Mistral Medium 3.5

Post by phamilton » Thu, Apr 30, 2026, 1:40 AM UTC

pbgcp2026 wrote: post #206

zozbot234 wrote: post #200

Earlier quoted context omitted.

Recent models support multi-token prediction, which can guess multiple future tokens in a single decode step (using some subset of the model itself, not a separate drafting model) and then verify them all at once. It's an emerging feature still (not widely supported) and it's only useful for speeding up highly predictable token runs, but it's one way to do better in practice than the common-sense theoretical limit mi…

It seems to me it's only Grok 4.20 that does this currently? Which other models did you have in mind, if I may ask?

Post by DANmode » Thu, Apr 30, 2026, 2:03 AM UTC

Aurornis wrote: post #160

zozbot234 wrote: post #143

Earlier quoted context omitted.

> Cloud hardware can run the original model. Quantization will reduce quality. New models are often being released in quantized format to begin with. This is true of both Kimi and the new DeepSeek V4 series. There is no "original model", the model is generated using Quantization Aware Training (QAT).

> There is no "original model", the model is generated using Quantization Aware Training (QAT). The original model is the model used for the benchmarks People will say "You can run it locally!" then show the benchmarks of the original model, but what they really mean is that you can run a heavily quantized adaptation of the model which has difference performance characteristics.

Post by tredre3 » Thu, Apr 30, 2026, 3:16 AM UTC

simjnd wrote: post #122

Aurornis wrote: post #82

Earlier quoted context omitted.

> This model? You can run it at Q4 with 70GB of VRAM. This is approaching consumer level territory (you can get a Mac Studio with 128GB of RAM for ~3500 USD). The one thing I would want everyone curious about local LLMs to know is that being able to run a model and being able to run a model fast are two very different thresholds. You can get these models to run on a 128GB Mac, but we need to first tell if Q4 retains…

> The one thing I would want everyone curious about local LLMs to know is that being able to run a model and being able to run a model fast are two very different thresholds. You can get these models to run on a 128GB Mac, but we need to first tell if Q4 retains enough quality (models have different sensitivities to quantization) and how fast it runs. Very valid. This is an active area of research, and there are a lo…

Post by deaux » Thu, Apr 30, 2026, 5:03 AM UTC

pama wrote: post #159

deaux wrote: post #37

Earlier quoted context omitted.

Where are the competitive models from Singapore, Japan, Taiwan, Korea, Russia, Canada, India, the UK? From anywhere that isn't China or the US? There are none. Mistral Small 4 is pareto-competitive in its pricing bracket at $0.15/$0.60, at worst it's second to Gemma 4 26B A4B. The above countries have never had a model that is even close to being so. This particular Mistral Medium looks to be uncompetitive at that pr…

What does Pareto competitive mean here? Look at the pricing of the V4-flash model: https://api-docs.deepseek.com/quick_start/pricing

Post by wasfgwp » Thu, Apr 30, 2026, 5:46 AM UTC

argsnd wrote: post #174

wasfgwp wrote: post #78

Earlier quoted context omitted.

Without Google’s funding its not obvious i DeepMind would have went anywhere. Unless the moved to US for funding while keeping a back office in the UK. It’s strange to expect anything significant to come out from Europe when VCs there are either very risk averse and/or don’t have enough cash to begin with. It’s not like government or EU funding can replace that since its almost always wasted or missdirected

It’s a company containing such remarkable talent that I’m sure they would not have run into significant issues raising capital on international markets. It’s not like VCs are only allowed to invest in companies in their own country.