One thing about these numbers that's absolutely shocking to me is how low the energy use is:<p>> That model’s usage was well within our budget ($68, about 4kWh of energy use / 365 grams of carbon emissions).<p>The energy cost is literally 1% of the total cost. For context, 4kWh of energy would drive you about 15 miles in an EV, about <i>half</i> of the average person's driving miles. It's boiling 10 gallons of water.<p>With the talk of AI Data Center's impact on the world, you'd think this would be 10x to 100x the amount of energy in order to get the effects they're using here.<p>My takeaway: the AI data center buildout is an overbuild probably at least as large as the fiber buildout that left us with so much dark fiber. If not even bigger. The only thing that will save the economy is the inability of NVIDIA and chip fabs to produce enough chips to match the buildout planned.
GLM5.3-flash has been fantastic for me to make minor fixes in ambigious ways. "Fix x feature, whats going wrong. " It does the job.
Was this post generated with LLM, did he properly mention anywhere why exactly did it fail with example or i have trouble reading.
> Unfortunately there are still consequences to it. I chose the 'wrong' model for the prototype, and we spent 450M tokens / $150 / 5kWh of energy use almost overnight. The MCP server itself works well and we now have a great demo of the capabilities, so it’s not for nothing:<p>> Nonetheless, it’s a good reminder to be careful with model selection and with agentic patterns. We could have achieved similar results for most likely 5x less cost with not that much more effort. Lessons learned! We need to budget for this, and be more careful. Could have seen it coming, but now we know.<p>I don't get it. Why was it wrong? Which one would have been better? What was the lesson and how could you have foreseen it?
How did you measure energy usage?<p>Edit: I found a linked article that mentions the inference provider who does the measurements.
It was a bit of a silly challenge, wasn’t sure how workable, learned a lot in the process about what actually drives usage / costs, and how to keep both under control
Considering they were your top two models, how did the flash and non-flash versions compare? Did you use them for different tasks?
I have a hard time justifying GLM 5.3 these days. It’s slightly better than Flash but rarely enough to justify the much steeper price. We chose to use usage-based billing only so are very sensitive to model price.
When text wuality or for pure but adwansed coding is concerned i always pick glm 5.3. The flash is awesome for everything that dosent really matter though.
Worth a comparison with DeepSeek v4.1 flash, if you've got another month to spare!
Ill tell you from my personal use glm 5.3 flash was better then deepseek 4.1 flash, also deepseek liked to yap in his reasoning traces soo fucking much, the yapping was fast but the task was so slow to complete...
Yep, I think next month will be on that. It feels slightly better from a few days of use, and in our WIP benchmarking it scores way higher
DS v4.1 Flash is roughly equivalent. It's going to get some things right/better that GLM flash doesnt and vice versa.
They've got an image of their homegrown benchmark in the post that lists DS4.1.
Flash is pretty decent coder, but it should be paired with good planner and reviewer. I would pick astra low for planning and sol 6.1 medium for reviews.
What would you use if you wanted to stay (at least) open weight?
qwen3.8, kimi3, kimi2.7, GLM-5.3 are all good families I use in my coding team<p>I'm mainly using flash varients, at least as the default, bump.up to stronger model as needed (less often these days)
Deepseek 4.1 Flash and Mimo 2.6 Flash.
Flash is plenty good for planning and reviewing, for my needs. In fact, I use it for that because it's too slow for execution, despite the name.<p>edit: I subscribe to z.ai, I don't host.
> I use it for that because it's too slow for execution<p>What kind of hardware and what particular quant?
Will agree on this. From z.ai i have found the non flash to have way more consistent performance.