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$1/M is hardly a big improvement over GPT5's $1.250/M (or Gemini Pro's $1.5/M), and given how much worse Haiku is than those at any kind of difficult problem (or problems with a large context size), I can't imagine it being a particularly competitive alternative for coding. Especially for anything math/logic related, I find GPT5 and Gemini Pro to be significantly better even than Opus (which reflects in their models having won Olympiad prizes while Anthropic's have not).


GPT-5 is $10/M for output tokens, twice the cost of Haiku 4.5 at $5/M, despite Haiku apparently being better at some tasks (SWE Bench).

I suppose it depends on how you are using it, but for coding isn't output cost more relevant than input - requirements in, code out ?


> I suppose it depends on how you are using it, but for coding isn't output cost more relevant than input - requirements in, code out ?

Depends on what you're doing, but for modifying an existing project (rather than greenfield), input tokens >> output tokens in my experience.


Unless you're working on a small greenfield project, you'll usually have 10s-100s of thousands of relevant words (~tokens) of relevant code in context for every query, vs a few hundred words of changes being output per query. Because most changes to an existing project are relatively small in scope.




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