LLM API Cost Comparison 2026: Per-Token Pricing for OpenAI, Claude, Gemini and DeepSeek
Token prices vary 10x between providers, and the cheapest model changes every quarter. How do the 2026 per-token prices compare for input, output and caching?
💡 What You Will Learn
Token prices vary 10x between providers, and the cheapest model changes every quarter. How do the 2026 per-token prices compare for input, output and caching?
📜 Table of Contents
The Price Per Token Game in 2026
LLM pricing is per million tokens, split into input, output, and increasingly cached input. The 2026 landscape has three tiers: frontier flagships, mid-tier workhorses, and budget models that changed the economics.
Reference Price Tiers (public list prices, 2026)
| Model tier | Input /1M | Output /1M | Notes |
|---|---|---|---|
| Flagship (GPT/Claude/Gemini top) | $3-15 | $15-75 | best quality, cached input 80-90% off |
| Mid-tier (fast models) | $0.15-1 | $0.60-8 | workhorses for most apps |
| Budget (DeepSeek, open models) | $0.07-0.55 | $0.27-2.19 | made per-token pricing a commodity |
DeepSeek in particular reset expectations: its R1 and V3 series list at a fraction of US frontier pricing while scoring near the top of open-source benchmarks. This pushed every provider to ship cheaper mid-tier models.
The Hidden Costs Beyond Per-Token
- Cached input - repeating the same system prompt can cost 10-25% of normal input price (or even less). A stable system prompt plus prompt caching can cut bills dramatically.
- Output tokens cost 3-5x input - agents that ramble cost real money; constrain max tokens.
- Tool calls and function schemas - every tool definition is re-sent with each request and counts as input.
- Multi-turn agents - history re-sent every turn makes effective cost per session much higher than the sticker price.
How to Compare Providers Honestly
- Build a representative workload: 100 real prompts with typical input/output lengths.
- Price it on each provider's calculator using actual token counts (see the tokenizer guide).
- Add caching assumptions - providers with big cache discounts win on chat workloads.
- Include quality: the cheapest provider is only cheap if its answers pass your eval set. A 10% accuracy drop usually costs more than the API savings.
The 2026 Bottom Line
For high-volume simple tasks, budget models are absurdly cheap - sometimes 20-50x cheaper than flagships. For complex reasoning, flagships still justify their price. The winning pattern: route simple queries to cheap models and hard ones to flagships (see model routing).
Check current prices on each provider's pricing page before committing - they move quarterly.
