Brief Summary
This video reviews Kimi 2.5, a new language model that is significantly cheaper than existing models like Opus and GPT, while offering comparable or even superior performance in some areas. The video includes tests of Kimi 2.5's ability to generate UI code, analyze code, and implement a file upload feature, comparing its performance against other models like Opus and Claude. The review also touches on the concept of AI agents and the importance of well-defined workflows for achieving consistent results with AI models.
- Kimi 2.5 is significantly cheaper than Opus and GPT.
- Kimi 2.5 performs well in coding and multilingual tasks.
- The video tests Kimi 2.5's ability to generate UI code, analyze code, and implement a file upload feature.
- The review also touches on the concept of AI agents and the importance of well-defined workflows for achieving consistent results with AI models.
Introduction
The video introduces Kimi 2.5, a new language model that has garnered attention due to its lower cost and performance benchmarks that are very close to those of Opus, particularly in coding tasks. Kimi 2.5 even surpasses GPT 5.2 extra high in multilingual tasks. The presenter proposes to test Kimi 2.5 to evaluate its capabilities.
Comparaison des prix et performances
The video compares the pricing and performance of Kimi 2.5 with other models, particularly Opus. Kimi 2.5 is eight times cheaper than Opus for both input and output tokens. Despite the lower cost, Kimi 2.5's performance is comparable to or better than other models, including GPT. The presenter uses Open Router to gather information on the models, focusing on speed and cost.
Latence et providers Open Source
The video discusses the latency and throughput of open-source models, noting that some models can be very fast. The presenter highlights the importance of tokens per second (TPS) as a key metric for speed. Kimi 2.5 is supported by multiple providers like Nita, Jimi, Moons and Firework, with Firework achieving a throughput of 132 tokens per second with low latency.
Test de génération d UI
The presenter tests Kimi 2.5 by using it to generate UI code for a YouTube thumbnail generator application. Kimi 2.5 adheres to the instructions better than GPT, producing a clean, minimal design. The presenter notes that Kimi 2.5's output is comparable to that of Opus, which is known for its minimalist designs. The presenter adds the API key to verify that the logic of Kimi respects the needs.
Analyse du code avec Gemini
The presenter uses Kimi 2.5 to analyze code and generate a thumbnail. The presenter uses Claude to request a thumbnail with specific elements, such as a burning cloud and an orange logo. The generated result is not perfect, but Kimi 2.5 correctly uses the specified model (Gemini 3 pro image). The presenter compares the results with those obtained using Opus, noting that the prompts are not well respected by any of the models.
Concept des agents et IA
The video briefly touches on the concept of AI agents. The presenter promotes a masterclass that covers the setup of cloud code and essential concepts related to AI agents.
Test de fonctionnalité Upload
The presenter tests Kimi 2.5's ability to implement a file upload feature in a web application. The goal is to add the ability to drag and drop a screenshot into the site. The presenter uses Open Code to compare Kimi 2.5 with Opus. Kimi 2.5 successfully implements the drag-and-drop functionality, while Opus fails to do so.
Debugging et workflow de développement
The presenter debugs the file upload feature implemented by Kimi 2.5, guiding it towards the correct implementation. The presenter emphasizes the importance of a developer's role in guiding AI when coding, as well as the importance of workflow. Kimi 2.5 took 5 minutes 54 to make the feature, while Opus took 2 minutes 35, but failed to make it work.
Limites des modèles actuels
The presenter reflects on the limitations of current AI models, noting that both Kimi 2.5 and Opus failed to implement the file upload feature correctly. The presenter attributes the failures to a lack of a well-defined workflow, which would provide the AI with the necessary context to complete the task successfully. The presenter highlights that Opus was finally able to upload, but the process after upload didn't work.
Création d un SDK TypeScript
The presenter tests Kimi 2.5 by asking it to create a TypeScript SDK for a service. The presenter compares the SDK created by Kimi 2.5 with that created by Claude. Kimi 2.5's SDK includes features such as rate limiting and separate files, demonstrating a good understanding of the task.
Revue de code et bonnes pratiques
The presenter reviews the code generated by Kimi 2, noting that it followed good practices such as including a base URL and timeout. The presenter also notes that Kimi 2.5 managed rate limiting information. The presenter concludes that both models performed well in creating the SDK, with Kimi 2.5 taking longer but producing a more complete solution.
Conclusion et futur des LLM
The presenter concludes that Kimi 2.5 is a very good model, especially considering its price. It can be used in Cloud Code and Open Code via Open Router. The presenter shares the cost of using Kimi 2.5 for the tests, noting that it is not super expensive. The presenter also discusses the size of the Kimi 2 model, noting that it requires a significant amount of memory to run. The presenter expresses optimism about the future of language models, anticipating further price reductions and intelligence gains.
