Model
Mistral
Publisher
Mistral AI (France)
Licence
Mistral Research Licence 0.1 (MRL) / Mistral AI Non-Production Licence 0.1 (MNPL for Codestral)
You have only limited ownership of these Mistral models: self-hosting keeps your data yours and the models are capable and well-supported (data-control and reliability strong), but the MRL/MNPL bar commercial and production use without a separately negotiated licence, so use-and-modify is weak - ownership is limited. For anything beyond research or evaluation, either negotiate a commercial licence with Mistral or use the Apache-2.0 Mistral models (the `mistral` entry), which carry none of these restrictions and reach substantial ownership.
Do you really own it?
Limited
none·limited·partial·substantial·full
Analytical input: AOI C · 63.2/100
The four ownership factors
Floor-weighted, not averaged. Use & modify is weak, and the weakest factor sets the ceiling, so the verdict stays limited however strong the rest.
1
Use and modify freelyCan you run, modify and adapt it with no gate and no field-of-use trap?
WeakThe MRL restricts use to research/academic/non-profit purposes, and the MNPL to non-production testing/evaluation; any commercial or production use of the model or a derivative requires a separately negotiated Mistral licence. A blanket non-commercial bar is the weakest use-and-modify position - weak.
How this scores (AOI sub-dimensions)
Openness2/5how much is released - weights, data, code, licence - and how freelyWeights are openly downloadable and inspectable, but the licence bars commercial / production use (research or non-production only), training data and code are closed, and evaluation is partial.
Legal2/5how permissive and clean the licence is for real commercial useA clear licence from an EU-domiciled provider (France law) with good documentation, but it bars commercial / production use without a separately negotiated agreement, so the open-source exemption does not apply and a commercial adopter cannot use it as released.
2
TransparencyDo you know what it is: weights, training, behaviour, and legible terms?
ModerateWeights are inspectable and Mistral documents its models well, but training data and code are closed - open weights, closed process, so moderate.
How this scores (AOI sub-dimensions)
Provenance4/5how well we can trace and verify what went into the modelVerified mistralai org on Hugging Face, safetensors with checksums, a clear canonical source and a documented per-model licence table, no malicious-checkpoint incident (checklist ~5/8).
Governance4/5how accountable and well-documented the publisher isA strong governance posture: a named, accountable, EU-domiciled provider (Mistral AI), an early GPAI Code of Practice signatory with a public licence table and good documentation.
3
ReliabilityIs it reliable and good enough for the job?
StrongPerformance 4, operational 4 and safety 3 from an accountable EU provider: capable models with strong serving; all at or above 3, two at 4, so reliability is strong. The caveat is the absence of a first-party guard model.
How this scores (AOI sub-dimensions)
Operational4/5how practical it is to run, serve and maintain in productionStrong serving mechanics (vLLM, transformers, llama.cpp, Ollama) and community quants, but the largest models (123B/124B) need serious infrastructure and, decisively, production deployment is licence-barred - so the practical operational ceiling is lower than the Apache line.
Safety3/5whether misuse risks are evaluated and guardrails are providedSafety-tuned instruct/multimodal models with documented behaviour, meeting the score-3 anchor, but Mistral has historically shipped lighter safety tuning than some peers and no first-party guard model - deployers must add their own guardrails.
4
Doesn't extract your dataDoes running it keep your knowledge and data yours?
StrongSelf-hosted for research, the weights run entirely on your own infrastructure with no telemetry or clawback, so your data stays yours - the constraint is on permitted use, not data residency.
How this scores
Not a scored AOI dimension. For a self-hosted model, data-control is a structural property of running the weights yourself, strong by default unless the model phones home or the licence claws back rights. For a hosted API this factor is the retention + train-on-inputs + residency read, scored from the binding terms.
How the AOI score is computed
The seven dimensions above, each scored 0 to 5, weighted and summed to the 0 to 100 headline. The score is the analytical input behind the ownership verdict, not the verdict itself.
DimensionScoreWeightPoints
Openness2/50.187.2
Provenance4/50.1612.8
Legal2/50.166.4
Safety3/50.169.6
Performance4/50.1411.2
Operational4/50.129.6
Governance4/50.086.4
HeadlineC · 63.2/100
Sources
Every rating traces to a primary document. Read means the text was verified; unverified means it is known to exist but has not yet been read.
DocumentWhat it grounds
Licenceread2026-08-03
Mistral licence docs, read: [MRL-0.1] use "solely for (a) personal, scientific or academic research, and (b) for non-profit and non-commercial purposes", excluding revenue activity and SaaS distribution (Ministral 8B, Mistral Large, Pixtral Large).
Model cardread2026-08-03
mistralai Hugging Face org (verified) and Mistral docs: Ministral 8B, Mistral Large 2 (123B), Pixtral Large (124B multimodal) tagged MRL; Codestral tagged MNPL; safetensors with checksums; broad serving (vLLM, transformers, llama.cpp, Ollama).
Third-party analysisunverified2026-08-03
On public leaderboards Mistral Large 2 and Pixtral Large are strong open-weight models, and Ministral 8B is strong for its size; not OneHill-reproduced this session.
Third-party analysisunverified2026-08-03
Mistral has historically shipped lighter safety tuning than some peers and no first-party guard model; independent behavioural analysis is limited.
Third-party analysisunverified2026-08-03
Mistral AI is EU-domiciled (France) and an early GPAI Code of Practice signatory with public per-model licensing and good documentation, but the MRL/MNPL are non-commercial (non-FOSS), so no open-source exemption applies on the licence axis.