Hey everyone,

I've been using both Vibe CLI and Vibe Code (the web version) side by side for the past few weeks, and the difference in speed and reliability is honestly night and day, even though the output quality is basically the same when I use identical prompts.

What I'm seeing:

• CLI: fires off tool calls almost instantly, runs multi-step tasks without hiccups, rarely times out. Even long-running builds or multi-file edits just work.

• Web: same prompt, same model, but I'm getting noticeable latency on every step, occasional "connection lost" errors, tool calls that hang for 30+ seconds, and sometimes the whole session just dies mid-task and I have to start over.

It's not about the quality of the code generated — both produce the same result. It's purely about speed and stability. The CLI feels like a tool I can trust to get a job done end-to-end. The web version feels like I'm constantly babysitting it.

My setup:

• CLI: running locally on Windows 11, latest version

• Web: Chrome, same account, same model config

• Same prompts, same files, same tasks

Has anyone else noticed this? Is there a technical reason the CLI would be faster/more stable, local execution vs. server-side, streaming differences, connection overhead? Or is it just my setup?

Would love to hear if others are seeing the same thing, or if there's a config tweak for the web version I'm missing.

Thanks!

As the title, since 1017 is so great i'm sure most of us wanted to use that model as daily coding driver, hoping mistral devs heard this and deploy this model.

Hi!

I own a Pixel Watch 5, most smartwatches that run Android will give you Gemini as the default and only AI out of the box. I wanted to make my own native app because I don't want to use Gemini + by making my own app I could make something fun and good that goes beyond what's currently there.

Wearstral is open source and its code can be found on GitHub (https://github.com/Hiburger/Wearstral), it works well with my API. Hopefully you'll find it useful too. It's of course easy to use and privacy friendly.

Where it gets complicated is the installation, because I haven't published it on Play store yet, you'll have to be a bit techy and use adb. Mistral Vibe CLI helped a lot the whole time with the code :)

You can find screenshots on the GitHub repo mentioned above, and I obviously take feedbacks/questions ! There's also a roadmap there, when omong other things I plan to add support for other providers aswell.

Thank you ! 🧡

Will Mistral launch a family of Models along with version 4? (small, medium, large)

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

Mistrals docs say cached prompt tokens bill at 10% of input and that prompt_cache_key raises the odds of a hit but they dont give a ttl at all. ppl in here reported it going after a few minutes of quiet, one said about 3 and reads didnt seem to push that out. so one coffee break on a 20k token system prompt plus tools means the next call pays that whole prefix at full price again

that changes whether keep alive pings make sense. on apis where every hit resets the timer for free like anthropic works like that, a tiny ping every few minutes keeps it alive for the price of a cached read. if reads dont extend anything the pings just burn calls. the other route is paying up front for a longer window, anthropic does a 1h ttl at 2x the write price and deepinfra sells a 5 min or 1h retention on a couple of models like kimi k2.7 code, priced as a write premium that you pay again each time you extend it

for a session where the user replies every 10 to 20 min a paid 1h window usually beats rewriting the prefix 3 or 4 times and for quick back and forth chats the default cache is enough and the premium is wasted. someone already asked the mistral team for a retention option in that thread so maybe it lands at some point

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.

When building an application with AI, choosing a model is only part of the decision. You also have to think about how it fits into your existing setup, especially when you want to use models from more than one provider.

For example, using Mistral for one task and another model for a different task can mean dealing with separate API configurations, usage limits, and billing. At the same time, integrating directly with a provider can give you more control over its specific features.

For those using Mistral in their projects, do you prefer integrating directly with Mistral's API, or using a unified API when working with multiple providers? What trade-offs have mattered most in your experience?

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!