OpenRouter Context Optimization

Overview

OpenRouter models have varying context windows (4K to 1M+ tokens). Since pricing is per-token, stuffing unnecessary context wastes money and can degrade output quality. This skill covers context window lookup, token estimation, conversation trimming, chunking strategies, and Anthropic prompt caching for large contexts.

Query Context Limits

# Check context window for specific models
curl -s https://openrouter.ai/api/v1/models | jq '[.data[] | select(
  .id == "anthropic/claude-3.5-sonnet" or
  .id == "openai/gpt-4o" or
  .id == "google/gemini-2.0-flash-001" or
  .id == "meta-llama/llama-3.1-70b-instruct"
) | {id, context_length, prompt_per_M: ((.pricing.prompt|tonumber)*1000000)}]'

Context-Aware Model Selection

import os, requests
from openai import OpenAI

client = OpenAI(
    base_url="https://openrouter.ai/api/v1",
    api_key=os.environ["OPENROUTER_API_KEY"],
    default_headers={"HTTP-Referer": "https://my-app.com", "X-Title": "my-app"},
)

# Cache model metadata at startup
MODELS = {m["id"]: m for m in requests.get("https://openrouter.ai/api/v1/models").json()["data"]}

def estimate_tokens(text: str) -> int:
    """Rough estimate: 1 token ~ 4 characters for English text."""
    return len(text) // 4

def select_model_for_context(messages: list, preferred: str = "anthropic/claude-3.5-sonnet") -> str:
    """Pick a model that fits the context, falling back to larger windows."""
    estimated_tokens = sum(len(m.get("content", "")) for m in messages) // 4

    FALLBACK_CHAIN = [
        ("openai/gpt-4o-mini", 128_000),
        ("anthropic/claude-3.5-sonnet", 200_000),
        ("google/gemini-2.0-flash-001", 1_000_000),
    ]

    # Try preferred model first
    preferred_ctx = MODELS.get(preferred, {}).get("context_length", 0)
    if estimated_tokens < preferred_ctx * 0.8:  # 80% safety margin
        return preferred

    for model_id, ctx in FALLBACK_CHAIN:
        if estimated_tokens < ctx * 0.8:
            return model_id

    raise ValueError(f"Content too large ({estimated_tokens} est. tokens)")

Conversation Trimming

def trim_conversation(
    messages: list[dict],
    max_tokens: int = 100_000,
    keep_system: bool = True,
    keep_last_n: int = 4,
) -> list[dict]:
    """Trim conversation history to fit context window.

    Strategy: Keep system prompt + last N messages.
    If still too large, reduce to last 2 messages.
    """
    system = [m for m in messages if m["role"] == "system"] if keep_system else []
    non_system = [m for m in messages if m["role"] != "system"]

    kept = non_system[-keep_last_n:]
    trimmed = non_system[:-keep_last_n] if len(non_system) > keep_last_n else []

    total_est = sum(estimate_tokens(m.get("content", "")) for m in system + kept)
    if total_est > max_tokens and keep_last_n > 2:
        kept = non_system[-2:]

    result = system + kept
    if trimmed:
        summary_note = {
            "role": "system",
            "content": f"[Previous {len(trimmed)} messages trimmed for context limits]",
        }
        result = system + [summary_note] + kept

    return result

Chunking for Large Documents

def chunk_and_process(document: str, question: str, model: str = "openai/gpt-4o-mini",
                      chunk_size: int = 8000, overlap: int = 500) -> str:
    """Process a large document in overlapping chunks, then synthesize."""
    chunks = []
    start = 0
    while start < len(document):
        chunks.append(document[start:start + chunk_size])
        start += chunk_size - overlap

    results = []
    for i, chunk in enumerate(chunks):
        response = client.chat.completions.create(
            model=model,
            messages=[
                {"role": "system", "content": f"Analyzing chunk {i+1}/{len(chunks)}."},
                {"role": "user", "content": f"Document:\n{chunk}\n\nQuestion: {question}"},
            ],
            max_tokens=1024, temperature=0,
        )
        results.append(response.choices[0].message.content)

    # Synthesize
    synthesis = client.chat.completions.create(
        model=model,
        messages=[
            {"role": "system", "content": "Synthesize these partial analyses."},
            {"role": "user", "content": f"Question: {question}\n\nResults:\n" + "\n---\n".join(results)},
        ],
        max_tokens=2048, temperature=0,
    )
    return synthesis.choices[0].message.content

Prompt Caching for Repeated Context

# Anthropic models support prompt caching -- mark large static blocks
# Subsequent requests with same cached block cost 90% less for input tokens
response = client.chat.completions.create(
    model="anthropic/claude-3.5-sonnet",
    messages=[
        {
            "role": "system",
            "content": [
                {
                    "type": "text",
                    "text": large_reference_document,  # 50K+ tokens
                    "cache_control": {"type": "ephemeral"},
                }
            ],
        },
        {"role": "user", "content": "Summarize section 3."},
    ],
    max_tokens=1024,
)
# First request: cache_creation_input_tokens at 1.25x rate
# Subsequent: cache_read_input_tokens at 0.1x rate (90% savings)

Error Handling

Enterprise Considerations

• Query /api/v1/models at startup to cache context limits -- don't hardcode (they change)
• Use max_tokens on every request to prevent runaway completion costs on large contexts
• Implement conversation trimming as middleware so all calls respect limits
• Use Anthropic prompt caching for RAG contexts that repeat across requests (90% input savings)
• Route large-context tasks to cost-effective models (Gemini Flash for 1M context at low cost)
• Monitor prompt_tokens in responses to detect context bloat before it hits limits

References

• Examples | Errors
• Prompt Caching | Models API