Three companies shipped top-tier models within a month of each other, and the headline price gap is large: Google's Gemini 4 Argon launched at $2 p...
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The headline gap is a list price. On an agent, most input is the same prefix on every call, so the cache hit rate sets the real gap. Compare the cached rate per model, then multiply by your call count.
Fair point, Paul. My tables use list prices only. For agents, where most of each call is the same cached prefix, the cached input rate × your call count is what actually decides the bill. Thanks for adding that.
rainytech, this is a brilliantly clear breakdown of the llm pricing and availability landscape! 🐯
your point about "run your own eval: 20 to 50 tasks from your actual product" is the absolute golden rule of llm engineering. vendor benchmarks are marketing; real-world performance on your specific edge cases is what actually matters.
this perfectly mirrors the model routing logic we built into koda. we don't fire up a heavy, expensive model for a simple syntax check. we route to a fast, cheap model (like a 20b) for quick tasks, and reserve the heavy compute (120b) only for complex architectural debugging.
since you've been digging into these apis, i'm curious: have you noticed any significant differences in rate-limiting behavior or context-window handling between gpt-6.1 sol and claude sonnet 5.5 at that $2/$10 tier? for indie devs, those operational limits often matter more than the raw token price.
fantastic, highly practical analysis. thanks for keeping the community grounded in reality! 🛡️📊
Thanks Harun, and thanks for flagging that phishing comment 🙏 I've hidden it.
Good question. I haven't tested rate limits or long-context behavior for GPT-6.1 Sol vs Claude Sonnet 5.5 myself. This piece only compares list prices and what each company publishes. Both set rate limits by account tier, so check each provider's rate-limit docs for your plan. A hands-on test of exactly that would make a good follow-up.
rainytech, you're very welcome! glad i could help keep the feed clean. 🛡️
that makes total sense regarding the rate limits. i'll definitely keep an eye on the provider docs and maybe run a small load test on my end to see how gpt-6.1 sol handles sustained, 3g-friendly payloads compared to sonnet.
thanks again for the top-tier, no-fluff breakdown!
At $10/$50, models like GPT-6 Astra and Claude Fable 5.1 are 5x more expensive than the $2/$10 tier (Sol/Sonnet 5.5). For those testing Astra's autonomous computer use capabilities or Fable 5.1's ability to run unattended for days, is that top tier actually delivering 5x the business value in your production environment?
I'm new to dev.to! Any feedback or thoughts on my posts would be greatly appreciated.
Welcome to dev.to, Rahul! This piece was a pricing comparison, not a hands-on test, so I can't say whether the $10/$50 tier delivers 5x. My approach: start at $2/$10 and only move a task up when your own eval shows the cheaper model failing on it.
@rainytechlab Right, and the cache hit rate is the part that moves. One change in the prefix mid-session, like a timestamp or a reordered tool list, makes every turn after it pay full price.
Good catch, and an easy one to miss. Keep everything that changes per call (timestamps, request IDs, the newest user message) at the end, and keep the tool list in a fixed order, so the cached prefix stays identical from turn to turn.