Input
$0.10 / MTok for prompts up to 100,000 tokens
$0.50 / MTok for prompts over 100,000 tokens
Output
$0.50 / MTok for prompts up to 100,000 tokens
$2.50 / MTok for prompts over 100,000 tokens
In both cases, still much cheaper than Haiku 4.5's $1 input / $5 output and these prices better compete with GPT-6 Luna. ($0.10 input / $0.50 output, but with no token threshold [EDIT: the threshold for Luna is apparently 272k])
I will try the new Haiku, but it would be worthwhile if Haiku could take sane instructions and do all file editing for Opus / Sonnet / Fable then it would be worth using.
From OpenAI's website: Prompts with more than 272K input tokens are priced at 2x input and cache rates and 1.5x output for the full request.
Fixed.
GDPval-AA v2.1 as of now: 1620
GDPval-AA v2.1 for Haiku 4.5: 735
The 100k tokens pricing makes sense, looks to be a hedge against OpenAI's decisions API and Jev or its open source alternatives that are springing up.
Nice release, congrats to Anthropic.
We are witnessing the acceptance of average and accelerating more of the same low quality slop.
This is more expensive, but it also looks like it's better enough that it's far more useful.
I also won't be surprised if you look at cost per completed task + wall clock time that it comes out ahead for the majority of what you'd want to actually use it for.
Luna will still be a great option for doing non-engineering tasks super cheaply.
For prompts over 100k tokens it's 5 times more expensive - $0.50 in, $2.50 out.
She said she was using Haiku 4.5 because she was advised to be careful with the spending.
I hate that model so much lol.
Anthropic has really been doing all the right things in the past few weeks, while OpenAI continues to fumble the bag.
https://support.claude.com/en/articles/15036540-use-the-clau...
I also have an "ask" script that I use daily to ask simple stuff, it can access websearch and webfetch, it's more than enough to parse logs, ask for commands, quick research on the internet, small stuff. https://github.com/mariocesar/dotfiles/blob/main/common/.loc...
I use haiku for things that needs to be quick, have really clear instructions.
Planning on doing flash analyses of PRs that impact evals in some way, and then post comments on GitHub whenever there’s flaws in them
If you block pentest or "other techniques more likely to be used by attackers", then what does "permit a wider range of defensive tasks" even mean?
Any defensive task that's meaningful is almost indistinguishable from legitimate red-teaming that then falls under 'likely to be used by attackers". If only they would just stop nerfing these models, that'd be great. No APT is waiting around for Anthropic's permission, so might as well let us have some cool stuff.
(sadly Mistral Large 4 isn't up to par - but Mistral serves GLM at 130 tps!)
Did anyone read this? We get free API credits on some plans now
This is a very big benefit for me. I can now ship actual ai enhanced features behind my subscription without paying extra or fully relying on on-device models. I do worry that this is to soften the blow for user-unfriendly changes
That's 5-15 minutes of work at most. Not exactly the type of lock-in the parent is implying.
Opus 5.5 runs 117 tps average on Openrouter, so it must be at least 10-20 tps slower for them to mention. IDK why they mention this as it does not help for marketing though. https://openrouter.ai/anthropic/claude-opus-5.5
Haiku 5.5: https://html.non.io/lcars-haiku-5.5/
Opus 5.5 for comparison: https://html.non.io/lcars-opus-5.5
Designs it was building from: https://diffui.ai/app/canvas/5093e689-1e74-4f26-b632-2a4500f...
One interesting thing is it took a look at the job at hand, and immediately delegated it to Opus 5.5. It at least knows what it isn't good at. Very fast though, and likely best used for small subagent tasks / tightly scoped work.
- https://blog.chrislewis.au/using-coding-agents-to-decompile-nintendo-64-games/
- https://blog.chrislewis.au/the-long-tail-of-llm-assisted-decompilation/
I could now one-shot a new game, yeah.
> but they still block penetration testing and other techniques more likely to be used by attackers.
>
> Haiku 5.5’s biology safeguards are the same as for Sonnet 5, Sonnet 5.5, and Opus 5. They allow research biology questions but restrict access to requests that we judge as likely to cause harm. Organizations working on wider-ranging biology and cyber activities can apply to our Life Sciences Verification Program and Cyber Verification Program.
These are the examples from "Detecting and countering misuse of AI: September 2026" - https://news.ycombinator.com/item?id=49647300
> Importantly, because our biological safety classifiers robustly block content involving high-risk biological research (in this case, the construction of enhanced pandemic potential pathogens), all of these exchanges occurred on models in our weakest class of models (specifically, the models were Claude Sonnet 4 and Haiku 4.5, the latter of which the user began using after Sonnet 4 was deprecated).
>
> Upon a detailed examination of the exchanges, we estimate that the uplift provided by Claude was primarily clerical assistance in data analysis, study ideation and design. This is consistent with our understanding of the capabilities of Sonnet 4 and Haiku 4.5, which are not able to perform expert-level biology research tasks; we estimate that the uplift provided to the researcher was limited and substantially lower than it would have been from one of our more capable models.
While doing my best to avoid comment, please note, they're talking about a domain expert in a state research institution using Claude to do paperwork.
