OK, I'm 100% rooting for both Mistral and task focused small models.
But Mistral has fall really far behind since 2025Q3. It seems they can't get good reasoning models working at even medium context sizes, which is necessary to be at the table right now.
Gemma4 and Qwen3.6 are currently best in the small size; Mistral's "small" model has ~4x the parameter count at 120B and isn't even competing with models a quarter its size.
Back one year ago with Mistral Small 3.1 they were keeping up, but they've fallen into irrelevancy right now.
If Mistral seriously wants to play the on-prem and small task-specific model game, a decent proxy would be to build models that get the r/localLlama crowd excited
> BNP Paribas runs Mistral models on-prem for KYC in Belgium, with sensitive data staying within the bank's walls. Abanca is using agent orchestration to handle sensitive customer information at a huge scale (2 million customers in their app). For European companies in regulated industries, this is a good alternative to relying on US hyperscalers.
Mistral leaning into on-prem and European-hosted models is very smart.
That's just one side of the story, not following it on details, but their own le chat explained to me that the company was a capitalist succubus starving to build data center in some north European country. Hilarious if you ask me.
When the humans have a track record of corruption, it might make sense for a company to seek parallel opinions from a LLM so they can at least flag suspicious human decisions.
Assuming BNP Paribas leadership wants to stop the corruption of course.
> Abanca is using agent orchestration to handle sensitive customer information at a huge scale (2 million customers in their app).
Maybe my perspective is skewed on what "huge scale" means, but 2 million users? That's like a few hundred megabytes of data? Or a couple GBs if there's a lot of per-user data?
While multi-national Big Tech projects and the startups that (naively) envision themselves joining those ranks might think in orders-of-magnitude greater scale, most teams aren't facing nearly that many "customers".
They're working in smaller markets scoped by region, industry, enterprise customization, growth reality (vs fantasy), etc and 2 million really is legitimately a lot. In this world, demonstrating success at that scale is very meaningful.
European consumer focused businesses do not scale easily the same way US ones do, which is a major contributor to their problems developing tech businesses generally.
OTOH such things can be quite defensible, they just rarely become anything like as profitable.
Maybe, but using state-of-the-art large language models to solve customer support queries with agentic can quickly use a lot of tokens. What I understood from the talk is that they used agents with limited responsibility and (assumption from me) smaller models, to the make sure the answers were quick, reliable and not too costly.
There are several payments processing companies that are already largely using AI for customer support queries. They still have an escape hatch to a human but at least one of those companies (on the smaller side) is reporting a ~99% success rate, they are down to a handful of human customer service employees now for cases where the customer can't find/produce the transaction ID.
I was at the event, and was impressed by the attendance, all the leaders from the major european listed companies were there.
Also interesting to note the number of partners they invited. Going from Microsoft, Accenture and EY to startups like alpic.ai or lingo.dev . Seems like they are ramping up their M&A game too
OK, I'm 100% rooting for both Mistral and task focused small models.
But Mistral has fall really far behind since 2025Q3. It seems they can't get good reasoning models working at even medium context sizes, which is necessary to be at the table right now.
Gemma4 and Qwen3.6 are currently best in the small size; Mistral's "small" model has ~4x the parameter count at 120B and isn't even competing with models a quarter its size.
Back one year ago with Mistral Small 3.1 they were keeping up, but they've fallen into irrelevancy right now.
If Mistral seriously wants to play the on-prem and small task-specific model game, a decent proxy would be to build models that get the r/localLlama crowd excited
Nobody trying to compete with Google, OpenAI, and Anthropic should be playing the small models / local models game.
Foundation model labs should be building very large reasoning models, then leaving it to the community to distill them down.
You can't scale a small model up, but you can scale a small model down.
> BNP Paribas runs Mistral models on-prem for KYC in Belgium, with sensitive data staying within the bank's walls. Abanca is using agent orchestration to handle sensitive customer information at a huge scale (2 million customers in their app). For European companies in regulated industries, this is a good alternative to relying on US hyperscalers.
Mistral leaning into on-prem and European-hosted models is very smart.
Also Mistral did just the right thing by acquiring Koyeb, to beef up their deployment at scale expertise.
Yeah but why use mistral on premises instead of Qwen?
One reason might be that Mistral doesn't have a risk of weird training biases that were required by the Chinese government.
That's just one side of the story, not following it on details, but their own le chat explained to me that the company was a capitalist succubus starving to build data center in some north European country. Hilarious if you ask me.
Lets hope the models can do a better KYC than the humans have been doing..because they are well known.
Or is this a case of the humans, now preparing for the excuse it was the AI failure?
"BNP Paribas Sentenced for Conspiring to Violate the Trading with the Enemy Act" - https://www.justice.gov/archives/opa/pr/bnp-paribas-sentence...
"BNP Paribas caught up in French money laundering investigation" - https://www.reuters.com/business/finance/bnp-paribas-caught-...
"BNP Paribas faces $246m fine in currency scandal" - https://www.bbc.com/news/business-40635070
"BNP Paribas caught in a Cypriot money laundering investigation" - https://www.lemonde.fr/en/les-decodeurs/article/2023/12/26/b...
In Money Laundering their track record is unmatched: https://violationtracker.goodjobsfirst.org/parent/bnp-pariba...
When the humans have a track record of corruption, it might make sense for a company to seek parallel opinions from a LLM so they can at least flag suspicious human decisions.
Assuming BNP Paribas leadership wants to stop the corruption of course.
> Abanca is using agent orchestration to handle sensitive customer information at a huge scale (2 million customers in their app).
Maybe my perspective is skewed on what "huge scale" means, but 2 million users? That's like a few hundred megabytes of data? Or a couple GBs if there's a lot of per-user data?
Your perspective is skewed.
While multi-national Big Tech projects and the startups that (naively) envision themselves joining those ranks might think in orders-of-magnitude greater scale, most teams aren't facing nearly that many "customers".
They're working in smaller markets scoped by region, industry, enterprise customization, growth reality (vs fantasy), etc and 2 million really is legitimately a lot. In this world, demonstrating success at that scale is very meaningful.
European consumer focused businesses do not scale easily the same way US ones do, which is a major contributor to their problems developing tech businesses generally.
OTOH such things can be quite defensible, they just rarely become anything like as profitable.
Maybe, but using state-of-the-art large language models to solve customer support queries with agentic can quickly use a lot of tokens. What I understood from the talk is that they used agents with limited responsibility and (assumption from me) smaller models, to the make sure the answers were quick, reliable and not too costly.
There are several payments processing companies that are already largely using AI for customer support queries. They still have an escape hatch to a human but at least one of those companies (on the smaller side) is reporting a ~99% success rate, they are down to a handful of human customer service employees now for cases where the customer can't find/produce the transaction ID.
I was at the event, and was impressed by the attendance, all the leaders from the major european listed companies were there.
Also interesting to note the number of partners they invited. Going from Microsoft, Accenture and EY to startups like alpic.ai or lingo.dev . Seems like they are ramping up their M&A game too
As an European: 100x YES!
I really like the direction and the transparency of Mistral, among those players.