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What's happening in robotics and AI — curated by the wujec.ai editors.
Mistral no longer sells a reasoning model: every Magistral now sits in the retirement table
In June 2025 Mistral AI launched Magistral, its own family of models that write out a chain of reasoning before answering. Fifteen months later the family is gone from the vendor's API. All six members — Magistral Small 1.0, 1.1 and 1.2, and Magistral Medium 1.0, 1.1 and 1.2 — appear in the retirement table of the lab's own documentation. The last two were switched off on 31 July 2026. The replacements named in that table are not another reasoning model. Users of Magistral Small are pointed to Mistral Small 4, users of Magistral Medium to Mistral Medium 3.5 — both ordinary mainline models. Reasoning did not disappear from the lab's line-up; it stopped being a product of its own. Since December 2025 the Ministral 3 family has shipped in three flavours of every size — base, instruct and reasoning — so a customer who wants deliberation picks a variant of the standard model instead of a different model. That is a pattern worth naming, because Mistral is not alone in it. A separate reasoning line made sense while the technique was new and expensive enough to be sold at a premium. Once it became a training stage that any model can be put through, keeping a parallel family meant maintaining two catalogues of the same sizes. What makes the case unusual is that switching a model off in the API did not switch it off in the world. Magistral Small 1.0, retired from the vendor's service on 30 November 2025, was downloaded roughly 80,700 times from Hugging Face in the thirty days to early September 2026 — more than six times the traffic of the final version 1.2, which is technically the better model. Devstral Small 2 makes the same point harder: withdrawn from the API on 31 March 2026, it pulled about 229,700 downloads in that same window, twelve times more than the 123-billion-parameter flagship it was released alongside. The counters cover thirty days only, and older releases have an advantage — tutorials, quantised forks and pipelines that were written once and still point at them. But the direction is clear enough. For a lab that publishes weights under Apache 2.0, retirement is a statement about what the company is willing to host, not about what people are running. That is why our profiles of Magistral Small 1.2 and Devstral Small 2, added today, are marked historical and at the same time describe how to run them — the service ended, the software did not.
Magistral Small 1.2 →Mistral retires Medium 3 in eight days — the replacement it names costs 3.75 times more per token
On 31 August 2026 Mistral AI switches off two models at once: Mistral Medium 3 (API name mistral-medium-2505, released May 2025) and Mistral Medium 3.1 (mistral-medium-2508, August 2025). Both were marked deprecated on 22 May 2026, and both are billed at $0.40 per million input tokens and $2.00 per million output. The company's documentation names a single replacement for them: Mistral Medium 3.5. That replacement is listed at $1.50 input and $7.50 output. The multiplier is the same in both directions — 3.75 times. A team that keeps its prompts, its volumes and its code exactly as they are, and does nothing but follow the migration path printed in Mistral's own table, will see its bill for this model line grow by 275% on the first of September. The part that is easy to miss is that the cheaper option is upwards, not sideways. Mistral Large 3, the company's larger and older model from December 2025, costs $0.50 input and $1.50 output — a third of Medium 3.5's input price and a fifth of its output price. On Mistral's public price list the ordering is inverted: the mid-tier model is the expensive one, and the model above it in the naming scheme is cheaper than both the model it sits above and the model it replaces. Below them, Mistral Small 4 runs at $0.15 and $0.60. The inversion is worth noting because of how Medium 3 was sold. Mistral launched it in May 2025 under the headline "Medium is the new large", with a pitch built almost entirely on price: roughly eight times cheaper than comparable models, deployable on four GPUs, at or above 90% of Claude Sonnet 3.7 on the company's own benchmarks. Fifteen months later the tier that was created to be the cheap one is the one that costs the most per token in the range. None of this makes Medium 3.5 a bad model — it is newer, and Mistral positions it as the stronger of the two. But the migration note in the documentation says only "use Mistral Medium 3.5 for new integrations", and says nothing about what that costs. Anyone still calling mistral-medium-2505 or mistral-medium-2508 has eight days to decide whether the named successor, the larger model or the smaller one is the right destination.
Mistral Medium 3 →Mistral built its name on freely licensed weights — its biggest one is still the model it published in April 2024
Mistral AI became a recognised name in September 2023 by publishing a 7.3-billion-parameter model under Apache 2.0 — the licence that lets anyone download, fine-tune and commercially resell the result without asking. Three months later, Mixtral 8x7B repeated the trick at a size that mattered: 46.7 billion parameters stored, 12.9 billion spent per token, benchmark scores at the level of Llama 2 70B and of the GPT-3.5 base model then serving free ChatGPT. In April 2024, Mixtral 8x22B scaled the same sparse design to 141 billion parameters, 39 billion of them active per token, with a 64,000-token window and native function calling. It, too, went out under Apache 2.0. That April 2024 release is still the largest model Mistral has ever opened. Everything above it changed terms: Mistral Large 2.1, from November 2024, shipped its weights under the Mistral Research License with a separate commercial licence sold on top, and the flagships that followed were never opened the same way. The company that made permissive licensing its signature has kept publishing open weights — but at the small and medium end, while the top of its range moved behind commercial terms. The practical consequence is that Apache 2.0 cannot be revoked. Mixtral 8x22B has disappeared from Mistral's own API line-up, which today lists Large 3, Medium 3.5, Small 4 and Ministral 3, and the company has never announced a shutdown date for it. That is not a problem for anyone relying on it: the weights sit on Hugging Face, third-party providers still serve them, and a licence granted under Apache 2.0 stays granted regardless of what the vendor's catalogue says. wujec.ai has now catalogued all three of these open releases.
Mixtral 8x22B →Mistral opens a 3B moderator that reads the rulebook at run time — not during training
Mistral AI released Shieldstral 1.0 on 4 August 2026: a 3-billion-parameter safety classifier published under Apache 2.0, the first moderation model the company has given away with open weights after two hosted APIs. The design choice is the story. Guard models usually ship with a fixed taxonomy of harms decided during training, so a platform whose rules differ from that taxonomy — or whose rules change — is stuck between retraining and mismatch. Shieldstral turns moderation into a single yes/no question asked at inference time: the developer supplies the context, a plain-language policy question and the content, and the model returns only the probabilities of "yes" and "no", normalised into a continuous score. Changing a policy means editing a prompt. The reported numbers put a small model against much larger ones. Mistral claims 88.1 F1 on WildGuardTest, 84.1 on ToxicChat and 99.4 on HarmBench prompt classification, plus 97.7 on the multimodal VLGuard set — matching or beating open guard models up to seven times its size, on the vendor's own evaluations. It covers twelve languages, judges images as well as text, and was trained at a 32k context. The practical figure is 16 GB. That is a single mid-range GPU, and with llama.cpp quantisation it drops to a CPU. A moderation layer that fits on one machine is available to operators who legally or commercially cannot ship user content to a third-party API — hospitals, public administration, anyone under strict data-residency rules. It is also, conveniently for Mistral, the argument European buyers have been asking for. One limitation worth stating plainly: every benchmark figure above comes from the company that built the model, and guard models are notoriously sensitive to how the policy is phrased. The weights are public, so independent numbers should follow.
Shieldstral 1.0 3B →