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Rank every major model by your real monthly API bill.
| Model | In $/1M | Out $/1M | Monthly bill | vs cheapest | You save vs priciest |
|---|---|---|---|---|---|
| DeepSeek V4 Flash (off-peak)cheapest | $0.21 | $0.63 | $1.68 | — | $98.32 |
| GPT-5.6 Luna | $0.2 | $1.2 | $2.2 | +$0.52 | $97.8 |
| DeepSeek V4 Flash (peak) | $0.42 | $1.25 | $3.35 | +$1.67 | $96.65 |
| Gemini 3.5 Flash-Lite | $0.3 | $2.5 | $4 | +$2.32 | $96 |
| DeepSeek V4 Pro (off-peak) | $0.63 | $1.88 | $5.03 | +$3.35 | $94.97 |
| Claude Haiku 4.5 | $1 | $5 | $10 | +$8.32 | $90 |
| DeepSeek V4 Pro (peak) | $1.25 | $3.75 | $10 | +$8.32 | $90 |
| GLM-5.2 | $1.4 | $4.4 | $11.4 | +$9.72 | $88.6 |
| Gemini 3.6 Flash | $1.5 | $7.5 | $15 | +$13.32 | $85 |
| Qwen3.8 Max | $2 | $6 | $16 | +$14.32 | $84 |
| Claude Sonnet 5 | $2 | $10 | $20 | +$18.32 | $80 |
| GPT-5.6 Terra | $2 | $12 | $22 | +$20.32 | $78 |
| Gemini 3.1 Pro | $2 | $12 | $22 | +$20.32 | $78 |
| Kimi K3 | $3 | $15 | $30 | +$28.32 | $70 |
| Claude Opus 5 | $5 | $25 | $50 | +$48.32 | $50 |
| GPT-5.6 Sol | $5 | $30 | $55 | +$53.32 | $45 |
| Claude Fable 5 | $10 | $50 | $100 | +$98.32 | — |
💸 Monthly bills assume uncached input rates per 1M tokens (tokencost.app, checked 2026-08). Cached prompts run 50-90% cheaper on input and batch APIs roughly halve output costs — real bills are usually lower than these worst-case numbers.
Per-token prices mean nothing until you multiply by volume. This calculator works from your monthly token totals— prompt input and output — and lays every major provider's projected bill side by side, sorted cheapest first with the gap to the most expensive model spelled out in dollars. The same workload routinely costs 10-20× more on a frontier model than on a budget tier.
Output tokens run 3-15× the input price on nearly every model. An agent that reads a 20k-token page but writes a 2k-token answer often pays more for the answer. Trimming max_tokens, forcing concise response styles, and caching repeated system prompts are the three biggest levers — the last one alone cuts input costs 50-90% on OpenAI, Anthropic, and DeepSeek.
Rows are the current uncached list prices per 1M tokens (aggregated from tokencost.app, re-checked August 2026 — providers change prices often, so treat numbers as planning estimates). "vs cheapest" shows what switching away from the budget tier costs you; "you save vs priciest" shows what picking any row over the frontier model banks every month.
Prototype on the cheapest capable model, measure real token distributions for a week, then decide where the final route deserves premium intelligence. Most production apps end up routing 80% of traffic to a budget model and reserving the flagship for the hard 20% — this table shows exactly what that split saves.
The LLM API Cost Comparison handles llm api cost comparisondirectly in your browser. Paste or type your input, and the tool processes it instantly — no upload, no signup, no waiting. It's built for the moments when you need a quick transformation and don't want to leave your workflow.
Because the tool runs client-side, it's fast and private. Your text never touches a server, which makes it safe for sensitive content. The interface is keyboard-friendly and works on any device with a modern browser.
Common uses: people reach for this tool when they need to use a llm api cost comparison 2026, monthly openai api bill estimator, cheapest llm api per million tokens, or gpt vs claude vs gemini monthly cost.
Browser-based tools like this one have a few real advantages over installed software or manual methods:
The LLM API Cost Comparison is based on the following formula:
monthly cost = (input tokens ÷ 1M × price_in) + (output tokens ÷ 1M × price_out)
Variables: price_in = Input price per 1M tokens ($) price_out = Output price per 1M tokens ($) input tokens = Monthly prompt (input) token volume output tokens = Monthly completion (output) token volume
Per-model API bill from monthly token volumes. Prices are uncached list rates per million tokens; cached-input and batch discounts are not applied, so results are worst-case planning numbers.
Worked example: Step 1: workload = 20M input tokens and 4M output tokens per month; model priced at per 1M input and 5 per 1M output. Step 2: input cost = 20 × = 0. Step 3: output cost = 4 × 5 = 0. Result: about 20 per month at list rates — prompt caching on the input side could cut the input half substantially.
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