Just that simple. I feel like Mistral is using a different model this week through the work section. It just gives really comprehensive answers, whereas in the past it was way simpler, but also it's using more tools. Furthermer, I haven't spotted any mistakes like I've been getting in the past. Are you experiencing the same, or is it just me?

I've been using Mistral for hobby projects, and I'm amazed at how much you can accomplish for free.

There are quotas, of course, but they appear to be quite generous in terms of development and experimentation. You can test the API, perform OCR on documents, experiment with classification or extraction workflows, and prototype features without having to worry about costs.

I wouldn't use the free tier for a production app, but it's perfect for learning and building side projects.

What do you use Mistral for, and have you already reached the free-tier limits?

GLM-5.2 is now available in Vibe Code for Pro, Team and Enterprise users.

• Hosted and served by Mistral AI in the EU.
• Generous usage limits.
• We see it as particularly strong on long-horizon agentic coding tasks.
• Available with up to 800k tokens of context in Vibe Code.

Install the Vibe CLI and try it out
https://docs.mistral.ai/getting-started/quickstarts/vibe-code/install-cli#step-1

Developers - let us know what you think.

PowerPoint files creation in Vibe is still not available.
And the presentation generation capabilities of Vibe are still very basic even in Work mode.
It would be nice to improve this as it’s a feature very much used in companies in Claude.
What are your opinions on this issue ?

Hi all so just curious what is the benefits of mistral pro vs something like opencode go ? Saw glm 5.2 was released which makes me curious if its worth it also zdr policy?

Hardware:

i5-7400

b250 mining expert

16gb ddr4

3x 1000w psu

2x p6000 24gb = 48gb

2x p4000 8gb = 16gb

2x gtx1080 8gb = 16gb

1x p100 12gb = 12gb

Total vram = 92gb

Been experimenting with a lot of models.

Here is the mistral family stats.

Mistral Medium 3.5: iq4_nl

prefill: 150t/s >>> down to 30 t/s beyond 60k context (limit is 90k otherwise end up in OOM)

decode: ~3t/s always consistent

Mistral Small 4: ud-iq4_nl

prefill: 700t/s >>> down to 400 t/s beyond 100k

decode: ~20t/s always consistent

Hi everyone,

How should I understand Mistral Ai's free plan?

I'm new to the world of AI—can someone explain this to me in detail?

How many messages or image generations do I get for free with the free plan?

Thanks 😄👍

On est le 01 septembre. Ça me rend fou de voir que c'est toujours pas réglé cette histoire. 🫪

I felt Le Chat different today. The behavior was quite similar to GLM 5.2, as I have been using it in Vibe CLI. When I asked what model was being used, it confirms it's GLM 5.2. I tried on various conversations.

Anyone else noticed?

the new GLM 5.3? kimi? a new in-house model? or all of the above?

would also be nice to get a response on this matter from the mistral team.

Thank you all.

Como muchos ya sabemos uno de los fundadores de Mistral anunció un próximo modelo de IA para finales de verano, pero el verano se está acabando y no les queda mucho tiempo. Sinceramente para estar el pruebas avanzadas , como ellos dijeron, están tardando mucho en dar aunque sea el numero de parámetros o el nombre.

He estado mirando fechas y eventos destacable de IA y el más próximo se el “IA Summer” el 22 de septiembre , si lo piensas ente que llegan de vacaciones (sé que no se ha ido toda la plantilla) y hacen algúna prueba más y hacen algún anuncio, la fecha me podría cuadrar más o menos.

Mistral lleva unos meses algo callada en cuestión de LLMs más tradicional. sinceramente espero que puedan superar los 55 puntos de Artificial Análisis,ya no solo por capacidades sino por prestigio de la startup y del la UE.

Ahora mismo les tengo aun algo de fe en el desarrollo de modelos internos, solo espero que no se queden con alquilar sus centros de datos, ser una especie de gestoría de IA y algo al estilo Palantir (que no entiendo que están haciendo con una oficina en Kiev si no es para es).

Example :

•  Steps: 32
•  Session Prompt Tokens: 1,598,353 (including 255,936 cached) -- 33% cache hit
•  Session Completion Tokens: 20,287
•  Session Total LLM Tokens: 1,618,640
•  Last Turn Tokens: 79,635
•  Cost: $2.3270

I ran prompt accross different tools, Pi, Mistral CLI and it gives me ludicrous amount. 120K token inputs resulted in 6E use !!

The cache is called half the time. I don't get it...

I'm considering subscribing to a Mistral Vibe coding plan mainly to try out GLM 5.2 (the Z.ai model Mistral now hosts). Before I pay, I want to confirm how it's actually exposed to subscribers.From Mistral's own docs, GLM 5.2 is officially listed as an available model (1M context window, tagged as an open third-party model). But I've also seen a blog post claiming you have to manually add entries to get it work.

So my questions for anyone who's actually used it: does GLM 5.2 show up automatically in Vibe's model picker/list after subscribing, or do you really need to hand-edit the config file?
Is it available on all paid plans, or gated behind a specific tier?
Has anyone noticed inconsistent performance/latency with GLM 5.2 specifically (vs Mistral's own models).

