AI Models

Best for: Budget alternative to flagship models, Chinese language tasks

How: OpenAI SDK with base_url='https://api.moonshot.ai/v1'. WARNING: has implicit reasoning that eats max_tokens.

Example: Use moonshot-v1-8k instead for structured JSON tasks — kimi-k2.5 wastes tokens on hidden thinking.

API: api.moonshot.ai — OpenAI-compatible

Best for: Complex multi-step coding, large codebase refactors, long-document analysis

How: Best via Claude Code CLI for coding tasks. For API: messages.create() with system prompt + tools.

Example: claude-code: point it at a repo, describe the feature, it reads/edits/tests autonomously.

API: api.anthropic.com — SDK: pip install anthropic / npm i @anthropic-ai/sdk

Best for: Production API backends, real-time chat, moderate complexity coding

How: Drop-in replacement for Opus when you need faster/cheaper. Same API, just change model ID.

Example: Use as the default model in your API gateway — upgrade to Opus only for hard problems.

API: api.anthropic.com — same SDK as Opus

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: General-purpose API integration, multimodal apps, coding assistance

How: client.chat.completions.create(model='gpt-4.1', messages=[...]). Supports vision, tools, JSON mode.

Example: Build a PR review bot that reads diffs + screenshots and posts comments.

API: api.openai.com — SDK: pip install openai / npm i openai

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: Hard math, science, multi-step planning, complex debugging

How: Use reasoning_effort param: 'low'/'medium'/'high'. No system prompt — use developer message instead.

Example: Debug a distributed system deadlock by feeding it the full trace + architecture.

API: api.openai.com — same SDK, just model='o3'

Best for: Coding with reasoning, moderate-complexity math, budget reasoning

How: Cheaper reasoning model. Use when o3 is overkill but you need chain-of-thought.

Example: Generate a migration plan for a database schema change with safety checks.

API: api.openai.com — same SDK

Best for: UI mockups, marketing assets, diagrams with text

How: Supports text overlays, inpainting, and style control. Best text rendering of any model.

Example: Generate architecture diagrams with accurate labels from a text description.

API: api.openai.com — client.images.generate(model='gpt-image-1')

Best for: Long-document analysis, multimodal tasks, apps needing search grounding

How: client.models.generate_content(model='gemini-2.5-pro', contents=[...]). Supports grounding with Google Search.

Example: Feed a 200-page architecture doc and ask it to find security issues.

API: generativelanguage.googleapis.com — SDK: pip install google-genai

Best for: Tasks needing real-time information, math-heavy problems

How: OpenAI SDK with base_url override. Also supports live search via tools.

Example: Monitor real-time tech news and generate summaries using live search.

API: api.x.ai — OpenAI-compatible SDK. Set base_url='https://api.x.ai/v1'

Best for: Budget reasoning tasks, math, lightweight chain-of-thought

How: Excellent cost-to-reasoning ratio. Use reasoning_effort param.

Example: Validate Terraform plans with reasoning about dependency chains for pennies.

API: api.x.ai — same as Grok 3

Best for: High-quality image generation, product photography

How: API or self-host Flux.1 Schnell (open). Pro via API only.

Example: Generate product mockups for landing pages programmatically.

API: api.bfl.ml OR via Replicate, fal.ai

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: RAG pipelines, semantic search, document retrieval

How: Set dimensions param to reduce size (e.g., 256 for fast search, 3072 for max quality).

Example: Index your internal docs and build a search API with pgvector + this model.

API: api.openai.com — client.embeddings.create(model='text-embedding-3-large')

Best for: life sciences research

How: N/A

Example: N/A

Auto-discovered from news articles.