It took me way too long to see this. Am I the only one that was slow on seeing this? 😄

Hi guys I'm waiting for Mistral Large 4 Le Chonk in my Mistral web app -> when? is there anybody who knows that date?

It looks like Mistral is testing voice live conversations on the Vibe app 👏👏👏

It’s looking good. It’s still a bit slow to answer. And the English voice is the same voice they were using for the dictation feature, which speaks really slow and makes it feel unnatural.

Hi everyone! When Timestral came out I wanted to try it on something personal, so I built Daycast: a small Windows app that records which apps and websites you use and forecasts your day hour by hour, like a weather forecast for your screen time.

How Timestral is used

• Timestral-5M runs fully offline on CPU. The 21 MB weights are bundled in the app, so nothing is downloaded and nothing leaves your PC.
• Once a day a separate low-priority process loads the model and forecasts the rest of the week. It covers total PC usage and every app or site used for at least 10 minutes, plus daily totals, with the p10–p90 range from Timestral's quantiles.
• A 24-hour forecast takes ~20 ms, and the whole daily run a few seconds. The background logger never loads PyTorch and stays at ~50 MB of RAM.

The part I found most interesting: it has to earn its place

Every day Daycast re-forecasts the last 7 days with both Timestral and a simple baseline (average of the same weekdays), each using only the days before it. It then shows whichever had the lower error on your data.

On my synthetic test data (a very regular week repeated) the baseline actually wins: about 3.6 vs 4.5 minutes of error per hour. That makes sense, since a perfectly repeating week is the best case for a plain average. I'm curious to see what happens on real, messier usage over a few weeks.

Other details

• Websites are recognised from the address bar domain (only the domain is stored, never full URLs or page titles).
• Categories are yours to define. The interface is in English and Italian.
• Open source (MIT); Timestral weights under Mistral's Modified MIT license, with the attribution included.

Honest caveats

• The zip is ~185 MB, almost all PyTorch.
• The exe isn't signed, so Windows SmartScreen will warn you.
• Windows only, for now.

Next: mobile

My real goal is to bring Daycast to phones, with the forecast running on the device so usage data never leaves it. On Windows I'm happy with PyTorch, but on a phone that's not an option, so I'm looking at ExecuTorch, ONNX Runtime Mobile or Core ML.

Questions for people who know Timestral better than me

1. Has anyone run Timestral on-device on Android or iOS? Did export (ONNX / ExecuTorch / Core ML) work out of the box, or did parts like the attention masks or RoPE need changes?
2. How well does Timestral-5M hold up when quantized to int8 or fp16? At 21 MB in fp32 it would be tiny on a phone.
3. Is Mistral planning official ONNX or mobile-friendly exports of the Timestral checkpoints?
4. On a phone you start with only a few days of history. Any advice on getting good zero-shot forecasts from very short hourly contexts?

GitHub: https://github.com/Fede046/Daycast
Download: https://github.com/Fede046/Daycast/releases/latest

Feedback very welcome!

Hi everyone!

I've been experimenting with Timestral-28M in a real-world application: predicting laptop battery discharge and charging times.

The result is Battery Forecast, a Windows port of Matteo Bonfanti's Till Dusk.

The app collects battery readings locally, uses historical observations as context for Timestral, and generates a 24-hour forecast.

It estimates when the battery will reach a reserve threshold or a charging target like 80%.

Everything runs offline on the CPU, without retraining the model or uploading data.

The application also includes a Windows tray interface, battery history, configurable alerts and forecast charts.

Current limitations: Accuracy hasn't been systematically validated yet, and charging forecasts remain experimental.

GitHub: https://github.com/Mattboxx/battery-forecast

Website: https://mattboxx.github.io/battery-forecast/

I'd love feedback from anyone experimenting with Timestral, particularly on forecasting from irregular real-world sensor data!

I live in Europe and want to divest as much from US tech as possible, and although I use ChatGPT Plus and Claude Pro, I tried Mistral about 3 months ago and it was terrible. I wanted to love it but just couldn't.

