Model Comparison

Chronos vs TimesFM: a practical comparison

Both are strong zero-shot time series foundation models. The right choice depends on your context length requirements, latency budget, and the characteristics of your data.

Head-to-head benchmarksLatency comparisonTry both on your data
Side by sideSame request, different models
# Chronos
{"model": "amazon/chronos-bolt-base", "inputs": [...], "parameters": {"prediction_length": 14}}

# TimesFM
{"model": "google/timesfm-2.0", "inputs": [...], "parameters": {"prediction_length": 14}}

Swap the model ID. Everything else stays the same.

Chronos

Transformer-based, multiple sizes from mini to large

TimesFM

Patched-decoder, strong on long-horizon forecasts

On TSFM.ai

Both available through one API with identical request shapes

Feature comparison

When to choose which

Choose Chronos when

You need multiple model sizes to trade off latency vs accuracy, want native probabilistic forecasts, or need the flexibility of choosing mini through large variants.

Choose TimesFM when

You want a single well-tuned model without size selection complexity, need strong long-horizon performance, or are working with data patterns similar to its training distribution.

Or compare both

Send the same series to both models through the TSFM.ai API and compare forecasts, quantiles, and latency on your own data before committing.

Try in playground

Frequently Asked Questions

Compare on your own data

The best model comparison is the one you run on your own series. Try both in the playground.