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.
Corpus
Survey comments, abstracts, transcripts, code files…
1 page ≈ 300 words ≈ 390 tokens
Task & model
Throughput assumptions
estimatesMindRouter'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.
| 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
estimatesDescribe 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.
Quota
your own budget — quotas vary by group, so enter yours= 100M tokens (placeholder — enter your own)
Timeline
Sensitivity
drag to see how time & tokens moveCompare 3 models
pick any three — click a row to select it| Model | Class | Window | Fits? | Tokens | Time |
|---|
Chunking visualizer
Python batching snippet
OpenAI-compatible MindRouter endpoint, your settings
Data & sources
-
MindRouter model catalog —
/v1/models, University of Idaho Research Computing and Data Services (RCDS) / Institute for Interdisciplinary Data Sciences (IIDS). Snapshot retrieved : 73 entries (63 models + 10 aliases). mindrouter.uidaho.edu The raw endpoint requires a MindRouter API key, so we link the public home page. - Throughput, time & quota figures — not from MindRouter. The catalog contains no tokens/second, latency, pricing or quota data. Every time/throughput number in this app is an editable estimate you set in step 3; nothing here is a real University of Idaho figure.
- Token counts — computed with an approximate JavaScript tokenizer (word / sub-word / non-Latin heuristics). These are estimates of fit, not exact counts from each model's own tokenizer.