What did they save us from? What bioweapons did these filters prevent? From the front matter report,
> The above LLM platform is not the only route via which researchers engaged in viral gain-of-function research have used our platform. In May 2026, we discovered a researcher outside the US using Claude in their research on highly-pathogenic avian influenza (“bird flu”). The research focused on viruses’ adaptation to mammals, and the mechanism by which it causes severe disease beyond the respiratory tract.
> The researcher pursued this work in a credible institutional context, and interacted with Claude over the course of several weeks, exchanging thousands of messages. In these exchanges, the researcher leveraged Claude’s knowledge of the scientific literature to assist the researcher in study planning and design, data analysis, and the interpretation and prioritization of experiments. The researcher also used Claude for editorial assistance in writing up the research.
What "uplift" are you providing to scientists working at specialized global BSL-4 labs that already have – and I quote their report - "physical access to such isolates." (as in samples of viruses)?
This nonsense has been expanded with even worse "safeguards."
This makes Claude unusable for any serious scientist or people curious about science, which is sad.
Was a big fan of Haiku 4.5, though understand why for most Sonnet was the far better option.
I ask:
> how many r's in diminished
It answers:
> Diminished has 1 r.
Neither encode nor decode are linear in compute, so providers need to price for average expected length.
This is just getting closer to the true cost of generating tokens.
For a while Anthropic has lacked a cost effective “cheap” LLM for summarisation, compacting, RAG helpers, etc.
These ‘ephemeral’ workloads are often under 100k tokens, or can be structured to be under 100k.
In some coding benchmarks, Haiku 5.5 beats Sonnet 5! (Especially implementation; do a well defined Jira ticket; etc), it’s really impressive how much intelligence per dollar has grown in just a few short months.
There are plenty of workflows like translations where you'd easily be under the cap.
Your vibes don't appear to be supported by facts. From the announcement:
>> Claude Haiku 5.5 is priced 90% lower than Claude Haiku 4.5 for requests up to 100,000 tokens, and 50% lower for requests over 100,000 tokens. On Haiku 4.5, 90% of requests fell into the former category.
You can test with Anthropic's count_tokens endpoint or with https://crates.io/crates/tokwc
So, it is might be even worse.
For this application 100K token input is plenty.
Of course Anthropic and OpenAI, both at $0.10/M, are still 2.5x the cost of Jev's $0.04/M.
For other tasks like summaries (another suggested usage) it's good to see Luna and Haiku now competing against each other on cost.
I'd love to know how the business automation market breaks down by volume of call type though - hard to imagine that decision making (e.g. branching, triage) isn't a very large part of it, greater than these other suggested Haiku use cases.
Of course it’s not as big, and hence falls-off quicker. I’d consider the 100k a “promotional price” to match Luna’s token pricing while delivering noticeably more intelligence.
I've been using Luna, but I'll probably switch to Haiku.
used <10% of my 5hr limit on a $100 codex plan.
This is more to encourage people to try out adding AI into their product, which is a totally different flow and experience from using AI to build the product.
Anthropic isn't even close to being this useful.
Biggest loss is that Ant models look like they are genuinely better.
This changes on a weekly basis, I ended up with subscriptions to most of the providers (except for X.ai).
https://support.claude.com/en/articles/15036540-use-the-clau...
Of course, they don't do this out of pure kindness, but I really struggle to see a negative for subscribers, especially given changing to another model is essentially frictionless, so once the monthly allowance is used up, you can still just decide not to use Anthropic models for the remainder.
It's meant to be a good test, not a good design.
TIME 19m COST $0.16 https://jonclegg.github.io/pacman-bakeoff/
TheAmazingRace•1h ago
himata4113•1h ago
serious bit: if you think about how these smaller models work, at the end of the day it seems that they are now capable of forgetting useless information because they're able to derive it in reasoning allowing models to become smaller at the cost of requiring more reasoning tokens to solve a task.
qeternity•1h ago
dyauspitr•1h ago
bravetraveler•1h ago
ChaseRensberger•1h ago
onlyrealcuzzo•52m ago
You'll know when the trend stops -> when the intelligence differential between smaller models like 7B starts to grow instead of shrink from 32B models -> that means 7B is getting about as smart as it can get. Then, 32B will follow next, then 70B, etc etc.
We haven't yet seen that at any size AFAIK.
thefourthchime•48m ago
onlyrealcuzzo•31m ago
I wouldn't be surprised if less than 1B param equivalent of our brain deals with solving math and writing computer programs and physics and all the things we tend to associate with "intelligence" - especially if you ultra optimized for that, I doubt our brain works like that.
Dealing with the real world, I highly highly doubt it.
jstummbillig•23m ago
Given that humans learn to talk while having encountered a measly number of word instances, and, given enough time, we should always be able to improve on the lottery that is biology, it does seems fairly likely.
istjohn•45m ago
> The cost of achieving a given level of AI performance has fallen about 47% per quarter since 2023, or 13× per year. [0]
0. https://epoch.ai/publications/the-plunging-price-of-thought
FooBarWidget•25m ago
jstummbillig•23m ago
teaearlgraycold•14m ago
adgjlsfhk1•13m ago