AI API Pricing Comparison 2026: GPT vs Claude vs Gemini

Full Pricing Table: All Models Compared

All major AI API models ranked by output price, from cheapest to most expensive. Prices are per million tokens.

Output prices range from $0.50 (GPT-6 Luna) to $50 (Claude Fable 5.1) — a 100x difference.

Cheapest AI API for Chat Applications

Chat applications are input-heavy — each user message, system prompt, and conversation history counts as input tokens. For high-volume chat, input price matters most.

The three cheapest models by input price:

1. GPT-6 Luna — $0.10 / 1M input tokens. Processing 75,000 chat messages costs $0.20.
2. Gemini 3.5 Flash-Lite — $0.30 / 1M input tokens. Processing 75,000 chat messages costs $0.61.
3. Gemini 3.8 Flash — $0.75 / 1M input tokens. Processing 75,000 chat messages costs $1.52.

Based on 75,000 messages at ~27 tokens per message (2,025,000 total input tokens).

Best Value for Code Generation

Code generation is output-heavy — the model writes more tokens than it reads. Output price is the dominant cost factor here.

The three cheapest models by output price:

1. GPT-6 Luna — $0.50 / 1M output tokens. Generating 10,000 lines of code costs $0.05.
2. Gemini 3.5 Flash-Lite — $2.50 / 1M output tokens. Generating 10,000 lines of code costs $0.25.
3. Gemini 3.8 Flash — $3.75 / 1M output tokens. Generating 10,000 lines of code costs $0.38.

Based on 10,000 lines at ~10 tokens per line (100,000 total output tokens).

Most Affordable for Document Analysis

Document analysis is input-heavy — you feed in large amounts of text and get back relatively short summaries or answers. Input price dominates.

Cost of processing 1,000 pages across the cheapest models:

1. GPT-6 Luna — $0.10 / 1M input tokens. Processing 1,000 pages costs $0.07.
2. Gemini 3.5 Flash-Lite — $0.30 / 1M input tokens. Processing 1,000 pages costs $0.20.
3. Gemini 3.8 Flash — $0.75 / 1M input tokens. Processing 1,000 pages costs $0.50.

Based on 1,000 pages at ~667 tokens per page (667,000 total input tokens).

Real-World Cost Examples

Three concrete scenarios showing the range between the cheapest and most expensive models.

How to Choose the Right Model

Use frontier models (like GPT-6 Astra) when accuracy matters most — complex reasoning, nuanced writing, or tasks where mistakes are costly. The higher price buys measurably better performance on hard problems.

Use budget models (like GPT-6 Luna) for high-volume, routine tasks — classification, summarization, simple chat, or data extraction. They're often 10–60x cheaper and perfectly adequate for straightforward work.

Start cheap, upgrade selectively. Run your workload on a budget model first. If quality isn't sufficient, move up one tier at a time. Most teams find that 80% of their API calls work fine on balanced or budget models, with only the hardest tasks needing frontier.