AI API Token Price Comparison 2026: GPT-5.6, Claude Fable 5, Gemini 3.1, DeepSeek & More

The table below lists standard API prices per 1 million tokens as of July 2026. International providers price in USD; Chinese providers price in CNY. Prices reflect standard (non-batch, non-cached) API calls — batch and prompt caching can reduce costs by 20–50%.

Note: prices change frequently. Always verify on the provider's official pricing page before making budget decisions.

Model capability is the biggest driver. More powerful models require more compute per token — longer reasoning chains, larger activation sizes, and more inference steps all add cost. This is why Claude Fable 5 costs 40x more per output token than Gemini Flash-Lite.

Compute infrastructure matters too. Companies like Google own custom TPUs and large-scale data centers, which reduces their marginal cost per token. OpenAI and Anthropic rely more on third-party GPU clusters, which affects their floor pricing.

Market positioning is a deliberate choice. Anthropic targets enterprise customers who prioritize safety, accuracy, and long context. OpenAI spans the full market from consumer to enterprise. Chinese providers like DeepSeek compete aggressively on price to build market share in the domestic developer ecosystem.

Architecture differences — such as Mixture of Experts (MoE) versus dense transformers — also affect costs. MoE models activate only a subset of parameters per token, reducing compute. DeepSeek uses MoE which contributes to its low pricing.

For every model in this comparison, output tokens are 3–6x more expensive than input tokens. This matters a lot in practice: a task that generates 2,000 output tokens costs far more than a task that reads 2,000 input tokens.

Use cases like summarization, classification, and extraction are input-heavy and cheaper. Long-form content generation, code synthesis, and multi-step reasoning are output-heavy and expensive. When estimating costs, always model your expected output length, not just your prompt length.

Thinking or reasoning modes (like extended thinking in Claude or o-series in OpenAI) can generate very large output token counts through internal chain-of-thought. These modes are powerful but multiply your output costs — enable them only when accuracy gains justify the expense.

There is no single right answer — the best model depends on your task, quality requirements, volume, and budget. Here is a practical breakdown by use case.

For high-stakes tasks requiring maximum accuracy — legal analysis, medical research, complex code review, enterprise agents — Claude Fable 5 or GPT-5.6 Sol are the top choices despite their price. The cost is justified when errors are expensive.

For general-purpose application backends — customer support, document processing, RAG pipelines — GPT-5.6 Terra, Gemini 3.1 Pro, or GPT-5.6 Luna hit the best capability-to-cost ratio. These models handle most real-world tasks well at a fraction of flagship cost.

For high-volume, latency-sensitive workloads — content moderation, classification, lightweight summarization — Gemini 3.1 Flash-Lite at $0.25/$1.50 is hard to beat. Similarly, DeepSeek V4 Pro at ¥3/¥6 is the dominant choice for Chinese-language, cost-sensitive applications.

For Chinese-language applications with domestic infrastructure needs — RMB billing, low latency in China, local compliance — DeepSeek, Kimi K3, and GLM-5.2 are the practical choices. DeepSeek leads on price; Kimi K3 leads on reasoning and long context.

Batch inference cuts costs by 50% on most providers. If your tasks do not require real-time responses — offline document processing, nightly data enrichment, bulk classification — always use the batch API. The tradeoff is latency (results return within hours, not seconds).

Prompt caching reduces repeated input costs. If your prompts share a long system prompt or large context document, providers like Anthropic and OpenAI cache the prefix and charge a fraction of the normal input price for cache hits. This is particularly effective for RAG and agent systems.

Model routing saves money at scale. Use a cheap model (Gemini Flash-Lite, GPT-5.6 Luna) for simple sub-tasks and a premium model only when complexity demands it. Many production systems route 80% of queries to cheap models and 20% to expensive ones, cutting average cost by 60–70%.

Trim your prompts. Verbose system prompts, unnecessary examples, and repeated context all add input tokens. A 30% reduction in average prompt length means a 30% reduction in input costs. Use structured output (JSON mode) to minimize output verbosity.

Direct price comparison between USD and CNY models requires a currency conversion. At roughly ¥7.2 per USD (mid-2026), DeepSeek V4 Pro's input price of ¥3/1M tokens converts to approximately $0.42/1M tokens — cheaper than even Gemini Flash-Lite's $0.25 input price only in CNY terms, but competitive when accounting for domestic infrastructure benefits.

However, international models are generally inaccessible directly from mainland China without a proxy. For teams operating in China, the real comparison is between domestic options: DeepSeek (lowest price), GLM-5.2 (mid-range), and Kimi K3 (premium reasoning). Each also includes domestic billing, support in Chinese, and compliance with domestic data regulations.

For teams with a global user base, international models are the default choice. The key question is whether to use a single premium model or mix tiers based on query complexity.