OpenAI vs Anthropic vs Google Gemini: Pricing Comparison 2026
Complete pricing comparison of GPT-4o, Claude Sonnet, and Gemini 2.0 for API developers. Which LLM gives the best value for your use case?
Choosing the right LLM provider isn't just about model quality - it's about cost efficiency. The pricing gap between providers is massive, and picking the wrong one for your workload can cost you thousands per month.
Here's the complete 2026 pricing breakdown for the three major providers, including when to use each one.
Raw Pricing Comparison
All prices per million tokens:
| Model | Input $/1M | Output $/1M | Context Window | Best For |
|---|---|---|---|---|
| GPT-4o | $5.00 | $15.00 | 128K | General purpose, code generation |
| GPT-4o-mini | $0.15 | $0.60 | 128K | Simple tasks, high volume |
| Claude Sonnet | $3.00 | $15.00 | 200K | Long context, analysis |
| Claude Haiku | $0.25 | $1.25 | 200K | Fast, cheap classification |
| Claude Opus | $15.00 | $75.00 | 200K | Complex reasoning, research |
| Gemini 2.0 Flash | $0.10 | $0.40 | 1M | Cheapest option, large context |
| Gemini 2.0 Pro | $1.25 | $5.00 | 1M | Balanced cost/quality |
| Mistral Small | $0.20 | $0.60 | 32K | European hosting, multilingual |
| Mistral Large | $2.00 | $6.00 | 128K | European compliance |
When to Use Each Provider
OpenAI (GPT-4o): The most versatile option. GPT-4o excels at code generation, function calling, and structured output. GPT-4o-mini is unbeatable for high-volume simple tasks at $0.15/1M input tokens.
Anthropic (Claude): Best for long-context workloads thanks to the 200K context window. Claude Sonnet offers the best quality/price ratio for complex analysis. Haiku is excellent for classification pipelines.
Google (Gemini): The budget option with the largest context window (1M tokens). Gemini 2.0 Flash at $0.10/1M input tokens is the cheapest quality model available. Ideal for RAG applications with lots of context.
Mistral: Best for teams with European data residency requirements. Competitive pricing with strong multilingual performance.
The Smart Approach: Multi-Provider Routing
The most cost-effective strategy isn't picking one provider - it's using the right model for each request. Simple classification? Gemini Flash. Complex code review? GPT-4o. Long document analysis? Claude Sonnet.
This is exactly what Promptly's smart routing does automatically. You send every request through a single OpenAI-compatible endpoint, and Promptly routes each one to the cheapest capable model based on complexity analysis.
Teams using multi-provider routing typically save 40-60% compared to using a single model for everything.
Hidden Costs to Watch
Raw per-token pricing doesn't tell the whole story. Consider: wasted tokens from verbose prompts (10-30% of spend), redundant API calls that could be cached (20-35%), context window waste from long conversations, and rate limiting costs from retry logic.
Optimizing these hidden costs can save as much as model selection itself. Prompt compression + caching + context pruning compound to reduce your effective cost far below the sticker price.
Ready to cut your LLM costs?
Promptly optimizes every API call automatically - smart routing, caching, prompt compression, and context pruning in one proxy.