For context, tech is a hobby and I have been building my homelab with a Synology NAS, Fujitsu Mini PC running Linux and about 80 docker containers, personal website and a couple of VM's, using AI to help build the systems, security, network etc.

Last week I decided to give Mistral another try and OMG!!!! WOW, what a difference!

I gave it read only access to some of my system (using Think mode and GLM 5.3) and it discovered my NAS username and password were exposed in some document on Github which both Claude and ChatGPT hadn't picked up despite security audits.

It's reasoning is now brilliant, speed of response amazing and cost is insanely cheap compared to ChatGPT (with the stupid 5 hour allowance which has made it unusable for me).

I've also now plugged it into my self hosted Open WebUI instance so it has read-only access to a large part of my system, for diagnostics, which makes a huge difference. Previously, I would have to upload a markdown file of my Wiki.js, which is my homelab manual and do that every time I updated the Wiki. But now it has access to the live Wiki, and eventually I would like to give it write access to update the Wiki, but not yet!!

This week I have used Mistral to build various monitoring systems, research agents and audit my system, my website, and bouncing ideas off it and then cross-referencing with ChatGPT.

I've only used €17 of my €255 allowance (included in the Pro plan)!

In comparison, ChatGPT Work has been using my 5 hour allowance after 3-4 prompts recently, when building something, so I have not been able to complete a project during the same session.

In addition to that, and as a bonus, I discovered Mistral Studio, where I recorded a 30 second voice message and then used it in Studio's Text to Speech option and it was frigging amazing!! It read different texts in my voice and was very, very close to sounding just like me!

The future looks bright for Mistral and things are moving in the right direction.

Thank you!! Merci!!

Im wondering which one of these is better for agentic tasks, running the base agent with (and calling CLIs with more powerful models when needed for complex planning and coding etc), doing more basic but multi step tasks like gathering info from web, processing it and creating report etc. and also for basic chatting with the agent.

Im sure some of you have tested these head to head, and i wonder if there is clear all around winner between them, or perhaps some specific stuff done better on one and better on other?

(tried using search, but apparently its too complex query for reddit search to find anything meaningful, as i would assume someone has asked this before)

Sur les derniers modèles Pixel, iPhone ou Samsung, on a déjà assez de puissance pour faire tourner des modèles locaux entre 0.8-3B voir du 7B de manière fluide et avec des outils de base. C'est ce que propose notamment Off Grid AI à la fois sur Android et iOS mais il y en a d'autres, y compris open source et gratuit.

Ce serait bien que Mistral, qui a déjà ouvert ses services à des modèles tierces, nous offre la possibilité d'utiliser son application y compris hors-ligne, via le téléchargement de modèles locaux et compatibles.

Je pense que ça ferait venir tout un tas de monde axés sur le local-first, et sensibles aux données personnels tout en profitant de l'interface mobile de Mistral qui est plutot classe.

Des avis là dessus ?

Been requested to do a small presentation on interesting use cases with some examples. Nothing too crazy but more of a showcase to people who aren't technical and want to make the most out of AI beyond email writing.

We use mistral at work for our in-house chat bot as we're not allowed to use the publicly available ones. It's not advertised widely for our workflows just yet but slowly being rolled in. We're an engineering company, so we work with a lot of technical documents and our bot allows us to upload them. My team also consists of software devs, engineers and project managers. That's all the context I can share. Cheers!

Hi all, I have an interview coming soon for the Applied AI Engineer, Fullstack Software Engineer. Did anybody go through it recently?

From what I’ve found online, it seems to be either:

• Mix of code review, an LLM/RAG quiz, PyTorch/algorithm coding and a project discussion
• Frontend live coding, agentic programming, system design

Any general tips, experiences to share, how to prep?

Thanks in advance!

Direction first: on this benchmark #1 = sloppiest and #58 is the cleanest, so a high rank number is the good end.

