For years I’ve been trying different AI tools in my coding workflow. I’ve tried them in browsers, inside editors, with APIs, with extensions, and nothing ever clicked for me the way AgenRouter does once you pair it with a real editor like Visual Studio Code. What’s different with this setup is that you don’t just have one AI “assistant,” you have access to a whole ecosystem of models that each have different strengths and trade‑offs, and you can switch between them in context while you’re working. That’s powerful because not every task needs the same kind of model or price point, and you don’t want to rewrite code or reconfigure your environment every time you want to try a different approach.

Before I go deeper into how I use AgenRouter every day, I want to break down some of the most interesting models it lets you access and why they matter from a developer’s perspective.

One of the families you’ll see referenced a lot is Claude, developed by Anthropic, which is known for its safety‑first design and strong reasoning ability. The specific variants like claude‑haiku‑4‑5‑20251001 and claude‑sonnet‑4‑5‑20250929 are both part of the 4.5 line, but they serve slightly different needs. The Haiku variant is optimized for speed and cost‑efficiency. It’s one of the fastest in the Claude lineup and still handles reasoning, coding, and multi‑step tasks quite well, but at much lower cost and latency than the bigger models. That makes it great when you want quick suggestions, instant feedback on a snippet, or want to power high‑volume requests where speed matters more than ultra‑deep analysis. Claude Docs Sonnet 4.5, on the other hand, is designed to be the balanced, go‑to model if you want strong performance across coding, reasoning, and multitask “agent‑like” use cases — it strikes a solid balance between cost, intelligence, and capabilities. Claude Docs

Another model you might see is gpt‑5.2, which is part of the GPT‑5 series and is one of the most capable general‑purpose models available today. It has a large context window and strong reasoning, and it shows up in many benchmarks where developers use it for long‑form generation, complex multi‑step reasoning, and deep code understanding. In the ecosystem fuelled by AgenRouter, having GPT‑5.2 means you can reach for it when you’re doing a big architectural ask, detailed refactoring, or multi‑stage logic explanation that needs more depth than a lighter, faster model. Firebender Documentation

Then there’s the DeepSeek family — things like deepseek‑r1‑0528, deepseek‑v3.1, and deepseek‑v3.2. These models come from alternative research groups outside the big US cloud players, and while they don’t always top the scoring charts in every benchmark, they’re impressively cost‑efficient and flexible for certain workflows. In community testing you’ll see them hold their own on reasoning tasks and often be chosen when you want experimentation, exploration, or broad language tasks without breaking the bank. These models are especially interesting if you’re playing with multi‑language support, rapid iteration, or scaling tasks where cost versus performance is a big consideration. Reddit

There’s also the GLM series, like glm‑4.5 and glm‑4.6, which are open‑source capable and often show up as a choice when you want transparency, self‑hosting options, or the ability to tweak things on your own infrastructure. Because they’re open on some platforms and not locked behind expensive APIs, they’re very appealing if you’re building tools where you want maximum flexibility — for example, embedding models directly into local workflows, integrating with custom backend logic, or deploying in environments where cost or data privacy matters. Some developers treat GLM as the “baseline go‑to”: solid enough for many tasks, fully open, and easy to customize. Reuters

Working with AgenRouter in VS Code — especially through tools like Kilo Code — means you can pick whichever model fits what you’re doing right now. I don’t have to guess which model is best; I simply test a few right in my editor on a real problem and see how they respond. For smaller tasks like generating a UI component, checking docs, or rewriting a function for clarity, I might start with a fast and inexpensive model like Claude Haiku. For a bigger feature, a detailed bug hunt, or something that needs deep reasoning or chain‑of‑thought, I’ll switch to GPT‑5.2. When I need something open‑source or self‑hosted for my backend service, I’ll reach for GLM.

The way I use this practically is pretty simple: I keep a panel open in VS Code where I can type plain language prompts or code snippets, and AgenRouter routes the request to whichever model I specify. This means I’m not stuck copying code between browser windows and editor windows, and I’m not paying for a super powerful model on tasks that don’t need it. My editor stays my “home base,” and the AI tools stay in context with my current file, my current logic, and my current project. Over time I’ve built personal patterns about when to use which model so that I waste less time and get more actionable output rather than generic text.

Another thing I appreciate is how different models really do have different personalities in responses — not in the gimmicky sense, but in the practical way they approach problem solving. Some give concise answers, some give step‑by‑step thought processes, some jump right into code — and because I can compare right there in the editor, I learn faster about what works best for me. That’s been especially useful when I’m learning something new or wrestling with a complex design problem.

In real projects this has saved me hours. Instead of stopping mid‑flow to evaluate different AI tools, I stay in the editor and keep coding. Instead of paying a flat subscription for one model that might be overkill for simple tasks or underpowered for complex ones, I pay only for what I consume. Instead of juggling separate accounts, logs, dashboards, and rate limits across different vendors, AgenRouter becomes the single control plane where I pick models, track usage, and deploy intelligent code suggestions seamlessly.

For people coming from traditional AI workflows — where you run prompts in a browser chat, copy answers to your editor, check back, and repeat — this feels like what AI was always supposed to be: something that works where you work, adapts to your needs, and gives you options instead of limits. In practical everyday coding, that’s invaluable.

If you’re someone who likes to experiment with different models, try new approaches, or just streamline your development workflow, checking out AgenRouter with free credit from signing up through my link can make that exploration feel risk‑free and practical. The ability to mix and match models like Claude Haiku for speed, Claude Sonnet for balanced performance, GPT‑5.2 for deep reasoning, DeepSeek for cost‑efficient runs, and GLM for open custom deployment has genuinely reshaped how I build software, think about problems, and ship features.

So if you’re ready to give it a try, use this link to get started with $250 credit and jump right into a more flexible, more capable AI‑enhanced coding process: https://agentrouter.org/register?aff=mV5Z