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| ▲ | aschobel 9 hours ago | parent | next [-] |
| For coding I don’t use any of the previous gen models anymore. Ideally I would have both fast and SOTA; if I would have to pick one I’d go with SOTA. There a report by OpenRouter on what folks tend to pay for it; it generally is SOTA in the coding domain. Folks are still paying a premium for them today. There is a question if there is a bar where coding models are “good enough”; for myself I always want smarter / SOTA. |
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| ▲ | wyre 7 hours ago | parent [-] | | FWIW coding is one of the largest usages for LLM's where SOTA quality matters. I think the bar for when coding models are "good enough" will be a tradeoff between performance and price. I could be using Cerebras Code and saving $50 a month, but Opus 4.5 is fast enough and I value the piece-of-mind I have knowing it's quality is higher than Cerebras' open source models to spend the extra money. It might take a while for this gap to close, and what is considered "good enough" will be different for every developer, but certainly this gap cannot exist forever. |
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| ▲ | gejose 5 hours ago | parent | prev | next [-] |
| > just need to be good enough and fast as fuck Hard disagree. There are very few scenarios where I'd pick speed (quantity) over intelligence (quality) for anything remotely to do with building systems. |
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| ▲ | ssivark 2 hours ago | parent | next [-] | | If you thought a human working on something will benefit from being "agile" (building fast, shipping quickly, iterating, getting feedback, improving), why should it be any different from AI models? Implicit in your claim are specific assumptions about how expensive/untenable it is to build systemic guardrails and human feedback, and specific cost/benefit ratio of approximate goal attainment instead of perfect goal attainment. Rest assured that there is a whole portfolio of situations where different design points make most sense. | | |
| ▲ | nkmnz 38 minutes ago | parent [-] | | > why should it be any different from AI models? 1. law of diminishing returns - AI is already much, much faster at many tasks than humans, especially at spitting out text, so becoming even faster doesn’t always make that much of a difference.
2. theory of constraints - throughput of a system is mostly limited by the „weakest link“ or slowest part, which might not be the LLM, but some human-in-the-loop, which might be reduced only by smarter AI, not by faster AI.
3. Intelligence is an emergent property of a system, not a property of its parts - with other words: intelligent behaviour is created through interactions. More powerful LLMs enable new levels of interaction that are just not available with less capable models. You don’t want to bring a knife, not even the quickest one in town, to a massive war of nukes. |
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| ▲ | jameshush 5 hours ago | parent | prev | next [-] | | I agree with you for many use cases, but for the use case I'm focused on (Voice AI) speed is absolutely everything. Every millisecond counts for voice, and most voice use cases don't require anything close to "deep thinking. E.g., for inbound customer support use cases, we really just want the voice agent to be fast and follow the SOP. | | |
| ▲ | nkmnz 34 minutes ago | parent [-] | | If you have a SOP, most of the decision logic can be encoded and strictly enforced. There is zero intelligence involved in this process, it’s just if/else. The key part is understanding the customer request and mapping it to the cases encoded in the SOP - and for that part, intelligence is absolutely required or your customers will not feel „supported“ at all, but be better off with a simple form. |
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| ▲ | gessha 3 hours ago | parent | prev [-] | | As long as the faster tech is reliable and I understand its quirks, I can work with it. |
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| ▲ | Aurornis 6 hours ago | parent | prev | next [-] |
| > They don't need to catch up. They just need to be good enough The current SOTA models are impressive but still far from what I’d consider good enough to not be a constant exercise in frustration. When the SOTA models still have a long way to go, the open weights models have an even further gap distance to catch up. |
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| ▲ | nl 9 hours ago | parent | prev | next [-] |
| GPT 5 Codex is great - the best coding model around except maybe for Opus. I'd like more speed but prefer more quality than more speed. |
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| ▲ | Demiurge 7 hours ago | parent | prev | next [-] |
| I get GPT 5.2 responses on copilot faster than for any other model, almost instantly. Are you sure they’re slow as fuck? |
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| ▲ | dontwannahearit 9 hours ago | parent | prev | next [-] |
| Confused. Is ‘fuck’ fast or slow? Or both at the same time? Is there a sort of quantum superposition of fuck? |
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| ▲ | echelon 6 hours ago | parent | prev | next [-] |
| This. You can distill a foundation model into open source. The Chinese will be doing this for us for a long time. We should be glad that the foundation model companies are stuck running on treadmills. Runaway success would be bad for everyone else in the market. Let them sweat. |
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| ▲ | nineteen999 9 hours ago | parent | prev [-] |
| Bullseye. |
