AI Workload Estimator

Plan an LLM-assisted research project on MindRouter

Research Built with OpenCode on MindRouter

Plan an LLM-assisted research project before you start

Have an AI read 40,000 survey comments or 2 million abstracts? Estimate the tokens, wall-clock time and quota you will need on MindRouter — and pick the right model — before you commit a single request.

1

Corpus

Survey comments, abstracts, transcripts, code files…

1 page ≈ 300 words ≈ 390 tokens

No sample yet — using the length above.
2

Task & model

Task template
3

Throughput assumptions

estimates

MindRouter's model catalog does not include throughput, pricing or quota figures. These are editable estimates — set them to match what you observe on MindRouter and your concurrency.

Tokens per second, by model class
Class Prompt tok/s Generation tok/s
Small ≤ 14B
Medium 15–40B
Large > 40B
Embedding

Prompt (prefill) tokens are processed far faster than generated tokens. Each model's class is derived from its id.

Assumes each request keeps its speed as concurrency rises — real servers slow down under heavy load.

Results

estimates

Describe your corpus to see an estimate

Enter a number of items and an average length (or paste a sample) in step 1, then pick a task and a model. Everything below updates live.

Data & sources