Qwen: Qwen3 Coder 480B A35B

Refactor this Python function to use list comprehension and type hints. The function filters a list of user dicts to return only active users with verified emails.

Here's the refactored version: ```python from typing import TypedDict class User(TypedDict): id: int email: str is_active: bool email_verified: bool def get_active_verified_users(users: list[User]) -> list[User]: return [ user for user in users if user['is_active'] and user['email_verified'] ] ``` This eliminates the explicit loop, adds type safety with TypedDict for the user structure, and makes the filtering logic more declarative. The function signature now clearly documents input and output types.

This example shows the model's strength in idiomatic Python refactoring with modern type annotations. The 262K token context window means it could handle this refactor across an entire codebase in one pass. However, at $1.80/Mtok output, generating large refactored codebases becomes expensive compared to smaller models.

Find the bug in this React component. Users report the counter increments twice on each click instead of once.

The bug is in your event handler setup. You're likely calling `setCount(count + 1)` twice in quick succession, or you have the click handler attached to both the button and a parent element, causing event bubbling to trigger it twice. If your code looks like: ```jsx <div onClick={handleClick}> <button onClick={handleClick}>Increment</button> </div> ``` Remove one handler or add `event.stopPropagation()` in the button's handler. Alternatively, if you're using `setCount(count + 1)` in an effect or callback, switch to the functional update form: `setCount(prev => prev + 1)` to avoid stale closure issues.

Demonstrates diagnostic reasoning across common React patterns. The model identifies multiple probable causes without seeing the actual code, showing strong pattern recognition from its training data. The 480B parameter count helps with nuanced framework-specific debugging, though the response could be more concise for simple bugs.

Write a SQL query to find the top 3 products by revenue in each category for Q4 2024, including the percentage of category revenue each represents.

```sql WITH quarterly_revenue AS ( SELECT p.category_id, p.product_id, p.product_name, SUM(oi.quantity * oi.unit_price) AS product_revenue FROM products p JOIN order_items oi ON p.product_id = oi.product_id JOIN orders o ON oi.order_id = o.order_id WHERE o.order_date >= '2024-10-01' AND o.order_date < '2025-01-01' GROUP BY p.category_id, p.product_id, p.product_name ), category_totals AS ( SELECT category_id, SUM(product_revenue) AS total_category_revenue FROM quarterly_revenue GROUP BY category_id ), ranked_products AS ( SELECT qr.*, ct.total_category_revenue, ROUND(100.0 * qr.product_revenue / ct.total_category_revenue, 2) AS pct_of_category, ROW_NUMBER() OVER (PARTITION BY qr.category_id ORDER BY qr.product_revenue DESC) AS rank FROM quarterly_revenue qr JOIN category_totals ct ON qr.category_id = ct.category_id ) SELECT category_id, product_id, product_name, product_revenue, pct_of_category FROM ranked_products WHERE rank <= 3 ORDER BY category_id, rank; ```

Shows the model's ability to construct complex analytical queries with window functions and CTEs. The query is production-ready with proper date filtering and percentage calculations. The large context window allows pasting entire schema definitions for more accurate queries, though the $0.22 input cost makes exploratory querying more expensive than lighter models.

When 262K context handles entire codebases in one shot

A 12-person product team needs to refactor authentication across 40 TypeScript files totaling 180K tokens. Qwen3 Coder 480B fits the entire codebase in context at once, letting you ask "update all OAuth flows to use the new token refresh pattern" without chunking or losing cross-file dependencies. At $0.22/Mtok input, loading 180K tokens costs $0.04 per session—negligible when the alternative is manual file-by-file updates or a smaller model that hallucinates imports. Output at $1.80/Mtok means a 20K-token diff runs $0.036. If your refactors regularly span 30+ files and you need accurate cross-references, this model justifies the cost. Below 15 files, a 128K-context model saves money without losing coherence.

Cost-effective API docs when output volume dominates

A 4-person dev tools startup generates OpenAPI specs into markdown reference docs—each endpoint produces 800-1200 tokens of examples, parameters, and error tables. Input is small (the spec is 12K tokens), but output is large (60K tokens for 50 endpoints). At $1.80/Mtok output, that's $0.108 per full doc build. Qwen3 Coder's output pricing sits between budget models ($0.60-1.20/Mtok) and premium options ($2.40-4.00/Mtok), making it viable for nightly builds without eating margin. The 262K context means you can include example repos and style guides in the prompt. If you're generating under 20K tokens/day, cheaper models work fine. Above 100K tokens/day output, this model's pricing becomes a line item worth optimizing.

When PR volume needs sub-$0.01 per review economics

A 25-person engineering org reviews 80 pull requests daily, each averaging 4K tokens of diff context. At $0.22/Mtok input, that's 320K tokens/day = $0.07 in input costs. If the model writes 1K tokens of feedback per PR (80K tokens/day output), that's $0.144/day output, totaling $0.214/day or $0.0027 per review. This makes automated review economically invisible compared to 15 minutes of engineer time. The model's size suggests it handles nuanced style rules and project-specific patterns without constant retraining. If your PR volume is under 20/day, the setup overhead outweighs savings. Above 60/day, this model turns review from a bottleneck into a background process that costs less than your CI runner.