AI API Pricing Comparison 2026: GPT-4, Claude, Gemini & More

Per-token pricing for GPT-4o, GPT-5.5, Claude 4.6 Sonnet, Claude 4.7 Opus, Gemini 2.5 Pro, DeepSeek-V4, Llama 4, and budget alternatives. Shows when each model wins on cost-per-quality. Includes auto-routing tactics that cut bills 20-40%.

AI inference is usually the largest variable cost in a software product, and per-token prices move every quarter. This 2026 comparison lays out what the leading models actually cost per million tokens, where each one wins on cost-per-quality, and the routing tactics that cut a real bill by 20–40% without a measurable quality drop.

How AI pricing works

Almost every modern model is billed per token, split into input (prompt) and output (completion) rates, quoted per million tokens. Output is typically 3–5× the input price, so output-heavy workloads (long generations, agents) cost far more than their token count suggests. A few models add separate rates for cached input, reasoning tokens, or images.

2026 per-million-token prices

Approximate published rates for the headline models (USD per 1M tokens, input / output):

Model	Input	Output	Tier
Claude 4.7 Opus			Frontier
GPT-5.5	.50		Frontier
Gemini 2.5 Pro	.25		Frontier
Claude 4.6 Sonnet			Mid
GPT-4o	.50		Mid
DeepSeek-V4 Pro	.74	.48	Value
Gemini 2.5 Flash	.30	.50	Value
DeepSeek-V4 Flash	.14	.28	Budget
Llama 4 (hosted)	.20	.60	Budget

Note the two-order-of-magnitude spread: Opus output is ~270× the price of DeepSeek-V4 Flash output. That gap is the entire reason routing matters.

Cost-per-quality, not cost-per-token

The cheapest model is rarely the cheapest solution. What matters is the cost to complete a task at acceptable quality. Three rules of thumb hold up in practice:

Frontier models for hard reasoning. Multi-step agents, novel code, ambiguous instructions — Opus and GPT-5.5 earn their price by getting it right the first time and avoiding expensive retries.
Mid-tier for most production traffic. Sonnet, GPT-4o and Gemini Pro handle the bulk of summarisation, extraction, and chat at a fraction of frontier cost.
Budget models for high-volume simple tasks. Classification, routing, short rewrites, and draft generation run perfectly on DeepSeek Flash or Gemini Flash at 1–5% of frontier cost.

Worked example: a support chatbot

Say a support bot answers 100,000 questions a month, averaging 800 input + 400 output tokens each. On Claude Opus that is roughly 80M input + 40M output = ,200 + ,000 = ,200/month. The same volume on Gemini Flash is + = /month. If 80% of questions are simple FAQs a Flash-class model answers perfectly and 20% route to Sonnet, the blended bill lands near /month — a 90% saving versus running everything on a frontier model.

Cost-optimisation tactics that work

Tier by difficulty. Cheap model first; escalate to a stronger model only when a confidence check fails.
Auto-routing. A gateway meta-model like celedog/auto-cheapest picks the lowest-cost model that clears your quality bar per request — the same tiering, without writing the router.
Cache aggressively. Models with cached-input pricing (often 10× cheaper) make reused system prompts nearly free.
Trim output. Output dominates the bill; cap max_tokens and prompt for concision.
Right-size context. Don't stuff a 100k-token context for a question that needs 2k.

Where Celedog helps

Celedog publishes a per-model rate for all 200+ models, bills pay-as-you-go from one multi-currency wallet (USD / CNY / IDR), and offers auto-routing so the cost-tiering above happens automatically. You can compare every model's live price on one page instead of opening a dozen provider pricing tabs.

Don't ask "what is the cheapest model?" Ask "what is the cheapest model that still passes my quality bar for this request?" — then let routing answer it per call.