Select Target LLM Model
Token & Traffic Controls
Max context 128K
Max output 16K
Requests per calendar day
Multi-Model Cost Comparison Table
Same workload · 4,096 in / 1,024 out · 1,000 req/day
| Metric | OpenAI GPT-4o | OpenAI GPT-4o Mini Best Value | Anthropic Claude 3.5 Sonnet | Gemini 1.5 Pro | DeepSeek DeepSeek V3 | DeepSeek DeepSeek R1 |
|---|---|---|---|---|---|---|
| Input / 1M | $2.50 | $0.1500 | $3.00 | $1.25 | $0.2700 | $0.5500 |
| Output / 1M | $10.00 | $0.6000 | $15.00 | $5.00 | $1.10 | $2.19 |
| Cost / Request | $0.0205 | $0.0012 | $0.0276 | $0.0102 | $0.0022 | $0.0045 |
| Estimated Daily Cost | $20.48 | $1.23 | $27.65 | $10.24 | $2.23 | $4.50 |
| Estimated Monthly Cost | $614.40 | $36.86 | $829.44 | $307.20 | $66.97 | $134.86 |
LLM Cost Optimization Guide & FAQ
Practical notes on token billing, prompt caching, model routing, and context window utilization. Expand a topic to see how to lower LLM API spend without shrinking product quality.
How is LLM API cost calculated from input and output tokens?
Most providers bill separately for prompt (input) tokens and completion (output) tokens, usually as USD per 1 million tokens. Estimated request cost is (input tokens / 1,000,000 × input price) + (output tokens / 1,000,000 × output price). Multiply by daily request volume and a 30-day month to forecast spend. Output tokens are often several times more expensive than input, so long completions dominate the invoice even when prompts look modest.
How does prompt caching (context cache) cut token spend?
If the same system prompt, tools, or retrieved context is sent on every call, vendors can serve a cache hit at a discounted input rate (often 50–90% off). Enable Context Cache in this calculator to apply each model's cached input price. Caching does not discount output tokens, so you still win the most when a large static prefix is reused across thousands of daily requests. Keep the cacheable prefix stable; small edits can force cache misses and full list-price input billing.
When should I pick a cheaper model instead of a huge context window?
A 128K–2M context window only helps if you actually fill it. Shipping a 200K prompt into a flagship model is usually more expensive than retrieving less context and routing easy turns to a mini or value model (GPT-4o Mini, DeepSeek V3). Use a large-context SKU for long documents, then cap max output tokens. Compare Estimated Monthly Cost across models on the same input/output/request workload before you lock a production default.
What is context window utilization and how do I avoid wasted tokens?
Context window utilization is (input tokens + output tokens) / max context tokens. High utilization raises latency, truncation risk, and input cost; very low utilization means you may be paying flagship rates for a job a smaller window could handle. Trim boilerplate, summarize history, set a tight max_tokens, and drop unused tools. Watch the Context Window Utilization bar: stay well below 90% so the model still has room to generate, and do not pad prompts just because the window is large.