LLM Pricing Comparison 2026: OpenAI, Anthropic, Google, DeepSeek

Model pricing moves fast — OpenAI, Anthropic, Google, and DeepSeek adjust prices, launch tiers, and run promotions several times a year. The headline price per million tokens is only half the story; output tokens cost more than input tokens, and caching, batch, and reasoning tokens change the math entirely.

This is the 2026 pricing comparison: how the four major providers structure their pricing, where each one wins, and the decision rule for picking by task. LayerFlow's cost check shows real-dollar comparisons before you send — pricing covers model access.

How the providers structure pricing

• Input vs output split: every provider charges more for output than input, usually 3-5x.
• Tiers within a family: each family has small, mid, and frontier models with steep price gaps.
• Caching discounts: cached input tokens are dramatically cheaper on most providers.
• Batch discounts: batch APIs cut prices substantially for non-urgent work.
• Reasoning tokens: thinking models charge separately for hidden reasoning.

OpenAI and Anthropic lead with frontier quality at frontier prices, each with a competitive small-tier. Google competes hard in the mid-tier and bundles generous context. DeepSeek undercuts on price per token and has become the default cost play for high-volume work — with the usual trade-offs in latency and ecosystem maturity.

Where each provider wins per task

• Frontier reasoning, code architecture: OpenAI and Anthropic's top models — pay only for the hard 10%.
• High-volume extraction and formatting: DeepSeek or the small tiers — pennies per thousand tasks.
• Mid-tier product traffic: Google's mid models and Anthropic's Sonnet class are the sweet spot.
• Long-context retrieval work: Google's large windows reduce the need to compress.

The pattern is consistent: price follows capability, and the winners are teams that match capability to task instead of defaulting to one family.

The real math: cost per completed task

Compare models on cost per completed task, not per million tokens. A small model at a third of the price that needs 10% more runs can still win. Factor in output tokens (expensive), context bloat (input tax), and rework (hidden cost). RouteLLM evidence shows routed mixtures cut bills 40-85% at 95% of frontier quality — mixture beats any single provider.

Common mistakes

• Comparing input prices only — output is the expensive token.
• Ignoring caching and batch discounts when estimating monthly cost.
• Buying one provider for everything out of loyalty or habit.
• Forgetting reasoning tokens on thinking models.

Internal next steps

See GPT vs Claude vs Gemini vs DeepSeek 2026 and LLM APIs Pricing Comparison. For the German market, read LLM Vergleich 2026.

Compare prices on your real workload: sign in to LayerFlow and run the same task across providers with cost in view. Pricing shows model coverage.

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