7 ms·
Without benchmarking LLMs, you're likely overpaying
- petcat 9mo ago> He's a non-technical founder building an AI-powered business. It sounds like he's building some kind of ai support chat bot. I despise these things.
- njhnjh 9mo ago[flagged]
- sullivanmatt 9mo agoIt's perfectly possible it's someone with deep domain experience, or someone who has product design or management skills. Regardless, dismissing these people out of pocket is not likely the best choice.
- deleted 9mo ago[deleted]
- r_lee 9mo agoAnd the whole article is about promoting his benchmarking service, of course.
- montroser 9mo agoThe whole post is just an advert for this person's startup. Their "friend" doesn't exist...
- lorey 9mo agoTotally agree with your point. While I can't say specifically, it's a traditional (German) business he's doing vertically integrated with AI. Customer support is really bad in this traditional niche and by leveraging AI on top of doing the support himself 24/7, he was able to make it his competitive edge.
- verdverm 9mo agoI'd second this wholeheartedly Since building a custom agent setup to replace copilot, adopting/adjusting Claude Code prompts, and giving it basic tools, gemini-3-flash is my go-to model unless I know it's a big and involved task. The model is really good at 1/10 the cost of pro, super fast by comparison, and some basic a/b testing shows little to no difference in output on the majority of tasks I used Cut all my subs, spend less money, don't get rate limited
- r_lee 9mo agoPlus I've found that overall with "thinking" models, it's more like for memory, not even actual perf boost, it might even be worse because if it goes even slightly wrong on the "thinking" part, it'll then commit to that for the actual response
- verdverm 9mo agofor sure, the difference in the most recent model generations makes them far more useful for many daily tasks. This is the first gen with thinking as a significant mid-training focus and it shows gemini-3-flash stands well above gemini-2.5-pro
- dingnuts 9mo ago[dead]
- dpoloncsak 9mo agoYeah, one of my first projects one of my buddies asked "Why aren't you using [ChatGPT 4.0] nano? It's 99% the effectiveness with 10% the price." I've been using the smaller models ever since. Nano/mini, flash, etc.
- phainopepla2 9mo agoI have been benchmarking many of my use cases, and the GPT Nano models have fallen completely flat one every single except for very short summaries. I would call them 25% effectiveness at best.
- andy99 9mo agoDepends on what you’re doing. Using the smaller / cheaper LLMs will generally make it way more fragile. The article appears to focus on creating a benchmark dataset with real examples. For lots of applications, especially if you’re worried about people messing with it, about weird behavior on edge cases, about stability, you’d have to do a bunch of robustness testing as well, and bigger models will be better. Another big problem is it’s hard to set objectives is many cases, and for example maybe your customer service chat still passes but comes across worse for a smaller model. Id be careful is all.
- candiddevmike 9mo agoOne point in favor of smaller/self-hosted LLMs: more consistent performance, and you control your upgrade cadence, not the model providers. I'd push everyone to self-host models (even if it's on a shared compute arrangement), as no enterprise I've worked with is prepared for the churn of keeping up with the hosted model release/deprecation cadence.
- andy99 9mo agoHow much you value control is one part of the optimization problem. Obviously self hosting gives you more but it costs more, and re evals, I trust GPT, Gemini, and Claude a lot more than some smaller thing I self host, and would end up wanting to do way more evals if I self hosted a smaller model. (Potentially interesting aside: I’d say I trust new GLM models similarly to the big 3, but they’re too big for most people to self host)
- blharr 8mo agoWhere can I find information on self-hosting models success stories? All of it seems like throwing tens of thousands away on compute for it to work worse than the standard providers. The self-hosted models seem to get out of date, too. Or there ends up being good reasons (improved performance) to replace them
- jmathai 9mo agoYou may also be getting a worse result for higher cost. For a medical use case, we tested multiple Anthropic and OpenAI models as well as MedGemma. Pleasantly surprised when the LLM as Judge scored gpt5-mini as the clear winner. I don't think I would have considered using it for the specific use cases - assuming higher reasoning was necessary. Still waiting on human evaluation to confirm the LLM Judge was correct.
- epolanski 9mo agoThe author of this post should benchmark his own blog for accessibility metrics, text contrast is dreadful.. On the other hand, this would be interesting for measuring agents in coding tasks, but there's quite a lot of context to provide here, both input and output would be massive.
- lorey 9mo agoAppreciate the feedback, will work on that.
- faeyanpiraat 9mo agoOne more vote on fixing contrast from me.