Trying to figure out if it's a "just works" experience or something that needs tinkering before it's usable. Thanks!

Today, this model suddenly stopped working because it is not supported by my plan. But I used the Large-Latest for 9 months on the free plan and everything was fine because I used not many tokens. Is it really impossible to use this model for free anymore?

EDIT: it has been thinking all along the way, but the reasoning traces was hidden. Had to set reasoning_field_name = “reasoning” under [[providers]] to make it visible. However, I can not turn thinking of :)

EDIT2: Set api_style = “openai-responses” and you can turn thinking on and off. Consider the problem solved

Hi all,

I am trying to get vibe cli to reason/think when it uses my local ollama with qwen3.5 9b as model.

My setup works fine, but changing ”thinking” does not seem to impact whether the model reasons or not (It doesn’t). The model has an enable_thinking that can be true or false. Can also be triggered via reasoning_effort (works when I call the model vi curl). But just can’t get vibe cli to pass on the thinking setting to the model…

I see no thinking traces in vibe cli (which I do for the online mistral medium), and the ollama server logs says thinking is off.

Hope you can help me!

ps: running on a second-hand laptop with an Quadro Rtx 5000 max-q card with 16 gb vram. These machines are cheaper than an 5060 ti card, but here you get an entire computer for the money.

I built a free AI coding cost calculator - would love some feedback

I’ve been using AI coding tools more and more, and it can be surprisingly hard to figure out what they’re actually costing once you start comparing models, token usage, and different pricing.

So I built a simple AI Coding Cost Calculator:

https://instacodingcost.com/

The idea is to make it easier to estimate and compare costs before you burn through credits/tokens.

Would genuinely appreciate feedback from people using tools like Claude Code, Codex, Cursor, etc.

What would make this more useful for you?

Anything missing, confusing, or calculated differently than you’d expect?

Feel free to roast it too - that’s probably more useful than “looks good” 😅

I added two custom MCP connectors a while ago, it works great.

But when I try to add another, I can't get mistral to enable the "Create" button. So I can't add it. Is there a limitation on the number of custom MCP connectors on the free tier or something?

The ones I'm trying to add are:
https://app.usevist.dev/mcp

and

https://mcp.mnemoverse.com/mcp

Can anyone else add these as custom MCP connectors?

I would love to hear the opinions of the community about this kind of use cases.

First of all, that this content is being created is great. It helps bring the product, the tech, the company... closer to us the users.

As I see it, Mistral's strategy is focused on enterprises and I understand that big clients have team members dedicated to helping with the implementation, but there are smaller companies or even freelancers that would like to use their tools as well.

I believe that this kind of content but more focused on using the tools to build more business focused workflows or applications, would resonate better with some of the people following AI, Mistral, European competitiveness. The people that usually participate in this community basically.

Always based on solid docs, of course.

Hope we can generate some ideas.

I don't know if this is common knowledge but Mistral’s Education plan is not the actual Pro plan, despite their website explicitly claiming, "The Education plan is the Pro plan with a discount."

Instead of giving you the standard Pro limits ($30 API / $300 Vibe), they quietly scale the quotas down to match the discounted price (~€12.75 API / €127.50 Vibe).

Support admitted the wording is "confusing".

Honestly, the reduced limits are enough for my personal use, but the marketing is still highly misleading and doesn't match the advertised terms. It explicitly says 30$ api tokens for the education plan on the price list.

Just don't buy it expecting the full Pro quotas!

It seems like Mistral is starting the migration from chat to work mode.
It’s already suggesting the conversion of agents to skills.
Did notice this ?

Been heads-down on something that sits before the LLM call instead of doing

standard retrieve-and-stuff RAG. Instead of chunk retrieval + rerank, it builds

a vector-field representation of the whole corpus, evaluates relational

relevance to the query, and collapses the candidate field down to a compact

evidence state — only that gets forwarded to the model.

On my internal benchmark (frozen 20-query set, project-native corpus) I'm

seeing an order-of-magnitude drop in tokens sent to the model with zero

measured quality regression (good/partial/poor scoring, OFF vs ON, reproduced

run matched the historical one exactly). Also runs fine single-threaded — did

a raw C++ core benchmark, 10M samples in ~140ms on an old 2015 i7, so the

underlying op isn't the bottleneck.

Haven't benchmarked it against BM25 or plain cosine-similarity RAG yet in

anything I'd call rigorous — that's the obvious next step before I'd trust my

own numbers fully, and I know that's the first thing this sub will (rightly)

ask about.

Running as local-first — full corpus stays on the user's side, only the

selected evidence chunk(s) + field-topology coordinates go to the external

model if you're using an API-based LLM. Wasn't originally optimizing for that,

but it's a nice side effect for anyone paranoid about what leaves their

environment in API workflows.

Genuinely asking: is "context collapse before inference" different enough

from what rerankers / good chunking already do, or am I just describing a

fancier reranker with extra steps? Wouldn't mind being told I'm wrong here.