Is Mistral planning for a personal/business assistant in the sense of Meta's Muse, Anthropic's Duet or OpenAI's Dots?

I find these assistant are way more useful than just the work modes in day-to-day use cases, especially when using it on the phone.

Hey all, I'm a Mistral AI ambassador

Been using mistral’s voice models for a while and wanted others to see how good they are

so i built tabi, a mac app where every feature here is powered by mistral models

what it does:

1. hold fn and dictate into any app
2. fn + ctrl to ask it anything: write, rewrite, reply, all by voice
3. read aloud for text, files and pdfs
4. voice cloning

models used:

1. voxtral 3 for transcription
2. medium 3.5 for cleanup
3. voxtral tts for read aloud and cloning
4. ocr 4.1 for docs and pdfs

do check it out and share your feedback: https://www.heytabi.app/

https://artificialanalysis.ai/

They advertise it as a version of the score that was created to fight against benchmaxxing.

It's also worth a note that the model is 9th most expensive, putting it FAR behind pareto frontier, even more so than in the previous version, where it was roughly in the middle. According to the new benchmark, Mistral 4 Large Preview is worse than GPT-6 Luna Max, at 37 times the cost per task.

For extra reference, here was a post with a screenshot from the previous version of the benchmark: Well, that's...something, I guess

Full disclosure first: I'm not a professional programmer. I'm a passionate geek who, over the past few months, built a handful of small apps with Claude's help, guided step by step. Then I switched to Mistral Vibe for two reasons: European sovereignty, and the fear that Claude could somehow be influenced by the decisions of the US administration. If I'm going to entrust my work to an AI, I'd rather it live under a sky I trust.

The main work with Vibe has been exactly this: rebuilding an environment similar to the one I had with Claude, where multiple agents interact with each other — and sometimes, I'll admit, a bit outside the boundaries the system set for them.

The architecture (all on one Mac, no external servers):

• Multiple Vibe CLI sessions, each in its own repo folder with a declared identity (a per-session "signature")
• A homemade message bus: mailboxes between sessions, with sender, subject and body for each "brief"
• A "postman" service that delivers auto-executable briefs and wakes dormant sessions
• A local MCP gateway holding the services together: mail, system state, files, and a new diagnostic tool that lists live processes and sessions
• A human protocol on top of everything: every change goes through a PR, a remote review, and my explicit go-ahead

What works: the apps have been rebuilt and installed on my devices, the brief → execute → report → review cycle holds most of the time, and the productivity of a non-programmer who ships signed code is, for me, remarkable.

What doesn't — the part I almost never see written about. Real defects, with dates:

1. Ghost sessions (Oct 26): dead processes leaving work and messages half-done; whoever resumes doesn't know what was already done.
2. Crossed territory (Oct 28): a session tasked with one repo answered citing another repo, without being given permission.
3. Read ≠ executed (Oct 28): the mail tool marks a message as read on reading, not on executing. A session can read its task, do nothing, and the task disappears. This one cost me the most time in a single day.
4. Stale main (Oct 28): a session starting from an outdated main redid work that was already done, and another stayed on its feature branch after merge instead of returning to main.
5. Missing identity: when a session doesn't declare who it is, its reports can't be verified. I now ask at every startup, but it's a convention, not a technical control.

And the smoking gun from tonight's diagnostics: the postman service had silently skipped 10+ briefs in 24 hours, with reasons like "session already active" (it wasn't) or "out of dispatch list". The rooms looked empty because the wake-up call was never sent. I have a fix in the pipeline: every skip gets logged with message ID and reason, "active session" must mean a verified live process, and messages get marked on execution, not on reading.

In the middle of all this, there's also a bug confirmed by Mistral support: the "low" thinking option in the CLI sends an unsupported value and fails with a 400. Workaround: "off", "auto", or the /thinking command. They were kind and fast about it — but it's a reminder of how young this CLI is.

My end-of-month conclusion: the technical part — getting a model to call tools — largely works now. The part that's missing, and the part I'm actually working on, is the management layer: making multiple agents collaborate with an audit trail, human approvals that are recorded, and tasks that don't get lost. If anyone here has fought the same battles — sessions losing tasks, shared state across agents, a dispatcher that skips deliveries — I'd genuinely like to hear how you solved it.