- lorey 9mo agoWill fix, thanks :)
- faeyanpiraat 9mo agoTried Evalry, its a really nice concept, thanks for sharing it!
- epolanski 9mo agoDo you have any insights on the platform evaluation for coding tasks?
- lorey 9mo agoPushed a fix. Could you check, please? Any resources you can recommend to properly tackle this going forward?
- gridspy 9mo agoWow, this was some slick long form sales work. I hope your SaaS goes well. Nice one!
- hamiltont 9mo agoAnecdotal tip on LLM-as-judge scoring - Skip the 1-10 scale, use boolean criteria instead, then weight manually e.g. - Did it cite the 30-day return policy? Y/N - Tone professional and empathetic? Y/N - Offered clear next steps? Y/N Then: 0.5 * accuracy + 0.3 * tone + 0.2 * next_steps Why: Reduces volatility of responses while still maintaining creativeness (temperature) needed for good intuition
- pocketarc 9mo agoI use this approach for a ticket based customer support agent. There are a bunch of boolean checks that the LLM must pass before its response is allowed through. Some are hard fails, others, like you brought up, are just a weighted ding to the response's final score. Failures are fed back to the LLM so it can regenerate taking that feedback into account. People are much happier with it than I could have imagined, though it's definitely not cheap (but the cost difference is very OK for the tradeoff).
- Imustaskforhelp 9mo agoThis actually seems really good advice. I am interested how you might tweak this to things like programming languages benchmarks? By having independent tests and then seeing if it passes them (yes or no) and then evaluating and having some (more complicated tasks) be valued more than not or how exactly.
- hamiltont 9mo agoNot sure I'm fully following your question, but maybe this helps: IME deep thinking hgas moved from upfront architecture to post-prototype analysis. Pre-LLM: Think hard → design carefully → write deterministic code → minor debugging With LLMs: Prototype fast → evaluate failures → think hard about prompts/task decomposition → iterate When your system logic is probabilistic, you can't fully architect in advance—you need empirical feedback. So I spend most time analyzing failure cases: "this prompt generated X which failed because Y, how do I clarify requirements?" Often I use an LLM to help debug the LLM. The shift: from "design away problems" to "evaluate into solutions."
- 9mo ago
- deepsquirrelnet 9mo agoThis is just evaluation, not “benchmarking”. If you haven’t setup evaluation on something you’re putting into production then what are you even doing. Stop prompt engineering, put down the crayons. Statistical model outputs need to be evaluated.
- andy99 9mo agoWhat does that look like in your opinion, what do you use?
- lorey 9mo agoThis went straight to prod, even earlier than I'd opted for. What do you mean?
- deepsquirrelnet 9mo agoI’m totally in alignment with your blog post (other than terminology). I meant it more as a plea to all these projects that are trying to go into production without any measures of performance behind them. It’s shocking to me how often it happens. Aside from just the necessity to be able to prove something works, there are so many other benefits. Cost and model commoditization are part of it like you point out. There’s also the potential for degraded performance because of the shelf benchmarks aren’t generalizing how you expect. Add to that an inability to migrate to newer models as they come out, potentially leaving performance on the table. There’s like 95 serverless models in bedrock now, and as soon as you can evaluate them on your task they immediately become a commodity. But fundamentally you can’t even justify any time spent on prompt engineering if you don’t have a framework to evaluate changes. Evaluation has been a critical practice in machine learning for years. IMO is no less imperative when building with llms.
- deleted 9mo ago[deleted]
- nickphx 9mo agoah yes... nothing like using another nondeterministic black box of nonsense to judge / rate the output of another.. then charge others for it.. lol
- coredog64 9mo agoAmazon Bedrock Guardrails uses a purpose-built model to look for safety issues in the model inputs/outputs. While you won't get any specific guarantees from AWS, they will point you at datasets that you can use to evaluate the product and then determine if it's fit for purpose according to your risk tolerance.
- OutOfHere 9mo agoYou don't need a fancy UI to try the mini model first.
- lorey 8mo agoThat is not what the article argues.
- matusp 9mo agoI do not disagree with the post, but I am surprised that a post that is basically explaining very basic dataset construction is so high up here. But I guess most people just read the headline?
- ebla 8mo agoAren't you supposed to customize the prompts to the specific models?
- lorey 8mo agoI've skipped that in the article, but absolutely!
- tantalor 8mo ago> it's the default: You have the API already Sorry, this just makes no sense to start off with. What do you mean?
