LLM API Pricing Comparison 2026: Costs Side by Side
LLM API pricing compared in 2026: input/output rates, caching, batch discounts, and how to model total cost across OpenAI, Anthropic, Google, and more.
Comparing LLM API pricing across providers is harder than reading a price list. Input/output splits, caching discounts, batch rates, and quality differences all change the real cost. Here's how to compare fairly in 2026.
The parts of every price
- Input price per 1M tokens.
- Output price per 1M tokens (usually 2-10x input).
- Cached input price (discount for cached prefixes).
- Batch/async price (often ~50% discount).
- Context window limits that shape real usage.
The tiers to compare
- Frontier tier: top reasoning models — highest price, highest quality.
- Mid tier: balanced models for most workloads.
- Fast/cheap tier: flash-class models for simple tasks.
How to compare for your workload
- Write down your prompt mix: lengths and output sizes.
- Model calls per month by tier.
- Apply cache and batch discounts you'd actually use.
- Estimate total monthly cost per provider.
- Factor quality: add retry/redo costs for failures.
Adjust for quality
Hidden costs to watch
- Prompt caching only helps if prefixes are stable.
- Batch discounts may not apply to interactive requests.
- Rate limits can force higher tiers or second providers.
- Retries and agent loops multiply effective cost.
FAQ
How do I compare LLM API prices?+
Model your real prompt mix, apply cache and batch discounts, and compare cost per successful task — not just listed input prices.
Why are output tokens more expensive?+
Generation is more compute-intensive than processing input, so providers price output tokens higher — typically 2-10x input.
Are batch API calls cheaper?+
Yes — most providers discount asynchronous/batch calls significantly, sometimes 50%. Only use them when latency allows.
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