Comment by sberens
10 days ago
For comparison, openrouter says opus 4.8 is ~55 tokens/s and fast mode is ~102.
750 tokens/s for their largest model is going to be nuts
10 days ago
For comparison, openrouter says opus 4.8 is ~55 tokens/s and fast mode is ~102.
750 tokens/s for their largest model is going to be nuts
What about 15k tokens per second? [0] I remember looking at this earlier in the year and it being so fast that it feels fake. And, yes, this model is old - but still awesome for what it is.
[0] https://chatjimmy.ai/
It’s not just old, it’s also tiny and quantized. It’s llama 3.1 8b at 3/6-bit quant. This is the type of thing you can run on almost any device…
I get that, but not at 15k tokens/s.
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I just tried it, and the answer is non-sense.
I asked it something simple, list some good indie puzzle games, and half the answers are games that don't exist. Imo quality > speed.
They baked the LLM into a CPU
at 15K tokens/s... do you need code anymore
Yeah, that's the point, right? With tool calling the LLM becomes code. So instead of asking it to write an accounting software, you can hire the LLM to be your accountant.
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Using gpt-5.4-mini in off-peak hours already feels like super-speed to me. That's probably no more than 100-150 tk/s. I can't imagine 750!
I've always eyed Cerebras but never had a use for it that would justify paying for the API directly. Although now that I think about it, trying out the API would probably cost less than a subscription for a month...
Try gpt-5.3-codex-spark - it's 1000 TPS and from my experience more capable than 5.4 mini.
If you have a subscription it's a different pool of usage.
Used it, very fast but tiny context window and doesn't have good reasoning. (good for quick simple code changes)
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The ChatGPT subscription gives you access to the -spark model(s) in Codex which are blazing fast (but pretty dumb) which I think runs on Cerebras hardware too.
is this specifically in codex? have been trying to use the models for months on opencode then pi but it says chatgpt subscriptions don't have access to it - i was under the assumption that OpenAI doesn't lock down their models based on harness a la Claude Code
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I have a pretty good use case for gpt-oss. The amount of time savings has actually been wild. Definitely worth a try. Just to be clear, it gets like 2000tok/s
But it seems that there is some queuing/load balancing on their side, I mean when opus is actually outputting this 55t/s it feles fast, but apart from it's internal reasoning I think there's sometimes just waiting.
Oh wait yeah good point. At 750 tokens a second and the same amount of human patients they can set it to think for the same amount of time but four or five times the amount of thinking tokens, which may improve the quality of the eventual output.
"up to 750 tokens per second"
Emphasis on "up to". Imagine whatever limited situation (e.g. pre-cached query) and that will probably be the only time it hits 750 tokens/sec.
the more advanced models also utilize a lot more tokens, and a lot of these extra tokens may go towards safeguards at a higher rate than prior models as well.
not to say a speed boost isnt there but if they didnt increase tokens / s at all youd likely see things slow down a lot with the new model compared to current
I think regular users will still have the old speed, so should be easy to tell whether it is more thinkier than 5.5.