Openrouter Pricing Basics

What it does

When your agent uses it

• Comparing model costs
• Tasks that involve Model routing and gateways
• Tasks that involve Budgeting and forecasting

Example prompts

• “openrouter pricing”
• “openrouter cost”
• “model pricing”
• “/openrouter-pricing-basics”

Requirements

• Python 3
• A credential in OPENROUTER_API_KEY
• Compatibility (from SKILL.md): Designed for Claude Code
• Pre-approved tools (allowed-tools): Read, Write, Edit, Grep, Bash(python3:*), Bash(curl:*), Bash(jq:*)

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

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

What it can do on your machine

Read from SKILL.md and the folder at commit cfae287. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves these tools, so the agent can use them without asking each time:

  • Read
  • Write
  • Edit
  • Grep
  • Bash(python3:*)
  • Bash(curl:*)
  • Bash(jq:*)

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Shell commands in SKILL.md call:

  • curl
  • jq

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Hosts in commands or code, which the agent is likely to contact:

  • openrouter.ai

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names these keys or tokens, usually read from environment variables:

  • OPENROUTER_API_KEY

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

• Compatibility

  Designed for Claude Code

  From compatibility in the SKILL.md frontmatter.

Context cost

Openrouter Pricing Basics loads about 2.1k tokens when it runs, and up to ~4.3k if it reads all its reference files. Until then it costs about 69 tokens; SKILL.md has 574 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~69

When it runs · the whole SKILL.md, loaded when a task matches

~2.1k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~4.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from jeremylongshore/tons-of-skills-marketplace at commit cfae287, republished under its MIT licence (© jeremylongshore). 574 words, ~2,104 tokens.

name

openrouter-pricing-basics

description

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'.

allowed-tools

Read, Write, Edit, Grep, Bash(python3:*), Bash(curl:*), Bash(jq:*)

compatibility

Designed for Claude Code

version

1.20.0

license

MIT

author

Jeremy Longshore <jeremy@intentsolutions.io>

tags

saas, openrouter, pricing, cost-optimization

# 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 }

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

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")

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
  }'

# :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,
)

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}

© jeremylongshore, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 7 other files (references) in skills/.curated/openrouter-pricing-basics of jeremylongshore/tons-of-skills-marketplace.

• SKILL.md
• references/cost-comparison-tool.md
• references/cost-optimization.md
• references/credit-system.md
• references/errors.md
• references/examples.md
• references/model-pricing-tiers.md
• references/monitoring-costs.md

Open the folder on GitHubat commit cfae287