To me, mini releases matter much more and better reflect the real progress than SOTA models.
The frontier models have become so good that it's getting almost impossible to notice meaningful differences between them.
Meanwhile, when a smaller / less powerful model releases a new version, the jump in quality is often massive, to the point where we can now use them 100% of the time in many cases.
And since they're also getting dramatically cheaper, it's becoming increasingly compelling to actually run these models in real-life applications.
pzo 2 minutes ago [-]
they do are cheaper than SOTA but not getting dramatically cheaper but actually the opposite - GPT 5.4 mini is around ~3x more expensive than GPT 5.0 mini.
Similarly gemini 3.1 flash lite got more expensive than gemini 2.5 flash lite.
HugoDias 18 minutes ago [-]
According to their benchmarks, GPT 5.4 Nano > GPT-5-mini in most areas, but I'm noticing models are getting more expensive and not actually getting cheaper?
GPT 5 mini: Input $0.25 / Output $2.00
GPT 5 nano: Input: $0.05 / Output $0.40
GPT 5.4 mini: Input $0.75 / Output $4.50
GPT 5.4 nano: Input $0.20 / Output $1.25
simianwords 16 minutes ago [-]
models are getting costlier but by performance getting cheaper. perhaps they don't see a point supporting really low performance models?
HugoDias 13 minutes ago [-]
I would be curious to know if from the enterprise / API consumption perspective, these low-performance models aren't the most used ones. At least it matches our current scenario when it comes to tokens in / tokens out. I'd totally buy the price increase if these are becoming more efficient though, consuming less tokens.
simianwords 17 minutes ago [-]
why isn't nano available in codex? could be used for ingesting huge amount of logs and other such things
machinecontrol 38 minutes ago [-]
What's the practical advantage of using a mini or nano model versus the standard GPT model?
aavci 32 minutes ago [-]
Cheaper. Every month or so I visit the models used and check whether they can be replaced by the cheapest and smallest model possible for the same task. Some people do fine tuning to achieve this too.
powera 22 minutes ago [-]
I've been waiting for this update.
For many "simple" LLM tasks, GPT-5-mini was sufficient 99% of the time. Hopefully these models will do even more and closer to 100% accuracy.
The prices are up 2-4x compared to GPT-5-mini and nano. Were those models just loss leaders, or are these substantially larger/better?
HugoDias 15 minutes ago [-]
For us, it was also pretty good, but the performance decreased recently, that forced us to migrate to haiku-4.5. More expensive but much more reliable (when anthropic up, of course).
throwaway911282 9 minutes ago [-]
they dont change the model weights (no frontier lab does). if you have evals and all prompts, tool calls the same, I'm curious how you are saying performance decreased..
ryao 18 minutes ago [-]
I will be impressed when they release the weights for these and older models as open source. Until then, this is not that interesting.
Rendered at 17:52:16 GMT+0000 (Coordinated Universal Time) with Vercel.
The frontier models have become so good that it's getting almost impossible to notice meaningful differences between them.
Meanwhile, when a smaller / less powerful model releases a new version, the jump in quality is often massive, to the point where we can now use them 100% of the time in many cases.
And since they're also getting dramatically cheaper, it's becoming increasingly compelling to actually run these models in real-life applications.
Similarly gemini 3.1 flash lite got more expensive than gemini 2.5 flash lite.
GPT 5 mini: Input $0.25 / Output $2.00
GPT 5 nano: Input: $0.05 / Output $0.40
GPT 5.4 mini: Input $0.75 / Output $4.50
GPT 5.4 nano: Input $0.20 / Output $1.25
For many "simple" LLM tasks, GPT-5-mini was sufficient 99% of the time. Hopefully these models will do even more and closer to 100% accuracy.
The prices are up 2-4x compared to GPT-5-mini and nano. Were those models just loss leaders, or are these substantially larger/better?