10 AI Coding Agents Compared: Architectures, Benchmarks, and the Llama 4 Impact

The landscape of software development is undergoing a seismic shift. In a single week, the industry witnessed ten major milestones from the most prominent AI coding agents. From OpenHands reaching 1.0 to the launch of Devin v2 and the controversial release of Meta's Llama 4, the category of 'AI Coding Agents' has reached escape velocity. For developers and enterprises, the question is no longer whether to use these tools, but which architecture best fits their specific workflow. At n1n.ai, we provide the high-speed, multi-model infrastructure necessary to power these autonomous systems, ensuring that whether you choose a terminal-based agent or a cloud-native teammate, your underlying LLM performance remains top-tier.

The Four Architectures of Coding Agents

Every production-quality coding agent today adheres to one of four primary architectural patterns. Understanding these is critical because the architecture determines the agent's reliability, flexibility, and transparency more than the raw benchmark score of the underlying model.

1. Code-as-Action (CodeAct)

Representative: OpenHands

The CodeAct architecture, pioneered by OpenHands (formerly OpenDevin), treats code as the universal interface for tool use. Instead of relying on a pre-defined set of JSON-schema tools (like read_file or search_code), the agent writes and executes Python or Bash scripts to interact with the environment. If the agent needs to find a specific function in a 100,000-line codebase, it doesn't just call a search tool; it writes a specialized grep or AST-parsing script on the fly.

• Pros: Infinite extensibility. Any operation expressible in code is available to the agent.
• Cons: Reliability. Executing arbitrary code introduces a larger failure surface than calling typed APIs. Debugging becomes a recursive problem where the human must debug the agent's debugging script.

2. Agent-Computer Interface (ACI)

Representative: SWE-agent

Developed at Princeton, SWE-agent focuses on the interface between the LLM and the computer. Much like Human-Computer Interface (HCI) research, ACI posits that LLMs perform better when given tools designed specifically for their cognitive patterns—such as file viewers that show line numbers and editors that operate on specific line ranges rather than raw text streams. This approach has led SWE-agent to solve over 45% of issues on the SWE-bench Verified leaderboard.

3. Plan-and-Execute

Representatives: Plandex, Devin

This architecture prioritizes safety and auditability. Before a single line of code is changed, the agent generates a comprehensive plan. The human developer reviews the plan, approves or modifies it, and only then does the agent begin execution in a sandboxed environment. This is particularly useful for complex refactors where 'hallucinating' a file deletion could be catastrophic. Using a reliable API provider like n1n.ai is essential here to ensure the planning phase isn't interrupted by latency or rate limits.

4. React-and-Iterate (Standard Tool-Use Loop)

Representatives: Cline, Aider, Roo Code, Goose

This is the most common pattern, mirroring the human developer's workflow: observe the code, reason about the task, take an action, observe the result, and repeat.

• Cline 4.0: Known for its strict safety controls, requiring human approval for every file edit or terminal command. It was an early adopter of the Model Context Protocol (MCP), making it highly extensible.
• Aider: The terminal-based power user's choice. Its new 'architect mode' uses a dual-model approach, where a high-reasoning model (like Claude 3.5 Sonnet) plans the change and a faster model implements it.

The Llama 4 Launch and Benchmark Controversy

Meta recently released Llama 4 Scout (109B) and Llama 4 Maverick (400B), both utilizing a Mixture of Experts (MoE) architecture with only 17B active parameters during inference. This allows for massive model capacity with significantly lower compute costs. However, the launch was marred by controversy when it was discovered that Meta's Chatbot Arena submissions utilized a specially tuned version of the model not available in the public weights.

Independent testing suggests that while Llama 4 is innovative, it still trails behind specialized models like DeepSeek-V3 or Claude 3.5 Sonnet in complex coding tasks. For developers using agents like Cline or Aider, switching between these models via n1n.ai allows for real-time performance comparisons without changing your local setup.

Ecosystem Deep Dive: Infrastructure for Agents

The speed at which the ecosystem adopted Llama 4 is unprecedented. Within days, we saw updates across the stack:

• vLLM 0.8.4: Enabled the V1 engine by default, offering a redesigned scheduler for production throughput. It now includes native MoE support and prefix caching.
• KTransformers v0.5: This tool allows running Llama 4 Scout (109B) on a single consumer GPU (like an RTX 4090) by intelligently offloading inactive experts to system RAM using Intel AMX/AVX-512 kernels. This achieves 10-15 tokens/second—a breakthrough for local inference.
• llama.cpp: Reached the b5060 milestone, adding GGUF support for MoE architectures and new quantization methods for expert weights.

Choosing the Right Agent for Your Workflow

1. Solo Terminal Developers: Choose Aider. Its git integration and architect mode make it the fastest way to ship code from the command line.
2. IDE Power Users: Choose Cline 4.0. The combination of multi-file editing and MCP support provides the best balance of power and safety within VS Code.
3. Enterprise Teams: Choose Amazon Q Developer or Devin. These tools offer the best asynchronous task delegation and environment sandboxing required for corporate security.
4. Researchers: Choose OpenHands. Its CodeAct architecture is the most fertile ground for experimenting with autonomous agent logic.

Pro Tip: The Model Matters Less Than the Interface

While benchmarks like SWE-bench are important, they often measure a specific type of bug-fixing. In real-world development, the quality of the 'Agent-Computer Interface' (ACI) is the true bottleneck. An agent with a well-designed file-navigation tool will outperform a smarter model with a poorly designed one. When building your own agentic workflows, focus on how the information is presented to the LLM. Minimize noise, provide clear line numbers, and use structured outputs whenever possible.

As we move into the era of 'AI Team Members,' the ability to swap models and maintain high-speed connectivity becomes a competitive advantage. Developers who master these agents today will be the 10x engineers of tomorrow.

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