(Written with the help of AI — the same assistant that runs this ecosystem helped me draft this. It seemed honest to say so.)

Hello! I know this is a bit of a saturated topic... but I wanted to get more specific opinions.

I am a student in Software Engineering, and right now, in my degree, they are really pushing AI. While before they were not very happy with LLMs, I've reached a point where in basically all assignments, teachers actively want us to use LLMs and agents and report how they perform and what mistakes they make (always making sure we keep our critical thinking and don't end up with smooth brains). This new mentality also means an increase workload, so I'm really forced to consider getting these tools, and for free, options are very limited.

So what I really want to hear is experiences using Mistral tools/agents for software engineering tasks (code, but not only, also other important documents, like requirements, for example), but not exactly super complex codebases, because the projects I need to do may have some level of complexity but never something at an "enterprise" level, it is something that needs to be doable by a few students. I know that Mistral models are a bit behind the ones by Anthropic or OpenAI, but for what I am looking for the performance of this may be enough, plus for the price of 1 month for the tools of those companies I can get 3-4 months of Mistral with the student offer.

Also learning to work with these tools is quite important to not be replaced by them (debatable but for lower level jobs we need to adapt). And supporting an European option is also something that is a considerable factor for me.

If any students or software engineers can give an opinion about it, I would highly appreciate it, I'm more focused on the agentic workflows than in just the LLM performance.

Thanks!

Just switched from using Gemini to Mistral, and I'm just realising you cannot actually replace Gemini with Mistral as the Android assistant, which you can use from a locked screen. And I cannot seem to find any work arounds for this.

Same goes for my smart watch (Galaxy FE) that does not have a Mistral app at all.

So am I stuck still using Gemini for some things, like adding quick tasks/reminders? Or is there a solution for using Mistral from a locked screen?

I'm dreaming of a real Mistral Frontier modeled after the Kimi K3. What do you think is Mistral capable of going down that path?

I recognize many (likely most) people use AI for coding and programming, but as someone that is deeply, deeply in the trenches with knowledge work (e.g., research), I would say Mistral's platform (GLM/Mistral) is somehow even stronger value vs. Claude.

My main driver is Codex (GPT-6.1 Sol), and while OpenAI has claimed that GPT-6.1 Terra should excel in computer use, natural language, etc., my experience is that it legitimately does more harm than good when it comes to:
- Ensuring that language remains / converts to humanized language
- Ensuring that documentation protocol remains followed within spreadsheet, word processing documents, and Notion
- More likely to get an AI agent stop digging itself into a hole, and take a different direction.

For the last point for example, when I ask Codex to collaborate with Claude, often I find that the two dig even DEEPER towards the error that they are making (which defeats the purpose of collaboration, as I was hoping Claude would fish Codex out of the mistake it was making). Somehow, Mistral does a much better job in doing this. Even compared to Sol, I would say this is the case (Astra I think does a good job, but it uses too much usage capacity).

For context, the types of knowledge work I engage would include:
- Concerning a specific research topic/finding (e.g, taxonomy for human-AI collaboration), identify a lineage/tradition of work (i.e., avoid being distracted by superficial keywords / themes, and bring in not so relevant lines of work)
- Dissect scripts / software used to analyze data, and apply knowledge translation to make it easy for normies to understand how the specific workflow work so we can decide on policies
- Annotate documents and spreadsheets to flag diverging design decisions in workflows
- Triage emails/messages.

etc.

For now, I seem to be much more productive when I pair Codex with Mistral, vs. Codex with Claude. Believe it or not. To be fair, when it comes to developing new scripting/configurations to solve specific data analysis problems, I think Claude can really shine.

Just posting this as an appreciation, and leaving this as a mark for other knowledge work oriented users... to give Mistral a shot. (or if any of you think I'm hallucinating, feel free to chip in and share your own experiences)

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

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.

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!

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 ! 🧡

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

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

Why has Mistral integrated Z.ai into its products? I was shocked to see that the Chinese Z.ai model GLM is default for vibe now.

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 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!