Compare GPT-5.5, GPT-5.4 nano, Claude Opus 4.8, Gemini 3.5 Flash, and 12+ models. Enter your token volume and request count — see exact monthly costs. Free, no signup, runs in your browser.
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All prices in USD per 1 million tokens, pay-as-you-go, June 2026. Source: official provider pricing pages.
| Model | Input / 1M | Output / 1M | Context | Best for |
|---|---|---|---|---|
| GPT-5.4 nanoCHEAPEST | $0.20 | $1.25 | 1M | Ultra-high volume, lowest cost |
| Gemini 3.1 Flash-Lite | $0.25 | $1.50 | 1M | High-volume budget workloads |
| o4-mini | $0.55 | $2.20 | 200K | Budget reasoning tasks |
| GPT-5.4 mini | $0.75 | $4.50 | 1M | Cost-efficient production |
| Claude Haiku 4.5 | $1.00 | $5.00 | 200K | Quality on a budget |
| Gemini 3.5 Flash | $1.50 | $9.00 | 1M | Frontier speed, long context |
| Gemini 3.1 Pro | $2.00 | $12.00 | 1M | Flagship reasoning |
| Claude Sonnet 4.6 | $3.00 | $15.00 | 1M | Coding & reasoning balanced |
| GPT-5.4 | $2.50 | $15.00 | 1M | Balanced flagship quality |
| Claude Opus 4.8 | $5.00 | $25.00 | 1M | Complex agentic tasks |
| GPT-5.5 | $5.00 | $30.00 | 1M | Flagship intelligence |
| Claude Fable 5 | $10.00 | $50.00 | 1M | Long-running agents |
OpenAI, Anthropic, and Google all offer batch processing at 50% discount for async workloads (up to 24h latency). Zero quality difference.
Monthly cost for common production workloads.
| Workload | Volume / month | GPT-5.4 nano | GPT-5.4 mini | Claude Sonnet 4.6 |
|---|---|---|---|---|
| Customer support bot | 150K req · 800+200 tok | $61.50 | $225 | $810 |
| Document summarizer | 15K docs · 4K+600 tok | $23.25 | $85.50 | $315 |
| Code review assistant | 6K req · 3K+1K tok | $11.10 | $40.50 | $144 |
| RAG Q&A system | 60K req · 2K+400 tok | $54 | $198 | $720 |
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A customer support bot with a 500-token system prompt, 200-token average user message, and 300-token responses running 1,000 requests/day: on GPT-5.4 nano ($0.20 input / $1.25 output per 1M), that's $0.00014 per request × 30,000 requests/month = $4.20/month. The same workload on Claude Sonnet 4.6 would be $27/month — a 6.4× difference.
How we source and maintain the pricing data in this calculator.
Source: All prices shown are official, published pay-as-you-go rates from OpenAI's API pricing page, Anthropic's pricing page, and Google AI's pricing documentation, as of June 2026. We do not estimate, interpolate, or infer rates — every figure has a direct source URL we verify before publishing.
Update cadence: We review all pricing monthly and update within 48 hours of any provider announcement. When a price change is confirmed, we update both the static HTML table and the /pricing-data.json file that powers the interactive calculator simultaneously, so both surfaces stay in sync.
What we include: Standard, on-demand (pay-as-you-go) rates only. Enterprise contracts, committed-use discounts, and academic grants are not included because they vary by negotiation and are not available to all developers.
What we exclude: Batch API discounts (50% off for async jobs) and prompt caching discounts (up to 90% off on Anthropic cached tokens) are excluded from the base calculation but documented in the tips and FAQ sections. We list context window sizes for reference because exceeding a model's context limit causes an error — not an additional charge.
Accuracy disclaimer: LLM API pricing changes frequently. Always verify current rates directly with the provider before signing contracts or publishing cost estimates to investors or customers.
GPT-5.5 costs $5.00 per million input tokens and $30.00 per million output tokens (pay-as-you-go, June 2026). For budget workloads, GPT-5.4 nano at $0.20/$1.25 per 1M is OpenAI's cheapest option — 25× less on input.
GPT-5.4 nano at $0.20/$1.25 per million tokens is the cheapest capable OpenAI model in 2026. Gemini 3.1 Flash-Lite ($0.25/$1.50) is Google's budget option. Both handle high-volume tasks well.
Claude Sonnet 4.6 costs $3.00 per million input tokens and $15.00 per million output tokens via the Anthropic API. It offers 1M token context and excels at coding and reasoning tasks.
Use cheaper models (GPT-5.4 nano vs GPT-5.5 is 25× cheaper on input). Enable Batch API for 50% off async workloads. Cache repeated prompts (Anthropic: 90% off cached tokens). Trim system prompts. Set max_tokens explicitly to avoid unnecessarily long responses.
A token is roughly 4 characters or 0.75 words. A 1,000-word document is approximately 1,333 tokens. Most LLM APIs charge separately for input (prompt) and output (completion) tokens. Code and non-English text tend to use more tokens per word.
Yes — OpenAI, Anthropic, and Google all offer 50% discounts for asynchronous batch processing (results returned within 24 hours). The trade-off is latency: batch jobs are not suitable for real-time user interactions. They are ideal for overnight document processing, dataset annotation, report generation, and embeddings computation where speed is not critical.
Anthropic's prompt caching lets you mark the beginning of your prompt (system prompt, document context) as cacheable. On subsequent requests that reuse that exact prefix, cached input tokens cost 90% less. At Claude Sonnet 4.6's $3.00/1M input rate, cached tokens cost $0.30/1M. This is most valuable when your system prompt is 500+ tokens and consistent across requests — common in RAG and agent applications.
Generating a token requires sequential forward passes through the model (each token depends on the previous one), which is computationally intensive and cannot be parallelized. Reading input tokens (the "prefill" phase) can be parallelized across the GPU, making it far cheaper per token. This is why output tokens are typically 4–10× the price of input tokens across all major providers.