OpenRouter Pricing Basics

Overview

OpenRouter charges per token with separate rates for prompt (input) and completion (output) tokens. Prices are listed per token in the models API (multiply by 1M for per-million rates). Credits are prepaid with a 5.5% processing fee ($0.80 minimum). Free models are available for testing and low-volume use.

Prerequisites

• An OpenRouter API key (sk-or-v1-...) exported as OPENROUTERAPIKEY — see the openrouter-install-auth skill for setup
• curl and jq for the model-pricing and credit-balance queries
• Python 3.8+ with the OpenAI SDK plus the requests package for the cost-calculation and generation-endpoint snippets
• Prepaid credits for paid models — the public models/pricing endpoint needs no auth, but real completions require credits or a :free model

Instructions

1. Read How Pricing Works: prepaid credits (5.5% fee, $0.80 minimum) are drawn down per request as (prompttokens promptrate) + (completiontokens completionrate).
2. Query per-token rates via GET /api/v1/models per Query Model Pricing, and place candidate models in the Cost Tiers table (free → premium).
3. Estimate spend before committing: run estimate_cost() from Calculate Request Cost with your expected prompt/completion token counts.
4. After sending real traffic, fetch the exact charge with GET /api/v1/generation?id= per Track Actual Cost Per Request.
5. Watch the balance via GET /api/v1/auth/key per Check Credit Balance, and enable auto-topup for production keys.
6. Cut costs with the :floor and :free variants per Save Money with Variants, and check Special Pricing for reasoning tokens, image inputs, per-request fees, and BYOK.

How Pricing Works

1. Buy credits at openrouter.ai/credits (5.5% fee, $0.80 minimum)
2. Each request deducts (prompttokens promptrate) + (completiontokens completionrate)
3. Check balance via GET /api/v1/auth/key or the dashboard
4. Auto-topup is available to prevent service interruption

Query Model Pricing

# Get pricing for all models
curl -s https://openrouter.ai/api/v1/models | jq '.data[] | select(.id == "anthropic/claude-3.5-sonnet") | {
  id: .id,
  prompt_per_M: ((.pricing.prompt | tonumber) * 1000000),
  completion_per_M: ((.pricing.completion | tonumber) * 1000000),
  context: .context_length
}'
# → { "id": "anthropic/claude-3.5-sonnet", "prompt_per_M": 3, "completion_per_M": 15, "context": 200000 }

Cost Tiers (Representative)

Calculate Request Cost

def estimate_cost(model_id: str, prompt_tokens: int, completion_tokens: int) -> float:
    """Calculate cost for a single request."""
    import requests
    models = requests.get("https://openrouter.ai/api/v1/models").json()["data"]
    model = next((m for m in models if m["id"] == model_id), None)
    if not model:
        raise ValueError(f"Model {model_id} not found")

    prompt_rate = float(model["pricing"]["prompt"])       # Cost per token
    completion_rate = float(model["pricing"]["completion"])
    return (prompt_tokens * prompt_rate) + (completion_tokens * completion_rate)

# Example: Claude 3.5 Sonnet, 1000 prompt + 500 completion tokens
cost = estimate_cost("anthropic/claude-3.5-sonnet", 1000, 500)
print(f"Estimated cost: ${cost:.6f}")  # ~$0.0105

Track Actual Cost Per Request

import requests

# Method 1: From response usage (estimate)
response = client.chat.completions.create(
    model="anthropic/claude-3.5-sonnet",
    messages=[{"role": "user", "content": "Hello"}],
    max_tokens=100,
)
# response.usage.prompt_tokens, response.usage.completion_tokens

# Method 2: Query generation endpoint (exact cost from OpenRouter)
gen = requests.get(
    f"https://openrouter.ai/api/v1/generation?id={response.id}",
    headers={"Authorization": f"Bearer {os.environ['OPENROUTER_API_KEY']}"},
).json()
print(f"Exact cost: ${gen['data']['total_cost']}")
print(f"Tokens: {gen['data']['tokens_prompt']} prompt + {gen['data']['tokens_completion']} completion")

Check Credit Balance

curl -s https://openrouter.ai/api/v1/auth/key \
  -H "Authorization: Bearer $OPENROUTER_API_KEY" | jq '{
    credits_used: .data.usage,
    credit_limit: .data.limit,
    remaining: ((.data.limit // 0) - .data.usage),
    is_free_tier: .data.is_free_tier
  }'

Save Money with Variants

# :floor variant picks the cheapest provider for a model
response = client.chat.completions.create(
    model="anthropic/claude-3.5-sonnet:floor",  # Cheapest provider
    messages=[{"role": "user", "content": "Hello"}],
    max_tokens=100,
)

# :free variant uses free providers (where available)
response = client.chat.completions.create(
    model="google/gemma-2-9b-it:free",
    messages=[{"role": "user", "content": "Hello"}],
    max_tokens=100,
)

Special Pricing

Output

• A per-model pricing record from the models API: promptperM, completionperM, context (e.g. $3 / $15 per 1M tokens for anthropic/claude-3.5-sonnet)
• A pre-request dollar estimate from estimatecost() and the exact post-request figures from the generation endpoint: totalcost, tokensprompt, tokenscompletion
• A credit-balance snapshot from /api/v1/auth/key: creditsused, creditlimit, remaining, isfreetier

Examples

Check remaining credits before a batch job:

curl -s https://openrouter.ai/api/v1/auth/key \
  -H "Authorization: Bearer $OPENROUTER_API_KEY" | jq '{
    credits_used: .data.usage,
    remaining: ((.data.limit // 0) - .data.usage)
  }'
# {"credits_used": 2.34, "remaining": 47.66}

Estimating first keeps surprises out: 1,000 prompt + 500 completion tokens on anthropic/claude-3.5-sonnet comes to roughly $0.0105 via estimate_cost(), and the generation endpoint then confirms the exact charge. More worked examples: references/examples.md.

Error Handling

Enterprise Considerations

• Set per-key credit limits via the dashboard or provisioning API to isolate blast radius
• Query /api/v1/generation?id= after each request for exact cost auditing
• Use :floor variant to automatically pick the cheapest provider
• Route simple tasks to budget models and complex tasks to premium models (see openrouter-model-routing)
• Set max_tokens on every request to cap completion cost
• Enable auto-topup to prevent service interruptions in production

References

• Examples | Errors
• Pricing | Credits | Models API