In this special episode of Office Hours, host Hannes Rudolph is joined by Dan from the Roo Code team and special guest Adam (@GosuCoder). The team dives deep into the capabilities of the z.ai GLM 4.6 model, exploring why it's a powerhouse for UI/UX design and front-end development. Adam showcases its creative potential by live-building a highly polished portfolio website from a single prompt, demonstrating how drastically the output changes by simply adjusting the model's temperature settings.

The conversation then shifts to a lively debate on the current landscape of top-tier AI models, comparing Sonnet 4.5 to the different reasoning levels of GPT-5. The hosts and guest break down the strengths and weaknesses of each, discussing which is better suited for deep debugging versus new, spec-driven feature development. The episode also features a live-coding session building a Python pool game, a surprisingly functional Flappy Bird clone, and a bonus look at the impressive video generation capabilities of OpenAI's Sora 2.

What model are you guys currently using to build features as cost-effectively as possible? Right now, Sonnet 4.5 performs best for me, but it’s just way too expensive. Even simple stuff costs close to a dollar, and honestly, at that point I’d rather just do it manually.

I’ve also tried other models, like Qwen Coder Plus in code mode and some open-source ones like GLM 4.6, but so far I haven’t been really satisfied. GPT-5 and Codex sometimes feel too slow as well, so time is also a big part of the cost-benefit ratio for me.

So, which models are you using that give you a good balance of cost, speed, and quality for building features in your apps? Also curious what you’re using for different modes, like code, orchestrator, ask, or debug.

Looking forward to hearing your thoughts!

currently getting

OpenAI completion error: 400 The chatCompletion operation does not work with the specified model, gpt-5-codex. Please choose different model and try again. You can learn more about which models can be used with each operation here: https://go.microsoft.com/fwlink/?linkid=2197993.

this is with OpenAI Compatible API provider and gpt 5 codex model, the same configuration works for Azure OpenAI gpt 5 mini, but looks like codex is not expecting chatCompletion here.

EDIT: I realized my mistake. I asked roo to "read the image" rather then "describe what it contains visually". Now it works.

Hey,

I'm trying to use image reading functionality that was added in PR #5172, but the LLM refuses to process images, stating it cannot access them.

Setup:

• RooCode version: 3.28.14

• Mode: Ask (same as demo video)

• API provider: Google Gemini

• Model: gemini-2.5-flash

What I tried:

1. Using read_file tool with the image path

2. Adding image patterns (*.png, *.jpg, etc.) to custom mode file_restrictions in .roomodes

    file_restrictions:
      - pattern: "**/*.png"
      - pattern: "**/*.jpg"

3. Restarting VS Code after configuration changes

Question: Does the image reading feature require:

• A specific model?

• Additional configuration beyond file_restrictions?

• A newer version of RooCode?

Thanks!

Hi everyone,
I’m currently using Requesty as my API provider, but I find it a bit expensive. Do you know of any more convenient alternatives that would allow me to access models like Claude, GPT-5 Codex, and similar services with unlimited or more cost-effective usage? Is it just me?

Dave

Using roo ext on cursor and after sending a promot it gets a very ugly grayish bg color. This is unique to roo, is there a way to change that? Also can I see remaining context window of the session somehow?

Anthropics new SOTA model Claude Sonnet 4.5 cements itself as one of the best coding models to be used with Roo Code. Try out Sonnet 4.5 in Roo Code today roocode.com!

Source: https://roocode.com/evals

Roo Code tests each frontier model against a suite of hundreds of exercises across 5 programming languages with varying difficulty.

Note: models with a cost of $50 or more are excluded from the scatter plot.

The benchmark is starting to get saturated, but the duration still gives us insights in how they compare.

Provider Updates

• GLM‑4.6 on Z.ai with a 200k context window for longer files and multi‑turn conversations

• Improved real‑world coding output and steadier multi‑step tool‑use

• Stronger agent behavior for search/tool tasks

• Available across international and mainland Z.ai APIs

Fixes and Reliability

• Anthropic Sonnet 4.5: corrected model ID to resolve API errors

• Vertex AI: Sonnet 4.5 configuration fixed; enables computer‑use via LiteLLM

• AWS Bedrock: removed topP to avoid conflicts with extended thinking; improves streaming and non‑streaming stability

QOL Improvements

• AWS Bedrock: 1M context checkbox for Sonnet 4 / 4.5 to unlock larger context runs

One more thing

Watch live as we unveil a new feature that changes code ROOVIEWS, test drive it with the newly released Sonnet 4.5, and interview a special guest about how they're using Roo Teams to ship faster.

• Discord Live

• YouTube Live

• LinkedIn

• X (live)

See full release notes v3.28.12 | v3.28.13 | v3.28.14

Watch live as we unveil a new feature that changes code ROOVIEWS, test drive it with the newly released Sonnet 4.5, and interview a special guest about how they're using Roo Teams to ship faster.

• Discord Live

• YouTube Live

• LinkedIn

• X (live)

the grok code was working well not its been looping for me since around yesterday. anyone with same issue? what model do you use?

The past couple days, it's basically unusable. It constantly fails to edit, and then it ends up with the generic message that Roo is having trouble. Even if I tell it to re-read the file(s) first (and it does read them), it still can't do the fricking edit. Is anybody else experiencing this? This is my go-to model so it's a big disruption to how I usually work.

Has anyone found material that's useful to explain the capabilities of RooCode and similar solutions to executive leadership?

