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Your pipeline idea—using a lightweight model for prompt improvement, then sending to a larger model—is solid. It can save compute and cost when set up well.
For free, high-quality models on OpenRouter, here are some recommendations:
Lightweight/free models for prompt improvement:
google/gemini-2.5-flash-lite— "optimized for ultra-low latency and cost efficiency...improved throughput, faster token generation, and better performance across common benchmarks compared to earlier Flash models" Gemini 2.5 Flash Litegoogle/gemma-3-4b-it:free— "multimodality, supporting vision-language input and text outputs...improved math, reasoning, and chat capabilities" Gemma 3 4B Freegoogle/gemma-3n-e2b-it:free— "optimized for low-resource deployment...strong multilingual and reasoning performance" Gemma 3n 2B Free
Larger models for main completion:
google/gemma-3-12b-it:free— "second largest...handles context windows up to 128k tokens, improved math, reasoning, and chat" Gemma 3 12B Freegoogle/gemma-3-27b-it:free— "latest open source model...handles up to 128k tokens, improved reasoning and chat" Gemma 3 27B Free
This setup should work well for local pipelines, letting you balance performance and resource usage. For more options, browse the full free/open source list at [OpenRouter Models](<https://openrouter.ai/model...