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LLM APIs & Integration Cheat Sheet

LLM APIs & Integration Cheat Sheet

Back to Generative AI
Updated 2026-05-28
Next Topic: LLM Evaluation Cheat Sheet

LLM API integration is the process of connecting applications to large language model providers through standardized interfaces, enabling developers to leverage AI capabilities without managing infrastructure. The 2026 provider landscape spans OpenAI's GPT-5.4 family, Anthropic Claude 4.6, Google Gemini 2.5/3.1, and cost-disruptive options like DeepSeek V4 (5–10× cheaper than frontier models) and Amazon Bedrock's unified multi-model API—nearly all offering OpenAI-compatible endpoints. The Model Context Protocol (MCP) has become the de facto tool connectivity standard, while first-party agent SDKs (OpenAI Agents SDK, Claude Agent SDK, Google ADK) have made stateful multi-step agentic workflows production-grade. The key challenge lies not in calling a single API, but in building resilient, observable, and cost-effective systems that handle rate limits, fallbacks, context management, multi-provider routing, and LLM-specific security threats such as prompt injection—skills that separate prototype AI apps from production-grade deployments.

What This Cheat Sheet Covers

This topic spans 14 focused tables and 151 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.

Table 1: Major LLM API ProvidersTable 2: Authentication & API Key ManagementTable 3: SDK & Client LibrariesTable 4: API Request PatternsTable 5: Response Configuration ParametersTable 6: Message Roles & Conversation StructureTable 7: Error Handling & ReliabilityTable 8: Cost OptimizationTable 9: LLM SecurityTable 10: AI Gateways & ProxiesTable 11: Monitoring & ObservabilityTable 12: Testing & EvaluationTable 13: Advanced Integration PatternsTable 14: Agent Frameworks

Table 1: Major LLM API Providers

Knowing each provider's flagship models, pricing tier, and authentication style is the first step before writing a single line of integration code. Pricing and model names change frequently; check provider dashboards before committing architecture decisions.

ProviderExampleDescription
OpenAI API
base_url="https://api.openai.com/v1"
model="gpt-5.4"
• Flagship: GPT-5.4, GPT-5.3 Instant, GPT-5-mini
• ~1.25/10 per M tokens
• de facto industry standard
• Responses API replaces Assistants API for agentic workflows
Anthropic API
base_url="https://api.anthropic.com"
model="claude-opus-4-6"
• Flagship: Claude Opus 4.6, Sonnet 4.6, Haiku 4.5
• ~5/25 per M (Opus)
• extended thinking, computer use, MCP, and subagents built-in
• strong safety profile
Google Gemini API
base_url="https://generativelanguage.googleapis.com"
model="gemini-2.5-pro"
• Flagship: Gemini 2.5 Pro (GA), Gemini 2.5 Flash, Gemini 3.1 Pro (preview)
• 1M+ token context
• grounding via Google Search
• multimodal (text, image, audio, video)
Azure OpenAI
base_url="https://{resource}.openai.azure.com/openai/deployments/{deployment}"
• Hosts GPT-5.4 and latest OpenAI models
• enterprise compliance (SOC 2, HIPAA, GDPR)
• private networking
• same OpenAI SDK with resource-specific endpoint
Amazon Bedrock
client = boto3.client("bedrock-runtime")
model_id="anthropic.claude-sonnet-4-6"
• Unified API for Claude, Llama 3.3, Mistral, Titan, DeepSeek R1
• IAM auth
• no per-model API keys
• serverless pay-per-token
• GuardRails, Agents, Knowledge Bases built-in
xAI Grok API
base_url="https://api.x.ai/v1"
model="grok-4-1-fast"
• Grok 4 (3/15/M), Grok 4.1 Fast (2M context, 0.20/0.50/M)
• real-time X/Twitter data access
• OpenAI-compatible
Mistral AI API
base_url="https://api.mistral.ai/v1"
model="mistral-large-3"
• Mistral Large 3, Medium 3, Codestral
• EU-hosted option
• function calling, vision
• strong European data-residency story

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