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points by Topfi 13 hours ago | 0 comments

They slashed pricing, it's now nearly half vs the preview (Input: $0.7/M Tokens, Output: $2.1/M Tokens vs Input: $1.36/M Tokens, Output: $4.18/M Tokens).

At the original pricing in my evals, Mistral Large 4 Preview was not competitive. With this price point and some improvements on top, this full release warrants a major reassessment.

[0] https://docs.mistral.ai/inference/pricing

[–] almindor 13 hours ago

They didn't. Original price was maybe higher but they slashed it during preview and in fact it was lower by a bit ($0.68/M tokens) and $2.09 for output.

I've been using it for my personal project and it's still too expensive and very slow when thinking. I think we need one more generation from mistral and with maybe 50% of this already "slashed" price for this to be competitive (I use Opus 5.5 at work and it's still much better than this).

[–] Topfi 12 hours ago

I just checked, seems you are right. They had roughly a week with the lower pricing, then increased it, now decrease it again. Very odd. My testing was with the higher pricing and looking at AA, theirs seems to have been too.

[–] derefr 6 hours ago

Is it odd? I would guess that they're doing cost-plus pricing and the costs have been fluctuating with model revs.

[–] ismailmaj 13 hours ago

Given the size of the model, that's very aggressive pricing.

I wonder if it is serving at a loss to get training feedback (maybe using the margins they get from GLM 5.3 serving which is a slightly smaller model but double the price).

[–] oh_no 11 hours ago

based on Anthropic's S1 no one is serving models at a loss, but cutting your price to keep usage high makes a lot of sense when you have your inference booked for long periods.

[–] traceroute66 10 hours ago

> They slashed pricing, it's now nearly half vs the preview (Input: $0.7/M Tokens, Output: $2.1/M Tokens vs Input: $1.36/M Tokens, Output: $4.18/M Tokens).

Those in the EU wanting EU-only inference should be aware Mistral put a 10% tax ("upcharge") on Regional Inference[1].

[1] https://docs.mistral.ai/inference/regional-inference

[–] oh_no 11 hours ago

> this full release warrants a major reassessment.

I really don't think it does, the benchmark numbers are dreadful and the cost is higher than Sol. If you have to use an EU model knock yourself out but there are a ton of models that are a lot smarter for a lot less money.

[–] jacquesm 11 hours ago

You mean they didn't benchmaxx it? That's an extra reason to give it a try.

[–] killingtime74 5 hours ago

So if scores are good, they must be benchmaxxed, and if they are bad, they must not be benchmaxxed. So basically, the narrative is whatever you want it, no matter what the data says.

[–] jacquesm 4 hours ago

No, if a model scores good on benchmarks it could be that they have benchmaxxed, if a modern model scores bad on benchmarks it could be a really bad model but it almost certainly isn't benchmaxxed. It's relatively easy to make a model do well on particular benchmarks, companies that don't care about that stand a better chance at making a model that does well in general because tweaking a model for benchmarks usually is at the expense of something else.

I run my own benchmarks (this model is - unfortunately too large so that won't be happening here) on data that no outsider has ever seen and this gives sometimes results that are not in line with the order that you would expect if you took the various benchmark sites into account. It is of course entirely possible that our benchmark is not representative but it seems to work quite well as a predictor for which models have value for us and which do not. Recently we looked a model that has been pushed with a significant amount of hype and for the life of me we could not get it to perform even close to the published benchmarks, whereas the models we used for controls were definitely within the margin of error. It's not always equally cut-and-dry.

[–] taneq 8 hours ago

“You know how Einstein got bad grades? Well MINE are even WORSE!” - Calvin, to Hobbes

[–] miroljub 13 hours ago

Why does everyone avoid publishing the most important part of the pricing while doing a comparison?

My coding sessions have a >97% input cache rate. DeepSeek got us spoiled. I am not going to use any AI provider with exorbitant or hidden cache rates.

[–] Topfi 13 hours ago

They did, I just didn't include it: $0.07 cached input / M

So 10x-20x vs Deepseek v4.1 Flash and I don't have on hand how efficient Mistral is regarding cache hits, etc.

On the other hand, could be some decent competition lowering pricing if the models general performance warrants broad hosting.

[–] esafak 13 hours ago

Look at the Intelligence Index vs. Cost per Task. Mistral 4 is not competitive. https://artificialanalysis.ai/#intelligence-efficiency-tabs

[–] Systemerror7A69 12 hours ago

Irregardless of wether or not Mistral Large 4 is competitive or not, I do not trust the AA numbers anymore, for various reasons. Them changing their numbers around so much recently brings the entire validity of this in question, not to mention if such a combined number is even important at all. The combined "intelligence" score of AA includes areas I do not care about or need at all.

[–] anon373839 5 hours ago

Agree. They had just fiddled with the formula a few weeks prior to this, too. There seems to be an effort to get a certain result from the model rankings.

[–] yako21000 13 hours ago

seems better now, but still, it will be hard to compete. It's just that a direct comparison to frontier models will not speak to Mistrals favor.