the system should refuse to reduce context
• agent/RAG edge cases
• what evidence you would want before trusting optimization in production
Thanks for testing it — and please challenge the methodology. That feedback is more valuable to us than simply getting another signup.
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About MMT Token Optimizer on Product Hunt
“Reduce AI token usage without losing the outcome”
MMT Token Optimizer was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #132 on the daily leaderboard. MMT Token Optimizer reduces unnecessary prompt and context usage across AI agents, RAG and API workloads while protecting required facts, instructions and outcomes. Published lab tests across 7 provider routes show measured input-token reductions from 6.54% to 22.17%, with before/after evidence and semantic outcome guards.
MMT Token Optimizer was featured in API (98.7k followers), Developer Tools (519.8k followers) and Artificial Intelligence (479.1k followers) on Product Hunt. Together, these topics include over 219.1k products, making this a competitive space to launch in.
Who hunted MMT Token Optimizer?
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Nour Omar