Skip to content

Claude Haiku 5.5: Anthropic's New Workhorse.

Read the Portuguese translation →

Author: Prompt Engineering · Watch on YouTube

Video authored by the channel shown, not by IANIA; all rights belong to the creator. Transcript and summary generated automatically; the original-language content is preserved.

Original language: English

Original video: Claude Haiku 5.5: Anthropic’s New Workhorse. · Channel: Prompt Engineering · 01:02 · Watch on YouTube

Topics: AI agents, AI developer tools, Model inference and optimization, AI models

Anthropic has released Haiku 5.5, describing it as its cheapest, fastest, and most capable small model so far. It is substantially cheaper than Haiku 4.5 and reportedly improves computer-use performance, with an effort setting that lets users trade cost for intelligence. Anthropic positions it for routine work alongside Opus and Sonnet, while saying larger models remain better for complex agentic coding. The video also notes new monthly API credits for Max and Team Plan users and says the model is available through the Cloud API and three major cloud providers.

  • Anthropic calls Haiku 5.5 its cheapest, fastest, and most capable small model to date.
  • For most requests, it is about 10 times cheaper than Haiku 4.5.
  • Input costs 10 cents per million tokens, which the video says is the same price as OpenAI’s GPT-6 Luna.
  • Above 100,000 tokens, the transcript says users get half off; Anthropic estimates it is about 75% cheaper on average.
  • Anthropic says computer-use scores rose from 16% to 72%.
  • Haiku 5.5 is the first Haiku model with an effort setting to trade cost for intelligence.
  • Anthropic positions it for summarization, classification, and sub-agent jobs alongside Opus and Sonnet.
  • Anthropic says larger models are still better for complex, agentic coding.
  • Max and Team Plan users are getting monthly API credits starting this week.
  • The model is available on the Cloud API and all three major clouds.
Start Chapter Summary
00:00 Haiku 5.5 pricing The video introduces Haiku 5.5 and outlines its pricing compared with Haiku 4.5.
00:31 Capabilities and intended use It covers reported gains in computer use, the effort setting, and the model’s role alongside larger Anthropic models.
01:02 Availability and competition The video comments on competition, mentions API credits for some plans, and notes the model’s availability.

the cheapest, fastest, and most capable small model they have ever released. — 00:00

For most requests, it’s about 10 times cheaper than Haiku 4.5. — 00:00

It’s also the first Haiku with an effort setting — 00:31

the bigger models are still better choices for complex, agentic coding. — 00:31

So, the competition is really heating up. — 01:02

Haiku 5.5, Haiku 4.5, GPT-6 Luna, Anthropic, OpenAI, effort setting, Computer Use, Cloud API

00:00 Anthropic just released Haiku 5.5 and they are calling it the cheapest, fastest, and most capable small model they have ever released. For most requests, it’s about 10 times cheaper than Haiku 4.5. Input now costs 10 cents per million tokens, which is the same price as OpenAI’s GPT-6 Luna. Above 100,000 tokens, you only get half off. And Anthropic’s own estimate is about 75% cheaper on average. Now, the weird thing is that this is

00:31 also much more capable. On computer use, Anthropic says its scores went from 16% to 72%. It’s also the first Haiku with an effort setting, which lets you trade cost for intelligence. Anthropic is positioning it as the workhorse model for Opus and Sonnet, doing things like summarization, classification, and sub-agent jobs. But, they also say the bigger models are still better choices for complex, agentic coding. With the last few releases, Anthropic has really

01:02 been pushing efficiency, which was the main selling point of OpenAI up till now. So, the competition is really heating up. Now, if you are on Max or Team Plan, you are also getting monthly API credits starting this week, which is pretty incredible. It is available today on Cloud API and all three major clouds. So, we’re going to see how good this model is in practice.