2026 LLM API Pricing Comparison - GPT-5, Claude, Gemini, DeepSeek
LLM API pricing in 2026 spans a huge range - from fractions of a cent to several dollars per million tokens. Picking the right model for each task, and the right provider to buy it from, is one of the biggest cost levers you have. This guide explains how pricing works, the rough 2026 landscape, and how relays change the math.
Note: exact prices change frequently. Use this as a framework, and always check live pricing (on the Model Square) before you commit.
How LLM pricing works
Most providers bill per token, separately for input (your prompt and context) and output (the model's response), usually quoted per million tokens. Key points:
- Output is usually more expensive than input, so long responses cost more.
- Context length matters: large prompts and history inflate input cost.
- Tiers differ widely: "mini"/"flash" models can be 10-50x cheaper than flagship tiers.
- Multipliers: gateways often express price as a multiplier on a base, per model group.
The rough 2026 landscape
Pricing tiers (directional, per million input tokens) generally look like this:
| Tier | Examples | Relative cost |
|---|---|---|
| Ultra-cheap | Open-source (Llama, Qwen), DeepSeek | Lowest |
| Cheap reasoning | DeepSeek Pro, GLM | Low |
| Mid flagship | GPT-5 tiers, Claude Sonnet, Gemini Pro | Medium |
| Premium | Claude Opus, top reasoning models | Highest |
Open-source and DeepSeek/Qwen/GLM models tend to offer the deepest discounts; flagship reasoning models sit at the top. Output tokens are typically several times the input rate across all tiers.
How relays change the math
Relay gateways buy in bulk and pass on discounts, so the effective price you pay can be 30-80% below official for many models. That means:
- A premium model on a discounted gateway can cost less than a mid-tier model at official rates.
- Cheap models get even cheaper.
- The "cheapest option" is rarely the official provider for non-open-source models.
Always compare the effective price (after discounts/multipliers), not the headline official rate.
A cost-estimation method
- Estimate tokens per request: input (prompt + context) and output.
- Multiply by per-token rates for the model and provider.
- Multiply by request volume per month.
- Add a buffer for retries and failover traffic.
Example: 1,000 input + 500 output tokens per request, 100,000 requests/month = 100M input + 50M output tokens/month. Plug in your model's effective rates to get a monthly estimate, then compare models and providers.
Practical tips to optimize spend
- Route by task: cheap models for simple work, premium for hard reasoning.
- Trim tokens: shorter prompts, capped
max_tokens, summarized history. - Use a discounted gateway to lower the base rate across all models.
- Cache repeated requests.
- Monitor cost per successful outcome, not just per token.
FAQ
Are input and output priced the same? Usually not. Output tokens are typically more expensive, so response length drives cost.
Why are relay prices lower than official? Bulk purchasing and routing efficiency, passed on as discounts. Verify the provider serves the genuine model.
Which models are cheapest in 2026? Open-source (Llama, Qwen) and DeepSeek/GLM tend to be cheapest; flagship reasoning models are most expensive. Check live rates before deciding.
See live, discounted per-model pricing on the Model Square at TokenVoke, or read the docs to start calling any model with one key.