LLM API Pricing Comparison: Real Monthly Costs by Provider — LLM Cost Calculator

Introduction

Choosing an LLM API provider based on headline per-million-token rates is misleading. Output tokens often cost 3–10× more than input tokens, and a chatbot that generates long answers can spend 40–60% of its budget on completion tokens alone. This guide walks through a realistic llm api pricing comparison using published rates verified June 2026, so you can estimate what your workload actually costs before you ship.

How token billing works

Every major provider bills separately for input tokens (your prompt, system message, and conversation history) and output tokens (the model's reply). Cached or repeated context may qualify for discounts on some platforms, but for budgeting purposes assume full-price input on every request.

A useful rule of thumb for production chat apps: assume a 4:1 input-to-output ratio for support bots (users write short questions, models write longer answers) and a 1:1 ratio for summarization or extraction jobs. A team processing 100M input tokens and 25M output tokens per month is a mid-size SaaS workload—not enterprise scale, but large enough that model choice matters.

Monthly cost at 100M input + 25M output tokens

Using current list prices from provider documentation:

- GPT-5.5 (OpenAI): $5.00/M input + $30.00/M output → $1,250/month - Claude Sonnet 4.6 (Anthropic): $3.00/M + $15.00/M → $675/month - Gemini 3.5 Flash (Google): $1.50/M + $9.00/M → $375/month - GPT-5.4 Mini (OpenAI): $0.75/M + $4.50/M → $187.50/month - DeepSeek V4 Flash: $0.10/M + $0.20/M → $15/month - Llama 4 Scout on Groq: $0.11/M + $0.34/M → $19.50/month

The spread between frontier and budget tiers is not incremental—it is 80× at this volume. GPT-5.5 is appropriate when reasoning quality directly drives revenue; DeepSeek V4 Flash or Groq-hosted open models are rational defaults for high-volume classification, routing, or draft generation.

Provider strengths beyond price

OpenAI offers the broadest tool-calling and multimodal ecosystem; GPT-5.4 Mini at $0.75/$4.50 is their practical production tier for most apps. Anthropic leads on long-context reliability—Claude Sonnet 4.6 supports 1M tokens with strong instruction following at $3/$15. Google Gemini 3.1 Flash-Lite ($0.25/$1.50) is competitive for latency-sensitive tasks with million-token context. Groq optimizes inference speed (Llama 3.1 8B at $0.05/$0.08, 840 tokens/sec) for real-time UX. DeepSeek undercuts everyone on price while shipping million-token context on V4 Flash.

Hidden costs to include in your comparison

API list price is only part of the bill. Add these line items when comparing providers:

1. Failed and retried requests — budget 3–8% overhead for timeouts and rate-limit retries. 2. Embedding and reranking calls — RAG pipelines often spend 20–40% of total tokens on embeddings and chunk retrieval, billed separately. 3. Batch API discounts — OpenAI and others offer ~50% off for non-real-time batch jobs; if 30% of your workload is async, factor that in. 4. Data egress — pulling large responses across regions can add $0.05–$0.12/GB on cloud-hosted backends.

When to switch models

Re-evaluate your stack when monthly API spend crosses $500 or when output-token share exceeds 55% of total cost. Route simple tasks to a cheap model (GPT-5.4 Nano at $0.20/$1.25, Gemini 3.1 Flash-Lite, or DeepSeek V4 Flash) and reserve frontier models for the 10–20% of requests that need them. Use our ai model pricing calculator to model your exact input/output split before committing to a provider contract.