I run a thing called The Slop Index that scores how much AI writing reads like "AI slop" (58 models, ~64k outputs across 112 short-form scenarios). Two Mistral models stood out to me because they sit at nearly opposite ends.

Every rank below is a mechanical rank (the mech score only): padding, templating, sentence rhythm, and the usual AI tells. That score only moves on a roster refresh, so it is the stable one to quote.

• Mistral Large: mechanical rank 1 of 58, the sloppiest model in the set right now. By category it is mech rank 1 on Slack, 2 on social posts, and 1 on essays.
• Mistral Large 4: mechanical rank 57 of 58, second cleanest overall, and it sits at the clean end across email (54), Slack (56), social (51) and essays (58).

These are two separate releases, not one model I watched change, so I am not claiming anyone patched anything. They just score very differently.

Mistral Large 4 only has 10 crowd votes so far, so its combined score (which blends daily crowd votes) sits mid-board and is not meaningful yet. That is why I am quoting mechanical ranks here.

Mechanical ranking card: https://theslopindex.com/mechanical-card.png

Fair limitation: this is short-form business writing only (email, Slack, social, essay), no fiction yet, and the mechanical axes can miss repeated sentence constructions.

Leaderboard: https://theslopindex.com/leaderboard Methodology: https://theslopindex.com/methodology Open dataset: https://huggingface.co/datasets/hgaddipati/slop-index You can play a few rounds of the blind arena here: https://theslopindex.com/arena

For context, I'm one of the people behind https://slashy.com, an email client that writes drafts that sound like you. This benchmark started as how we pick models.

It basically shows what might go wrong with the product you are looking for, quotes everything and weights it. It is open source and supports Mistral Vibe CLI.

I'd like to hear your opinions about this.

Kriko Website

Github Repo

Edit: I forgot to add links.

I hear a lot about Mistral's AI safety strategy being inadequate... what do you guys think of that? I mean they scored worse than Grok on the AI safety index!

Hi,
I use Opencode and I am considering the Mistral Pro plan.

Be fire subscribing, I would like to understand if the official Vibe CLI share the same token pool as the $25.5/mo API credits or does the CLI have a separate coding quota while the $25.5 credits are dedicated to external API calls?

I’m quite confused by the website, thanks

I'm working on PII, financials and client's information and need somewhere safe to do data manipulation. Working in Australia financials sector with complaints and harsh privacy protection. Need AI agent to work on pdf and excel files. Is Mistral Pro subscription a good fit for purpose? Thanks

Hello everyone I just wanted to post my perspective on Mistrals services.

Half an year ago I had a quick test with Mistal Medium3.5 and while doing OK I was a bit disappointed by the results.

I am generally not a heavy AI user so I continued to use opencode's free models for some things.

A month ago I decided to try opencode's go subscription. That was OK but if anyone knows how opencode go works, it basically has plan changes daily. It almost forces you have social media open the whole time or you might miss some change - cheap models come and go daily...their allocations change etc.

If you go their subs, they are full of complaints that they make the service unusable - getting rated all the time, quantised models to the point of being stupid etc.

Then they had two security breaches..

In that last two weeks I have been using Mistal with GLM5.3 exclusively and I feel like I discovered some hidden gem.

The vide sub gives so much credits 17€ for a whopping 225€ of token credits. It feels like it's unlimited!

Here is what I got done:

- migrate mit smallish app from flutter to react native. Full stack migration!

- write and drive automated ui tests

- make promo material, screenshots with cool marketing slogans etc

- remade my website

In total the admin console shows: 57€.63 from 255€ spent!

Now I read that they are deploying a Flash model with GLM5.3 flash!

Needless to say I'll extend my subscription for a whole year!

Thank you Mistral for the great offer! Being European is just the great side bonus!

It would be nice to have an option between Pro and Team. I don't need higher limits, more storage etc. Only the sharing of Organizations and Workspaces between members. The price jump from Pro to Team is not justifiable for a family application.