AI Models

Best for: High-volume processing, real-time apps, budget-conscious pipelines

How: Set thinking_budget to control reasoning cost. 0 = no thinking, 24576 = max.

Example: Summarize 1000 GitHub issues per hour for a triage dashboard at ~$1.

API: Same SDK as Gemini Pro. model='gemini-2.5-flash-preview-05-20'

Best for: Pipelines, batch processing, structured data extraction, routing

How: Use for high-volume, low-complexity tasks: classification, extraction, summarization.

Example: Process 10K support tickets per hour to classify priority and extract entities.

API: api.anthropic.com — same SDK

Best for: Embeddings preprocessing, log parsing, lightweight generation

How: Same API as GPT-4.1. Best for high-volume, simple tasks where cost matters.

Example: Parse 50K structured logs per hour and extract error patterns.

API: api.openai.com — same SDK

Best for: Intent classification, entity extraction at massive scale

How: Use for routing, tagging, simple extraction where quality bar is lower.

Example: Route 1M incoming messages per day to the right service for $4 total.

API: api.openai.com — same SDK

Best for: Batch processing, structured extraction, JSON pipelines

How: Best for structured output tasks. Supports response_format: json_object. No reasoning overhead.

Example: Process RSS feeds into structured summaries for pennies per 1000 articles.

API: api.moonshot.ai — OpenAI-compatible. model='moonshot-v1-8k'

Best for: Mobile and laptop applications requiring efficient AI models

How: Integrate Gemma 4 QAT models into your application for on-device AI processing

Example: Use Gemma 4 QAT for image recognition on smartphones with low latency and power consumption

Auto-discovered from news articles.

Best for: expanding on-device AI capabilities in Microsoft Edge

How: use Prompt and Writing Assistance APIs in Microsoft Edge

Example: integrated with Microsoft Edge for on-device AI tasks

Auto-discovered from news articles.