AI API Cost Calculator: Compare Claude, GPT-4, Gemini Pricing 2026

The cheapest AI API for your use case is almost certainly not the one with the lowest listed price per token. Pricing pages show input and output costs per million tokens. They do not show you the model’s tendency to use more or fewer tokens for equivalent tasks, context window degradation effects, or caching economics. Once you factor those in, the price ranking often inverts.

This guide breaks down the real 2026 pricing for Claude (Anthropic), GPT-4o and GPT-4.1 (OpenAI), and Gemini 2.5 Pro (Google) — then shows you how to use our AI API cost calculator to model your specific usage and get an accurate monthly estimate before you commit to a provider.

Try it yourself: Free AI Prompt Cost Calculator — free, no signup, runs in your browser.

2026 AI API Pricing: The Actual Numbers

Prices as of May 2026. All figures are USD per million tokens unless noted.

Anthropic Claude

• Claude Opus 4.7: $5.00 input / $25.00 output
• Claude Sonnet 4.6: $3.00 input / $15.00 output
• Claude Haiku 4.5: $0.25 input / $1.25 output

Claude’s pricing includes a prompt caching feature: cached input tokens cost $0.30/MTok for Opus (94% discount) and $0.30/MTok for Sonnet (90% discount). For applications with stable system prompts — chatbots, document analysis tools, customer support agents — prompt caching can cut effective input costs dramatically.

OpenAI GPT Models

• GPT-4o: $2.50 input / $10.00 output (cached input: $1.25/MTok)
• GPT-4.1: $2.00 input / $8.00 output (cached input: $0.50/MTok)
• GPT-4.1 Mini: $0.40 input / $1.60 output
• o3: $10.00 input / $40.00 output (reasoning tokens billed separately)

OpenAI’s reasoning models (o3, o4-mini) bill for internal "thinking" tokens that are not visible in the response. A task that appears to use 2,000 output tokens may internally consume 8,000-15,000 reasoning tokens. For reasoning-heavy workloads this is often still economical, but the effective output cost is much higher than the listed rate.

Google Gemini

• Gemini 2.5 Pro: $1.25 input (up to 200K tokens) / $2.50 input (over 200K) / $10.00 output
• Gemini 2.5 Flash: $0.15 input / $0.60 output (thinking: $3.50/MTok)
• Gemini 2.0 Flash: $0.10 input / $0.40 output

Gemini’s tiered pricing for long-context requests is significant. Below 200K tokens, Gemini 2.5 Pro is among the cheapest frontier models. Above 200K, the price doubles and the economics shift. For document analysis over very long corpora, this threshold matters.

Why Listed Price Per Token Is Misleading

The metrics that actually determine your monthly bill are not listed on any pricing page.

Token efficiency variance. Different models use different numbers of tokens to produce equivalent outputs. Benchmark data from early 2026 shows GPT-4.1 using approximately 72% fewer output tokens than Claude Opus 4.7 for comparable coding tasks. At face value, Opus 4.7 output tokens ($25/MTok) vs GPT-4.1 output tokens ($8/MTok) looks like a 3x price difference. After token efficiency, the effective cost gap narrows substantially. Use our API cost estimator to model this with your actual task types.

Context accumulation in multi-turn applications. For chatbots and agents that maintain conversation history, the input token count grows with each turn. Turn 15 in a conversation includes the entire history of turns 1-14 as input context. A 30-turn customer support conversation can accumulate 40,000-80,000 input tokens by the end — making the average input cost per turn dramatically higher than the first turn. Models with cheaper long-context pricing (Gemini) or better caching (Claude, GPT-4o) have a significant advantage here.

Output-to-input ratio for your specific tasks. Most pricing comparisons assume a fixed output:input ratio (commonly 1:3 or 1:4). But real workloads vary widely. Code generation tasks produce high output:input ratios. Classification and extraction tasks produce very low ratios. At a 1:10 input:output ratio (common for code generation), output token cost dominates — Claude Haiku becomes more expensive than it appears and GPT-4o becomes more competitive than listed price suggests.

Latency costs. Not a line item on the invoice, but relevant. Slower models with lower per-token prices can be more expensive when you factor in the cost of users waiting for responses, or the infrastructure overhead of longer-running agent tasks. Time-to-first-token and total generation time vary significantly across providers and models at the same capability tier.