- lorey 8mo agoFixed, thanks. Not a native speaker.
- iFire 8mo agoI love the user experience for your product. You're giving a free demo with results within 5 minutes and then encourage the customer to "sign in" for more than 10 prompts. Presumably that'll be some sort of funnel for a paid upload of prompts.
- iFire 8mo agohttps://evalry.com/question-benchmarks/game-engine-assistant-coding-quality-for-godot-engine-4-74 https://evalry.com/question-benchmarks/game-engine-assistant... Here's a bug report, by switching the model group the api hangs in private mode.
- gforce_de 8mo agoWow - interesting how strong the differences are! What seems missing: I can not see the answer from the different models. One have to rely on the "correctness" score. Another minor thing: the scoring seems hardcoded to: 50% correctness, 30% cost, 20% latency - which is OK, but in my case i care more about correctness and latency I don't care. Wow! This was my testprompt: You are an expert linguist and translator engine. Task: Translate the input text from English into the languages listed below. Output Format: Return ONLY a valid, raw JSON object. Do not use Markdown formatting (no ```json code blocks). Do not add any conversational text. Keys: Use the specified ISO 639-1 codes as keys. Target Languages and Codes: - English: "en" (Keep original or refine slightly) - Mandarin Chinese (Simplified): "zh" - Hindi: "hi" - Spanish: "es" - French: "fr" - Arabic: "ar" - Bengali: "bn" - Portuguese: "pt" - Russian: "ru" - German: "de" - Urdu: "ur" Input text to translate: "A smiling boy holds a cup as three colorful lorikeets perch on his arms and shoulder in an outdoor aviary."
- Havoc 8mo agoI’m also collecting the data my side with the hopes of later using it to fine tuning a tiny model later. Unsure whether it’ll work but if I’m using APIs anyway may as well gather it and try to bottle some of that magic of using bigger models
- dizhn 8mo agoI paid a total of 13 US Dollars for all my llm usage in about 3 years. Should I analyze my providers and see if there's room for improvement?
- lorey 8mo agoDepends on your remaining budget ;)
- dizhn 8mo agoThat is absolutely right. :)
- regenschutz 8mo agoHow? All LLM-as-a-Servive's are prohibitively expensive for me. $13 over 3 years sounds too-good-to-be-true.
- dizhn 8mo agoAll local CLIs with free to use models. CLIs are opencode, iflow, qwen, gemini. What I did splurge on was brief openai access for some subtitle translator program and when I used the deepseek api. Actually I think that $13 includes some as yet unused credits. :D I'd be happy to provide details if CLIs are an option and you don't m ind some sweatshop agent. :) (I am just now noticing I meant to type 2 years not 3 above. Sorry about that.)
- wolttam 8mo agoI'm consistently amazed at how much some individuals spend on LLMs. I get a good amount of non-agentic use out of them, and pay literally less than $1/month for GLM-4.7 on deepinfra. I can imagine my costs might rise to $20-ish/month if I used that model for agentic tasks... still a very far cry from the $1000-$1500 some spend.
- lorey 8mo agoDoesn't this depend a lot on private vs company usage? There's no way I could spend more than a few hundreds alone, but when you run prompts on 1M entities in some corporate use case, this will incur costs, no matter how cheap the model usage.
- PeterStuer 8mo agoAll true, but from what I see in the field it is most often an "ain't nobody got time for that" as teams rush into adoption the costs be dammed for now. We'll deal with it only if cost becomes a major issue.
- lorey 8mo agoHaha, very true. Exactly as described in the article.
- xmcqdpt2 8mo agoThis is useful when selecting a model for an initial application. The main issue I'm concerned about though is ongoing testing. At work we have devs slinging prompt changes left and right into prod, after "it works on my machine" local testing. It's like saying the words "AI" is sufficient to get rid of all engineering knowledge. Where is TDD for prompt engineering? Does it exist already?
- cap11235 8mo agoEvals have always existed, and not using them when building systems is relying on superstition.
- lorey 8mo agoThis is true with one caveat. In most cases, e.g. with regular ML, evals are easy and not doing them results in inferior performance. With LLMs, especially frontier LLMs, this has flipped. Not doing them will likely give you alight performance and at the same time proper benchmarks are tricky to implement.
- lorey 8mo agoThis is a very good point. When I came in, the founder did a lot of evaluation based on a few prompts and with manual evaluation, exactly as described. Showing the results helped me underline the fact that "works for me" (tm) does not match the actual data in many cases.