Openrouter pricing basics
Skill jeremylongshore/claude-code-plugins-plus-skills/skills/.curated/openrouter-pricing-basics
'Understand OpenRouter pricing, calculate costs, and optimize spend. Use when budgeting, comparing model costs, or tracking spend. Triggers: ''openrouter pricing'', ''openrouter cost'', ''model pricing'', ''openrouter budget'', ''how much does openrouter cost''.From its SKILL.md
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SKILL.md
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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 asOPENROUTER_API_KEY— see theopenrouter-install-authskill for setup curlandjqfor the model-pricing and credit-balance queries- Python 3.8+ with the OpenAI SDK plus the
requestspackage 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
:freemodel
Instructions
- Read How Pricing Works: prepaid credits (5.5% fee, $0.80 minimum) are drawn down per request as
(prompt_tokens * prompt_rate) + (completion_tokens * completion_rate). - Query per-token rates via
GET /api/v1/modelsper Query Model Pricing, and place candidate models in the Cost Tiers table (free → premium). - Estimate spend before committing: run
estimate_cost()from Calculate Request Cost with your expected prompt/completion token counts. - After sending real traffic, fetch the exact charge with
GET /api/v1/generation?id=per Track Actual Cost Per Request. - Watch the balance via
GET /api/v1/auth/keyper Check Credit Balance, and enable auto-topup for production keys. - Cut costs with the
:floorand:freevariants per Save Money with Variants, and check Special Pricing for reasoning tokens, image inputs, per-request fees, and BYOK.
How Pricing Works
- Buy credits at openrouter.ai/credits (5.5% fee, $0.80 minimum)
- Each request deducts
(prompt_tokens * prompt_rate) + (completion_tokens * completion_rate) - Check balance via
GET /api/v1/auth/keyor the dashboard - 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)
| Tier | Example Model | Prompt/1M | Completion/1M | Use Case |
|---|---|---|---|---|
| Free | google/gemma-2-9b-it:free | $0.00 | $0.00 | Testing, prototyping |
| Budget | meta-llama/llama-3.1-8b-instruct | $0.06 | $0.06 | Simple Q&A, classification |
| Mid | openai/gpt-4o-mini | $0.15 | $0.60 | General purpose |
| Standard | anthropic/claude-3.5-sonnet | $3.00 | $15.00 | Complex reasoning, code |
| Premium | openai/o1 | $15.00 | $60.00 | Deep reasoning |
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
| Item | Pricing |
|---|---|
| Reasoning tokens | Charged as output tokens at completion rate |
| Image inputs | Per-image charge listed in pricing.image |
| Per-request fee | Some models charge a flat fee per request (pricing.request) |
| BYOK | First 1M requests/month free; then 5% of normal provider cost |
| Free model limits | 50 req/day (free users), 1000 req/day (with $10+ credits) |
Output
- A per-model pricing record from the models API:
prompt_per_M,completion_per_M,context(e.g. $3 / $15 per 1M tokens foranthropic/claude-3.5-sonnet) - A pre-request dollar estimate from
estimate_cost()and the exact post-request figures from the generation endpoint:total_cost,tokens_prompt,tokens_completion - A credit-balance snapshot from
/api/v1/auth/key:credits_used,credit_limit,remaining,is_free_tier
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
| HTTP | Cause | Fix |
|---|---|---|
| 402 | Insufficient credits | Top up at openrouter.ai/credits or use :free model |
| 402 | Key credit limit reached | Increase key limit or use a different key |
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
:floorvariant to automatically pick the cheapest provider - Route simple tasks to budget models and complex tasks to premium models (see openrouter-model-routing)
- Set
max_tokenson every request to cap completion cost - Enable auto-topup to prevent service interruptions in production
References
- Examples | Errors
- Pricing | Credits | Models API
What ships with it: 7 files
8.5 KB alongside SKILL.md
references/
- cost-comparison-tool.md1006 B
- cost-optimization.md995 B
- credit-system.md713 B
- errors.md478 B
- examples.md3.5 KB
- model-pricing-tiers.md1.2 KB
- monitoring-costs.md732 B
Gives 0 of the 12 instructions most pricing monetisation skills give in ~2.1k tokens
Counted across 324 of the 339 authors here whose files we hold, read 2026-09-06
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Said here and by no other author read
- Calculate cost from prompt and completion token rates
- Query per-token rates from the models endpoint
- Place candidate models into cost tiers
- Estimate spend before sending real traffic
- Fetch exact charge from the generation endpoint
- Monitor credit balance via the auth key endpoint
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.