31–40 of 292 posts
The main thing holding these Anthropic models back is context size. Yes, quality deteriorates over a large context window, but for some applications, that is fine. My company is using grok4-fast, the Gemini family, and GPT4.1 exclusively at this point for a lot of operations just due to the huge 1m+ context.
https://docs.claude.com/en/docs/build-with-claude/context-wi...
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With caching that's 10 cents per million in. Most of the cheap open source models (which this claims to beat, except glm 4.6) have limited and not as effective caching. This could be massive.
The funny thing is that even in this area Anthropic is behind other 3 labs (Google, OpenAI, xAI). It's the only one out of those 4 that requires you to manually set cache breakpoints, and the initial cache costs 25% more than usual context. The other 3 have fully free implicit caching. Although Google also offers paid, explicit caching. https://docs.claude.com/en/docs/build-with-claude/prompt-cac... https://ai.google…
If I'm missing something about how inference works that explains why there is still a cost for cached tokens, please let me know!
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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…
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 ?
Depends on what you're doing, but for modifying an existing project (rather than greenfield), input tokens >> output tokens in my experience.
Where it doesn't shine much is on very large coding task. but it is a phenomenal model for small coding tasks and the speed improvement is much welcome
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The funny thing is that even in this area Anthropic is behind other 3 labs (Google, OpenAI, xAI). It's the only one out of those 4 that requires you to manually set cache breakpoints, and the initial cache costs 25% more than usual context. The other 3 have fully free implicit caching. Although Google also offers paid, explicit caching. https://docs.claude.com/en/docs/build-with-claude/prompt-cac... https://ai.google…
I don't understand why we're paying for caching at all (except: model providers can charge for it). It's almost extortion - the provider stores some data for 5min on some disk, and gets to sell their highly limited GPU resources to someone else instead (because you are using the kv cache instead of GPU capacity for a good chunk of your input tokens). They charge you 10% of their GPU-level prices for effectively _not_…
$1/M input tokens and $5/M output tokens is good compared to Claude Sonnet 4.5 but nowadays thanks to the pace of the industry developing smaller/faster LLMs for agentic coding, you can get comparable models priced for much lower which matters at the scale needed for agentic coding. Given that Sonnet is still a popular model for coding despite the much higher cost, I expect Haiku will get traction if the quality is a…
Yeah, I'm a bit disappointed by the price. Claude 3.5 Haiku was $0.8/$4, 4.5 Haiku is $1/$5. I was hoping Anthropic would introduce something price-competitive with the cheaper models from OpenAI and Gemini, which get as low as $0.05/$0.40 (GPT-5-Nano) and $0.075/$0.30 (Gemini 2.0 Flash Lite).
Haiku 4.5: I $1.00/M, O $5.00/M
Grok Code: I $0.2/M, O $1.5/M
I am really interested in the future of Opus; is it going to be an absolute monster, and continue to be wildly expensive? Or is the leap from 4 -> 4.5 for it going to be more modest.