LLM

Large language model comparisons, benchmarks, and implementation guides for engineers

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The LLM API landscape in 2026 has standardized around the OpenAI API format, with most providers offering compatible endpoints. Understanding the core patterns — function calling, structured outputs, streaming, and prompt caching — lets you switch models with minimal code changes.

Model Selection Guide 2026

Model	Best for	Context	Price (input)
GPT-4o	General + vision + reasoning	128K	$$$
Claude 3.5 Sonnet	Code, analysis, long documents	200K	$$$
Gemini 2.0 Flash	Speed + cost + multimodal	1M	$
DeepSeek R1	Reasoning, math, STEM	128K	$
Mistral Small 3	Fast European-hosted option	32K	$
Llama 3.3 70B	Self-hosted, no data sharing	128K	Free

Universal API Pattern

from openai import OpenAI

# Works with OpenAI, Together, Groq, Ollama, LM Studio
client = OpenAI(
    api_key="your-key",
    base_url="https://api.openai.com/v1",  # swap for any provider
)

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain RAG in 2 sentences."}
    ],
    max_tokens=200,
)
print(response.choices[0].message.content)

Key API Patterns

Structured Output

from pydantic import BaseModel

class ArticleSummary(BaseModel):
    title: str
    key_points: list[str]
    sentiment: str

response = client.beta.chat.completions.parse(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Summarize: ..."}],
    response_format=ArticleSummary,
)
summary = response.choices[0].message.parsed

Streaming

with client.chat.completions.stream(model="gpt-4o", messages=[...]) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

Learning Path

API basics — chat completions, tokens, temperature, system prompts
Structured outputs — Pydantic models, JSON schema, reliable parsing
Function calling — tool definitions, multi-turn tool use
Streaming — SSE, real-time UI updates, abort handling
Prompt caching — reduce costs 80%+ on repeated system prompts
Production patterns — fallback chains, rate limiting, cost tracking

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