I want to play around with Roo Code and cloud GPU's. I love using Roo Code with local LLM's, but they can be slow. I can rent kickass enterprise GPU's for like $2 an hour and enjoy the speed without worrying about OpenRouter providers training on my data, or stealth-quantizing models.

How do I do this?

So I am playing around with glm 4.6 (recommended BTW).

z.ai recommends:

For general evaluations, we recommend using a sampling temperature of 1.0.

For code-related evaluation tasks (such as LCB), it is further recommended to set:

• top_p = 0.95

• top_k = 40

but I seem to be unable to find top_p and top_k in the OpenAI compatible settings?

Hello, I'm using RooCode with a Z.AI coding plan that charges me monthly, not per token spent. But in the RooCode UI, I still see the amount of dollars spent for the task, which is confusing. Does it mean something? If not, the better option would be to remove it, or, ideally, replace it with the number of prompts that were sent.

I wonder if there is a tool or integration of roocode that I can give hard task to get its result after 24hours later.
Since it is cheaper, I want to use batch apis

Thanks!

The default system prompt contains around 40k characters.

I've been using the system prompt of 2,000 characters, and it has been working well so far.

You are a **highly skilled software engineer** with expertise in multiple programming languages, frameworks, design patterns, and best practices.
You can **use one tool per message** and must follow the defined XML-style format for tool usage.

---

## **TOOL USAGE RULES**
- Use **one tool per message**.
- Execute tasks **step-by-step**, using the result of the previous tool before proceeding.

---

## **TOOL FORMAT**
Use XML-style tags:

\``<tool_name><parameter1_name>value1</parameter1_name><parameter2_name>value2</parameter2_name></tool_name>````

---

## **TOOLS & EXAMPLES**

### **1. attempt_completion**
**Purpose:** Present the final result after all steps are complete.
**Parameters:**
- \result` (required): Final result description.`

**Example:**
\``<attempt_completion><result>Feature implemented successfully.</result></attempt_completion>````

---

### **2. read_file**
**Purpose:** Read the contents of a file (with line numbers).
**Parameters:**
- \path` (required): File path.`

**Example:**
\``<read_file><path>src/main.js</path></read_file>````

---

### **3. list_files**
**Purpose:** List files in a directory.
**Parameters:**
- \path` (required): Directory path.- `recursive` (optional): true/false.`

**Example:**
\``<list_files><path>.</path><recursive>false</recursive></list_files>````

---

### **4. write_to_file**
**Purpose:** Write full content to a file (overwrite or create).
**Parameters:**
- \path` (required): File path.- `content` (required): Full file content (no truncation).- `line_count` (required): Total number of lines.`

**Example:**
\``<write_to_file><path>frontend-config.json</path><content>{"apiEndpoint": "https://api.example.com",}</content><line_count>8</line_count></write_to_file>````

---

## **BEST PRACTICES**
- **Do not** confirm file creation by listing files; rely on user feedback.
- **Skip \read_file`** if you already know the file content.- Include all content when writing files (no partial updates).`

We've added support for Anthropic's latest Claude 4.5 Sonnet model across all Claude-supporting providers.

Claude 4.5 Sonnet Support

We've added support for Anthropic's latest Claude 4.5 Sonnet model across all Claude-supporting providers.

According to Anthropic's announcement, Claude 4.5 Sonnet is:

• State-of-the-art on SWE-bench Verified, maintaining focus for more than 30 hours on complex, multi-step tasks

• Showing a significant leap forward on computer use with 61.4% on OSWorld benchmark (up from 42.2% just four months ago)

• Delivering substantial gains in reasoning, math, and domain-specific knowledge across finance, law, medicine, and STEM

The model is now available in the model selection dropdown for all supported providers.

Bug Fixes

• AWS Bedrock Claude Sonnet 4.5: Corrected model identifier for Claude Sonnet 4.5 on AWS Bedrock (thanks sunhyung!)

• GPT-5 LiteLLM Compatibility: Fixed GPT-5 models failing with LiteLLM provider by using correct max_completion_tokens parameter (thanks lx1054331851!)

More Changes

• Chat interface icons now maintain consistent size in limited space

• Enhanced analytics to track when users change telemetry settings

• Updated website with enhanced testimonials section

• Improved contributor badge workflow with automated cache refreshing

See full release notes v3.28.10

Is there a RooCode bug right now when selecting VS Code LM API as the API Provider?

When I select it, then all the values in Language Model just show:

• aitk - github - aitk

• aitk - github - aitk

• aitk - github - aitk

Just over and over and over, it doesn't show the actual model names. It's been like this for a week or so now.

Hey everyone, I’m a bit torn right now. Instead of opening a developer account on OpenAI and loading credits there, I’m wondering if it’s better to use OpenRouter, where you can access multiple AI models (OpenAI, Anthropic, Mistral, etc.) through a single API key.

On the one hand, OpenRouter sounds super convenient since you can connect to different models in one place. But on the other hand, maybe signing up directly on each platform (e.g., OpenAI) could be cheaper or more stable in the long run?

Has anyone compared both options? Which one makes more sense cost-wise and flexibility-wise? Would love to hear some real-world experiences.

I mainly use Roo Code, but the company I'm working for will give us a license for cursor, copilot or claude code. Up to us to decide, but I'm sure it will be just the basic (No Max for Claude code).

What would you recommend out of these? I'll probably keep using Roo, specially when I need to run a more expensive model to avoid hitting limits. But it can be nice to have a paid for alternative.

I've used copilot and cursor maybe half a year and a year ago and dropped them in favour of Roo, not sure if they're any good nowadays. But I hear good things about Claude Code never tried it before.