Best AI coding Agents with some crazy upsets | GPT 5, Grok Code Fast, Claude, Qwen 3 Coder

Summary

The video provides a comprehensive evaluation of the latest AI coding agents, including GPT5, Gro Code Fast, Kira, Coder, Augment CLI, Quint3 Coder, Warp, and more. The presenter tests models on complex, multi-file projects, analyzing instruction-following, tool-calling, environment stability, and cost-effectiveness. Key insights reveal top performers like Quint3 Coder, Warp, and GPT5, while newcomers like Augment CLI and Kira show promise. Trends indicate model knowledge, configuration, and reasoning levels are crucial, and some agents face environment challenges. Overall, the video highlights evolving AI coding capabilities, comparing performance, speed, and value to guide users in selecting the right tools for their projects.

Takeaways

• 🚀 GPT-5, Quint3 Coder, Claude Sonnet 4, and Opus 4.1 are currently the top-performing AI coding models, with very little difference in accuracy, making cost, speed, and model knowledge the deciding factors.
• 🆕 New agents like Kira, Augment CLI, and Coder have entered the market, with Augment CLI topping the newcomer chart but GPT-5 integration not fully optimized.
• 💡 Grock Code Fast is extremely fast and low-cost, making it a strong value option for light coding tasks, but struggles with tool calling and environment-specific errors.
• ⚡ Warp surprised with dramatic performance improvements, achieving top scores with Opus 4.1 and demonstrating robust auto-run capabilities.
• 🔧 Error handling and iteration loops are crucial; models like GPT-5 and Quint3 Coder self-correct effectively, while others like Grock Code Fast may produce less accurate fixes after errors.
• 🖥️ Environment sensitivity impacts performance; WSL, PowerShell, and tool configurations can cause some agents to fail or behave inconsistently.
• 📊 Scores are converging among top models, suggesting that differentiation is increasingly based on usability, speed, and framework-specific knowledge rather than raw coding ability.
• 💰 Cost and value are significant; Grock Code Fast offers a very low daily cost, whereas Opus 4.1 is expensive and mostly advantageous for planning and debugging rather than daily coding.
• 🛠️ Root Code and Open Code provide excellent flexibility for local testing, configuration, and provider selection, making them ideal for production workflows.
• 📈 GPT-5 is expected to climb further with prompt optimization, while Warp and other newcomers show rapid improvement, highlighting the evolving nature of AI coding agents.
• 📝 The speaker plans to develop a version 3 evaluation suite, focusing on larger, multi-file projects and open-sourcing portions for community contribution.
• 🌐 Personal workflow recommendations prioritize GPT-5, Claude Sonnet 4, and Quint3 Coder for primary tasks, with Warp, Augment CLI, Kira, and Grock Code Fast used selectively based on task type.

Q & A

Which AI coding models were identified as the top performers in the video?

The top-performing AI coding models mentioned are Quint3 Coder, Sonnet 4 (Claude 4), Opus 4.1, and GPT5. These models are noted for their high scores, strong instruction-following capabilities, and ability to handle complex multi-file projects.

What was surprising about the performance of Warp?

Warp showed a significant improvement compared to previous months, achieving some of the highest scores, particularly with Opus 4.1. This was unexpected as Warp had traditionally been lower on the rankings.

How did GPT5 perform in the evaluations, and what is required to maximize its performance?

GPT5 performed well, scoring around 25,570 with medium reasoning. To maximize its performance, prompts need to be carefully tailored, and iterative instruction following is important. GPT5 is expected to climb higher in rankings in future evaluations.

What are the notable strengths and weaknesses of Grocode Fast?

Grocode Fast is extremely fast and inexpensive, making it good for light coding tasks. However, it struggles with tool calling, handling errors, and some environment-specific issues, which can lead to incorrect or inefficient code changes.

What evaluation methodology did the speaker use for testing the agents?

The speaker used a rigorous evaluation methodology including unit tests, linting, static code analysis, and an LLM as a judge. Tests focused on large, multi-file projects, instruction-following, and iterative corrections to capture practical coding performance.

Which agents were identified as notable newcomers, and how did they perform?

Notable newcomers included Augment CLI (GPT5, 22,880), Kira (Sonnet 4, 25,540), and Coder (likely Quint3 Coder, 20,274). Augment CLI topped the newcomer chart, Kira was solid but essentially a VS Code clone, and Coder performed decently but lacked clear model information.

What observations were made about Cloud Code's performance?

Cloud Code's scores declined relative to prior months, falling from a top position to more middle or lower rankings (around 24,934). This drop might be due to token conservation or optimizations, but it still provides good value overall.

Why is configuration and usability important when choosing an AI coding agent?

Configuration and usability impact workflow efficiency. Agents like Root Code and Open Code are preferred because they offer easy configuration for providers, temperature, and reasoning levels. Complex configurations in other agents can slow down testing and coding processes.

What issues did the speaker encounter with environment-dependent errors?

Some agents, including Grocode Fast and Open Code, encountered environment-specific errors such as failing in WSL or PowerShell commands. These issues caused some tests to fail or agents to stop mid-task, highlighting variability in real-world coding scenarios.

What future developments did the speaker anticipate for GPT5 and Grocode Fast?

The speaker expects GPT5 to climb in performance with better prompt instructions and usage. Grocode Fast is anticipated to improve with future updates that address tool-calling issues and environment handling, potentially making it a stronger value option.

How does model knowledge influence AI coding performance according to the speaker?

Model knowledge is crucial because agents perform well with tool calling and instruction following, but they can fail when lacking understanding of a framework, language, or library. Errors may be fixed incorrectly if the model doesn’t have adequate domain knowledge.

Which agents were highlighted for handling multi-provider testing well?

Open Code and Root Code were highlighted as excellent for handling multiple providers. They allow easy selection of providers and configurations, making them reliable for testing and working with various models in different setups.