LLM API Token Cost Estimation
Compare token costs across OpenAI, Anthropic, Google, and Mistral. Input vs output pricing, batch discounts, and cost comparison across 8 popular models. Plan your API budget.
Why This ML Metric Matters
Why: LLM API costs scale with tokens. Understanding input vs output pricing and batch discounts helps optimize spend for chatbots, RAG pipelines, and high-volume applications.
How: Cost = (inputTokens × inputPrice + outputTokens × outputPrice) / 1M per request. Total = cost per request × numRequests × (1 - batchDiscount/100).
- ●Output typically 2–5× input cost
- ●Batch API saves 50%
- ●~4 chars/token for English
- ●8 models compared
Compare Token Costs Across OpenAI, Anthropic, Google & Mistral
Input vs output pricing, batch discounts, and cost comparison across 8 popular models. Plan your API budget.
📊 Quick Examples — Click to Load
Inputs
Cost Comparison Across Models
Input vs Output Cost Breakdown
⚠️For educational and informational purposes only. Verify with a qualified professional.
🤖 AI & ML Facts
~4 characters ≈ 1 token for English; code and non-Latin text use more tokens per character (BPE, Sennrich et al. 2016)
— BPE
GPT-4o output can cost 4× input; Claude Sonnet output is 5× input — generation is expensive
— Provider pricing
OpenAI and Anthropic offer 50% discounts for Batch API when latency is not critical
— Provider docs
Gemini 1.5 Flash is often the cheapest for high-throughput use cases at ~$0.075/1M input
📋 Key Takeaways
- • Output tokens typically cost 2–5× more than input tokens across providers
- • GPT-4o-mini and Gemini 1.5 Flash offer the lowest cost for high-volume workloads
- • Batch APIs (OpenAI, Anthropic) can save 50% on token costs for async jobs
- • RAG pipelines with large context windows: input cost dominates; optimize retrieval
- • Monitor token usage per request to avoid surprise bills; set usage caps
💡 Did You Know
📖 How It Works
1. Tokenization
Text is split into tokens (BPE). ~4 chars/token for English; prices are per million tokens.
2. Input vs Output
Input = prompt + context; output = generated text. Output is usually priced higher.
3. Per-Request Cost
Cost = (inputTokens × inputPrice + outputTokens × outputPrice) / 1,000,000.
4. Batch Discount
Providers offer 50% off for Batch API; apply (1 - discount/100) to total.
5. Scaling
Multiply by number of requests. High-volume apps benefit from cheaper models and batch discounts.
🎯 Expert Tips
Use batch APIs for 50% savings
When latency isn't critical, OpenAI and Anthropic Batch APIs cut costs in half.
Shorter prompts = lower cost
Trim system prompts and context. Use retrieval to fetch only relevant chunks.
Match model to task
Simple tasks: GPT-4o-mini or Gemini Flash. Complex reasoning: GPT-4o or Claude Sonnet.
Set usage caps
Configure hard limits in API dashboards to avoid runaway costs.
⚖️ This Calculator vs. Other Tools
| Feature | This Calculator | Provider Docs | aipricing.org | Spreadsheet |
|---|---|---|---|---|
| 8 models compared | ✅ | ❌ | ✅ | ⚠️ |
| Input vs output breakdown | ✅ | ⚠️ | ⚠️ | ✅ |
| Batch discount | ✅ | ✅ | ⚠️ | ✅ |
| Example presets | ✅ | ❌ | ❌ | ❌ |
| Cost comparison chart | ✅ | ❌ | ⚠️ | ❌ |
| Step-by-step LaTeX | ✅ | ❌ | ❌ | ❌ |
| Copy & share | ✅ | ❌ | ❌ | ❌ |
| Educational content | ✅ | ❌ | ⚠️ | ❌ |
❓ Frequently Asked Questions
How much does 1 million tokens cost?
It depends on model and input vs output. GPT-4o-mini: ~$0.15/1M input, $0.60/1M output. GPT-4o: $2.50/1M input, $10/1M output. Gemini Flash is often cheapest at ~$0.075/1M input.
Why is output more expensive than input?
Output requires autoregressive generation (token-by-token), which is compute-intensive. Input is processed in parallel. Most providers charge 2–5× more for output.
What is a token?
A token is a subword unit from BPE (Byte Pair Encoding). ~4 characters per token for English; code and non-Latin text use more tokens per character.
How do I reduce LLM API costs?
Use cheaper models for simple tasks, shorten prompts, use batch APIs (50% off), implement prompt caching (Anthropic), and set usage caps.
Does batch discount apply to all providers?
OpenAI and Anthropic offer 50% for Batch API. Google and Mistral have different programs. Check provider docs for current offers.
How accurate are these prices?
Prices change frequently. This calculator uses approximate 2025/2026 rates. Always verify with official provider pricing pages before budgeting.
What about prompt caching?
Anthropic offers prompt caching: cached reads cost ~10% of base input. Reduces cost when the same context is reused across many requests.
Which model is cheapest for high volume?
Gemini 1.5 Flash and GPT-4o-mini are typically cheapest. Llama 3 70B via Groq is competitive for open models. Compare with this calculator for your workload.
📊 LLM API Cost by the Numbers
📚 Official Sources
⚠️ Disclaimer: This calculator provides estimates for educational and planning purposes. Actual pricing varies by provider, region, and plan. Always verify current rates at OpenAI, Anthropic, Google, and Mistral before budgeting. Batch discounts and prompt caching may have eligibility requirements.