IBM: Granite 4.2 8B

Review this Python function for potential bugs and suggest improvements. Focus on edge cases and performance: def calculate_discount(price, quantity): if quantity > 10: return price * 0.9 return price

This function has several issues worth addressing: 1. **Missing quantity multiplication**: The function returns a per-unit price but doesn't multiply by quantity, which likely isn't the intended behavior for a total calculation. 2. **Edge case handling**: No validation for negative prices or quantities, which could produce nonsensical results. 3. **Floating-point precision**: Direct multiplication by 0.9 can introduce rounding errors for currency. Improved version: ```python from decimal import Decimal def calculate_discount(price, quantity): if price < 0 or quantity < 0: raise ValueError("Price and quantity must be non-negative") total = Decimal(str(price)) * quantity if quantity > 10: total *= Decimal('0.9') return float(total) ```

Granite 4.2 8B demonstrates solid code review capabilities with attention to practical concerns like floating-point precision and input validation. The 131K token context window supports reviewing entire codebases in one pass. However, at 8B parameters, explanations can be more mechanical than conversational compared to larger models, prioritizing correctness over pedagogical depth.

Summarize this quarterly earnings report into three key takeaways for executive leadership. Focus on actionable insights: [Report excerpt: Revenue up 12% YoY to $45M, customer churn increased from 3.2% to 4.8%, new product line contributed $3M, operating expenses grew 18% due to expanded sales team...]

**Key Takeaways for Leadership:** 1. **Revenue growth is masking retention problems**: While top-line revenue grew 12% YoY, the 50% increase in churn rate (3.2% → 4.8%) indicates customer satisfaction issues that will compound if unaddressed. This suggests new customer acquisition is outpacing retention efforts. 2. **New product validation with scaling questions**: The new product line's $3M contribution (6.7% of total revenue) shows market fit, but we need clarity on unit economics and whether this justifies the 18% OpEx increase. 3. **Sales efficiency declining**: Operating expenses grew faster than revenue (18% vs 12%), driven by sales team expansion. This suggests either longer sales cycles or diminishing returns on headcount additions—requires immediate pipeline analysis.

This example highlights Granite's enterprise-focused training, producing structured business analysis with quantitative reasoning. The $0.06/$0.25 per Mtok pricing makes it cost-effective for high-volume document processing. The output stays factual and avoids speculation, though some users may want more interpretive depth or strategic recommendations beyond the data presented.

I'm writing technical documentation for a REST API. Convert this internal note into clear API documentation: "The /users endpoint takes a POST with email and name. Returns user object with id. Fails if email exists already or is invalid format."

## Create User Creates a new user account in the system. **Endpoint:** `POST /users` **Request Body:** ```json { "email": "string (required)", "name": "string (required)" } ``` **Success Response (201 Created):** ```json { "id": "string", "email": "string", "name": "string" } ``` **Error Responses:** - `400 Bad Request`: Invalid email format - `409 Conflict`: Email address already registered **Example Request:** ```bash curl -X POST https://api.example.com/users \ -H "Content-Type: application/json" \ -d '{"email":"user@example.com","name":"Jane Doe"}' ```

Granite 4.2 8B excels at structured technical writing, converting informal notes into standardized documentation formats. The model follows common API documentation conventions without over-engineering. With a 131K context window, it can process entire API specifications for consistency checks. The output is workmanlike and clear, though it won't add creative examples or anticipate edge cases not mentioned in the source material.

When Granite 4.2 8B handles employee knowledge-base queries at $0.06/Mtok

A 12-person SaaS startup needs to answer employee questions about internal processes, HR policies, and product specs without burning through their AI budget. Granite 4.2 8B wins here because the 131k context window swallows entire policy documents and the $0.06 input rate means you can embed full manuals in every query for under $8/million tokens. Most internal Q&A runs under 2k output tokens, so you're paying $0.25/Mtok on responses that rarely exceed a few paragraphs. The model handles straightforward retrieval and summarization without the overkill of frontier models that cost 10-50x more per input token. If your queries demand complex reasoning over the docs (not just lookup), you'll hit accuracy limits and need to step up. For teams processing 500-2000 internal questions per month where the answer is in the text, this is the price-performance floor.

Using Granite 4.2 8B to classify user-generated content overnight

A community platform with 40k daily posts needs to flag policy violations before human moderators review them each morning. Granite 4.2 8B fits because moderation is a classification task (safe/unsafe, category tagging) where you're processing text in bulk, not real-time. The 131k window lets you include the full content policy and 10-15 example posts in each prompt for few-shot accuracy. At $0.06 input and $0.25 output, you're spending roughly $3-5 per 100k posts if each decision averages 1.5k input and 50 tokens output. The model handles binary and multi-class decisions well when the rules are explicit. If you need nuanced judgment calls (sarcasm detection, cultural context), accuracy drops and you'll route edge cases to a larger model. For overnight batch jobs where speed isn't critical and the policy is documented, this is the cheapest way to triage before human review.

When Granite 4.2 8B summarizes hour-long meetings for under $0.02 each

A 20-person consulting firm records client calls and needs same-day summaries with action items and key decisions. Granite 4.2 8B works because an hour-long transcript runs 8k-12k tokens, well within the 131k window, and the output (a 300-500 word summary) costs $0.0075-0.0125 at $0.25/Mtok. Input cost is $0.0048-0.0072 per transcript at $0.06/Mtok, so you're under $0.02 per meeting. The model extracts action items and decisions accurately when the transcript is clean and the meeting follows a structure (agenda, Q&A, wrap-up). If your calls are unstructured brainstorms or require reading between the lines, summaries miss nuance and you'll need a reasoning-focused model. For firms running 100-500 meetings per month where the goal is structured notes, not strategic insight, this is the unit economics that make AI transcription profitable.