On: Grok 4 Fast now has 2M context window

[mg]

If a model is not making use of the whole context window - shouldn't that be very noticeable when the prompt is code?

For example when querying a model to refactor a piece of code - would that really work if it forgets about one part of the code while it refactors another part?

I concatenate a lot of code files into a single prompt multiple times a day and ask LLMs to refactor them, implement features or review the code.

So far, I never had the impression that filling the context window with a lot of code causes problems.

I also use very long lists of instructions on code style on top of my prompts. And the LLMs seem to be able to follow all of them just fine.

[MallocVoidstar → mg]

I don't think there are any up-to-date leaderboards, but models absolutely degrade in performance the more context they're dealing with.

https://wandb.ai/byyoung3/ruler_eval/reports/How-to-evaluate...

>Gpt-5-mini records 0.87 overall judge accuracy at 4k [context] and falls to 0.59 at 128k.

And Llama 4 Scout claimed a 10 million token context window but in practice its performance on query tasks drops below 20% accuracy by 32k tokens.

[mg → MallocVoidstar]

That makes me wonder if we could simply test this by letting the LLM add or multiply a long list of numbers?

Here is an experiment:

https://www.gnod.com/search/#q=%23%20Calcuate%20the%20below%...

The correct answer:

    Correct:    20,192,642.460942328

Here is what I got from different models on the first try:

    ChatGPT:    20,384,918.24
    Perplexity: 20,000,000
    Google:     25,167,098.4
    Mistral:    200,000,000
    Grok:       Timed out after 300s of thinking

[gcanyon → mg]

> Do not use a calculator. Do it in your head.

You wouldn't ask a human to do that, why would you ask an LLM to? I guess it's a way to test them, but it feels like the world record for backwards running: interesting, maybe, but not a good way to measure, like, anything about the individual involved.

[throwuxiytayq → mg]

I’m starting to find it unreasonably funny how people always want language models to multiply numbers for some reason. Every god damn time. In every single HN thread. I think my sanity might be giving out.

[solatic → throwuxiytayq]

A model, no, but an agent with a calculator tool?

Then there's the question of why not just build the calculator tool into the model?

[KristoAI → mg]

Since grok 4 fast got this answer correct so quickly, I decided to test more.

Tested this on the new hidden model of ChatGPT called Polaris Alpha: Answer: $20,192,642.460942336$

Current gpt-5 medium reasoning says: After confirming my calculations, the final product (P) should be (20,192,642.460942336)

Claude Sonnet 4.5 says: “29,596,175.95 or roughly 29.6 million”

Claude haiku 4.5 says: ≈20,185,903

GLM 4.6 says: 20,171,523.725593136

I’m going to try out Grok 4 fast on some coding tasks at this point to see if it can create functions properly. Design help is still best on GPT-5 at this exact moment.

[jarek83 → mg]

Isn't that LLMs are not designed to do calculations?

[cluckindan → jarek83]

They are not LMMs, after all…

[mg → jarek83]

Neither are humans.

[cuu508 → mg]

But humans can still do it.