FitBenchmarking
The facts below are what the maker publishes about FitBenchmarking, checked and dated. Our editors have not written about it yet.
Plans and prices
From FitBenchmarking's own pricing page, checked 11 Oct 2026.
| Plan | Price | What it gives you |
|---|---|---|
| FitBenchmarking | Free | Open source tool · installable with pip · optional fitting packages may require separate installation |
The facts
Each one read from FitBenchmarking's own pages, with the date it was checked.
What it does
- Audience
- The project identifies scientists, scientific software developers, and mathematicians or numerical software developers as communities interested in fitting software.checked 11 Oct 2026
- Benchmark data
- Shipped problem data includes NIST nonlinear regression, CUTEst, Mantid, and SASView examples.checked 11 Oct 2026
- Comparison caveat
- The documentation cautions that comparing minimizers across different software packages and problem formats may not be on truly equal terms.checked 11 Oct 2026
- Comparisons
- It compares fitting methods by runtime and accuracy on benchmark problems.checked 11 Oct 2026
- Custom datasets
- Users can add custom datasets in supported file formats and run them with available fitting methods.checked 11 Oct 2026
- Custom problems
- Users can analyze problems in supported file formats by passing their paths, and can add custom datasets to the benchmark.checked 11 Oct 2026
- Data import formats
- CUTEst, Native, Mantid, NIST, Horace, HOGBEN, BALchecked 23 Sep 2026
- Dependency limit
- The Gradient-Free-Optimizers integration requires finite bounds on all parameters.checked 11 Oct 2026
- Deployment
- desktopchecked 23 Sep 2026
- External dependencies
- Some benchmark datasets require external packages such as Mantid, CUTEst, pycutest, or sasmodels.checked 11 Oct 2026
- Fitting integrations
- Supported fitting software includes SciPy, Bumps, DFO-LS, GALAHAD, GOFit, Mantid, MATLAB, Minuit, and NLopt.checked 11 Oct 2026
- Installation
- The package can be installed with pip, and the documentation also describes installing from source.checked 11 Oct 2026
- Integrations
- The documented benchmark data includes problems from NIST, CUTEst, Mantid, and SASView examples and tests.checked 11 Oct 2026
- Interactive results
- By default, running FitBenchmarking starts a Dash app to provide interactive plots.checked 11 Oct 2026
- Large problems
- Bundle Adjustment in the Large problems can currently only be run with scipy_ls, which supports sparse Jacobians.checked 11 Oct 2026
- Optional dependencies
- Some fitting packages are optional dependencies, and external software that cannot be installed using pip must be installed separately.checked 11 Oct 2026
- Problem inputs
- Each test problem includes data points, a fitting function definition, and at least one set of initial parameter values.checked 11 Oct 2026
- Purpose
- FitBenchmarking is an open source tool for comparing minimizers and fitting frameworks.checked 11 Oct 2026
- Regression models
- Nochecked 23 Sep 2026
- Scripting support
- Yeschecked 23 Sep 2026
- Security
- The project advises users to install the latest version to stay up to date with security fixes.checked 11 Oct 2026
- Statistical tests
- Nochecked 23 Sep 2026
Money
- Free plan
- Yeschecked 23 Sep 2026
The company
- Maker collaboration
- The package documentation describes it as a collaboration between STFC’s Scientific Computing Department, ISIS Neutron and Muon Facility, and Diamond Light Source.checked 11 Oct 2026
