The video reviews Anthropic’s Claude 4.5 Haiku model as a cost-effective and reasonably capable option for frontend and simple backend tasks, offering solid performance comparable to higher-priced models but with higher token usage and some limitations in prompt-based tool calling. While not the best or worst model, Haiku 4.5 excels in affordability and speed, making it suitable for mid-level work, though its context-gathering constraints may affect more complex applications.
The video provides an in-depth review of Anthropic’s Claude 4.5 Haiku model, highlighting that it is neither the best nor the worst model the company has produced. The presenter shares personal experience working extensively with the model, particularly in backend tasks using LoopBack 4 and frontend development with Vue3. While the model is described as “okay,” it has notable strengths and weaknesses. One standout feature is its pricing, which is exactly one-third the cost of Sonnet 4.5, making it an attractive option for cost-conscious users. However, the presenter struggled to get the model to work effectively with prompt-based tool calling in environments like Clin Root Code or Kilo Code, although it performed exceptionally well in Cloud Code.
The evaluation of Claude 4.5 Haiku was conducted primarily through Open Code, as attempts to use it in Root Code were unsuccessful. The model scored a solid 24,954 on the presenter’s evaluation scale, closely matching Sonnet 4.5 and GPT-5 CodeX, which scored 25,674 and a similar range, respectively. Despite similar performance scores, Haiku 4.5 uses significantly more tokens—about 30% more than Sonnet 4.5 and 44.8% more than GPT-5 CodeX—resulting in higher token consumption that somewhat offsets its speed advantage. Cost-wise, Sonnet 4.5 is more than double the price of Haiku 4.5, while GPT-5 CodeX falls in between, making Haiku a cost-effective choice despite its higher token usage.
The presenter also tested Haiku 4.5 in Cursor’s plan mode for a large frontend refactor involving Vue and TypeScript. The model produced a thorough and mostly successful refactor plan, earning a B+ grade for its analysis and follow-up questions. Although some TypeScript errors remained and further iteration was needed, the refactor reduced the size of a key file by about 60%, which is a significant improvement. This performance was somewhat surprising given the complexity of the task and the presenter’s initial low expectations for Haiku in planning and refactoring scenarios.
In terms of design capabilities, Haiku 4.5 demonstrated a decent sense of aesthetics, creating micro-interactions and animations that were visually appealing, though not perfect. When compared to Sonnet 4.5 on the same design prompt, the outputs were very similar, reinforcing the idea that these models share a common foundation but differ in cost and efficiency. Some demos, like a purple-themed Connect 4 game and a Calendly clone, showed mixed results—functional but with some visual or interaction flaws. Overall, Haiku 4.5 is seen as a capable model for certain frontend and simple tasks, especially when cost is a consideration.
Finally, the presenter ranks Haiku 4.5 within a broader landscape of AI coding models. For simple tasks, GLM 4.6 remains the top choice due to its low cost and good performance, with Haiku 4.5 as a strong second. For mid-complexity tasks, GPT-5 CodeX and Sonnet 4.5 lead in quality, with Haiku potentially fitting in for lighter mid-level work. High-complexity tasks are best handled by GPT-5 High or GPT-5 High CodeX. The presenter is curious to see how much mid-level work can be shifted to Haiku 4.5, especially given its speed and price advantages, but notes that its context-gathering abilities may limit its effectiveness. Overall, Haiku 4.5 is a promising, cost-effective model with some limitations, particularly in prompt-based tool calling environments.