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Machine Learning · head to head

JMP vs Mistral AI

JMP logo

JMP

Machine Learning

Desktop statistical and design of experiments software from a SAS subsidiary

From
Free
Rated
-
Full reviewWebsite
Mistral AI logo

Mistral AI

Machine Learning

European AI lab with open models, API platform and Le Chat assistant

From
On request
Rated
-
Full reviewWebsite

The short version

  • Only JMP has a free tier, so it costs nothing to try first.
  • Each has a real cost: JMP it is a desktop application holding the working table in memory, so a data set that outgrows the workstation has no in-place upgrade path, only a move to a different tool and a different skill set.; Mistral AI smaller model selection compared to OpenAI; Mistral Medium 3.5 significantly more expensive than competing mid-tier models
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which JMP and Mistral AI actually diverge.

Attributes where JMP and Mistral AI differ
AttributeJMPMistral AI
Starting priceFreeOn request
Pricing modelsubscriptionusage-based
Free tierYesNo
PlatformsMac, WindowsWeb, API, Self-hosted, Cloud (AWS, Google Cloud, Azure, SAP, IBM, Snowflake, NVIDIA, Outscale)
Founded1976Unknown

Identical on both: user rating (Not yet rated), category (Machine Learning).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in JMP

  • Custom design of experiments
  • Linked interactive graphics
  • Analysis platforms
  • Quality and process tools
  • Graph Builder
  • JSL scripting
  • Scoring code export
  • Predictive modelling in JMP Pro

Only in Mistral AI

Nothing recorded that JMP does not also cover.

What people use each for

The jobs each tool is most often brought in to do.

JMP

  • Planning a physical experiment where each run is expensive, and the question is which twelve runs to perform rather than how to model data you already havenot Mistral AI
  • Process capability and control chart work in a manufacturing quality function that has to produce evidence for a customer or regulatornot Mistral AI
  • Exploratory analysis by an engineer or scientist who will not write code but needs more than a spreadsheetnot Mistral AI
  • Semiconductor, chemical and pharmaceutical development groups where JMP is already the shared language for reporting resultsnot Mistral AI

Mistral AI

  • EU-regulated workloads requiring data residency outside USnot JMP
  • Custom model training and domain-specific fine-tuningnot JMP
  • Multi-modal document processing with OCRnot JMP
  • Autonomous development with Vibe for Codenot JMP

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

JMP

  • It is a desktop application holding the working table in memory, so a data set that outgrows the workstation has no in-place upgrade path, only a move to a different tool and a different skill set.
  • There is no Linux build and no server edition for running analyses, so JMP cannot sit in a scheduled pipeline the way an R or Python script can, and recurring reports depend on a named person running them on a laptop.
  • The predictive modelling capability most buyers mean when they call this machine learning software is in JMP Pro, a separate and more expensive licence, so the base product's price is not the price of the thing being evaluated.
  • JSL is proprietary to JMP, so the scripts, add-ins and automation a group accumulates over a decade do not port anywhere and become sunk cost the moment anyone questions the renewal.
  • Deployment ends at exported scoring code with no registry, monitoring or retraining, so a model that runs in production is maintained by another team in another language and steadily diverges from the version the analyst still has open.

Mistral AI

  • Smaller model selection compared to OpenAI; Mistral Medium 3.5 significantly more expensive than competing mid-tier models
  • Batch processing only available at 50% discount, not free tier
  • No free tier; all API access requires payment

Pricing, plan by plan

JMP

Free
  • TrialFree
    • 30-day trial
    • Full features
  • JMP$1785/year
    • Core JMP
    • Standard features

Mistral AI

On request
  • Mistral Small 4$0.15/per million input tokens
    • Multimodal
    • Multilingual
    • Apache 2.0 license
  • Mistral Small 4 output$0.6/per million output tokens
    • Same model
  • Mistral Large 3$0.5/per million input tokens
    • General-purpose flagship
  • Mistral Large 3 output$1.5/per million output tokens
    • Same model

Which should you pick?

Choose JMP if

  • You need custom design of experiments.
  • You want to start without paying.
  • You work on Mac, Windows.
  • You also want linked interactive graphics.
JMP review

Choose Mistral AI if

  • You work on Web, API, Self-hosted, Cloud (AWS, Google Cloud, Azure, SAP, IBM, Snowflake, NVIDIA, Outscale).
Mistral AI review

Questions people ask

Is JMP or Mistral AI better?
Neither clearly leads. JMP starts at Free and Mistral AI at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, JMP or Mistral AI?
JMP has a free tier; the other does not. Paid plans start at Free for JMP and On request for Mistral AI.
Does JMP or Mistral AI run on more platforms?
JMP runs on Mac, Windows. Mistral AI runs on Web, API, Self-hosted, Cloud (AWS, Google Cloud, Azure, SAP, IBM, Snowflake, NVIDIA, Outscale).
Can I use JMP for free?
Yes. JMP has a free tier, so you can try it without paying. Mistral AI starts at On request.
What is JMP best used for?
JMP is most often used for planning a physical experiment where each run is expensive, and the question is which twelve runs to perform rather than how to model data you already have, process capability and control chart work in a manufacturing quality function that has to produce evidence for a customer or regulator, exploratory analysis by an engineer or scientist who will not write code but needs more than a spreadsheet, semiconductor, chemical and pharmaceutical development groups where jmp is already the shared language for reporting results. Of those, planning a physical experiment where each run is expensive, and the question is which twelve runs to perform rather than how to model data you already have and process capability and control chart work in a manufacturing quality function that has to produce evidence for a customer or regulator are not what Mistral AI is typically brought in for.
What can JMP do that Mistral AI cannot?
JMP covers Custom design of experiments, Linked interactive graphics, Analysis platforms, Quality and process tools.

Answered from the vendors’ own pages

JMP: Is JMP the same thing as SAS?

No. JMP is a separate desktop product from a SAS subsidiary, with its own interface, its own scripting language and its own licence. Knowing SAS does not transfer to it beyond the statistics.

Mistral AI: How much does Mistral AI cost?

Mistral AI offers a free plan with 10 USD/month in API credits, Pro at 14.99 USD/month with 30 USD/month in credits, and Team at 24.99 USD per user/month with a 50 USD minimum.

Source
JMP: Do I need JMP Pro?

If you want cross validation, penalised regression, boosted trees or neural networks, yes. The base edition covers classical statistics, graphics and design of experiments well and stops short of predictive modelling.

Mistral AI: Is there a free plan?

Yes, Mistral AI includes a free plan with 10 USD/month in API credits, Studio access, and 100+ connectors for limited use.

Source
JMP: Does it run on Linux?

No. Windows and macOS only, as an installed application.

Mistral AI: What are the API costs?

API pricing is per million tokens for most models with input and output charged separately; OCR costs per 1,000 pages; speech models charged per minute.

Source
JMP: Can I put a JMP model into production?

Only by exporting the scoring formula as SQL, C, Python or similar and running it in another system. JMP itself does not serve, monitor or retrain models.

JMP: Who actually uses it?

Process and quality engineers, and scientists in R&D, particularly in semiconductor, chemicals, pharmaceutical and medical device work. It is not usually chosen by data engineering or platform teams.

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