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Claude API Pricing: Input, Output and Cache Token Facts

Claude API pricing starts with three token categories

Claude API costs are easier to control once you separate input tokens, output tokens, and cache tokens. An input token is text you send to the model: system instructions, user messages, tool definitions, conversation history, documents, and code. Output tokens are the words, code, or structured data Claude generates. Cache tokens apply when a reusable prompt prefix is stored and reused through prompt caching.

For developers using an API relay such as 59API, the same accounting principle matters: inspect the model-specific rate, then calculate each request from the input, output, and cache usage reported in the response. 59API provides pay-as-you-go access to Claude Opus, Sonnet, Haiku, and Fable models, plus GPT models, while maintaining compatibility with Claude Code, Codex, and OpenAI SDK-based applications.

Myth: Every token in a request has the same price

Fact: Input and output tokens are normally priced separately, and output is often more expensive. This is because generating a response requires sequential inference: Claude must produce one token before generating the next. Sending a long repository, policy document, or tool schema may therefore cost less than asking for a very long answer, even when the token counts look similar.

To estimate a request, use this practical formula: non-cached input tokens multiplied by the input rate, plus output tokens multiplied by the output rate, plus any cache-write or cache-read charges. The exact rate depends on the selected Claude model and the provider’s current pricing. Higher-capability models generally cost more, while faster lightweight models can be a better fit for classification, extraction, routing, and short support replies.

• Choose Haiku-class models for high-volume, lower-complexity tasks.
• Use Sonnet-class models for balanced coding and reasoning workloads.
• Reserve Opus-class models for difficult analysis where quality has clear business value.
• Set a realistic maximum output limit so verbose answers do not create surprise bills.

Myth: Prompt caching makes all future calls free

Fact: Caching reduces the cost of reprocessing an unchanged prompt prefix; it does not eliminate charges. A cache write is created when Claude processes eligible content for the first time. Later requests that match that cached prefix can be charged as cache reads. You still pay for new user content, uncached input, and every output token generated.

On native Claude-style pricing, cache reads are typically far cheaper than ordinary input tokens, while cache writes can cost more than standard input because the provider is creating the reusable cache entry. Cache duration options and exact multipliers can differ by model and provider, so developers should verify the current 59API model pricing and cache settings before forecasting spend.

Myth: Any similar prompt will hit the cache

Fact: Prompt caches depend on an identical reusable prefix. Small changes near the beginning of a request can prevent a cache hit. For example, putting a timestamp, user name, random request ID, or changing conversation text before a large cached document can force the model to process that large content again.

Build requests in a stable order. Place long, reusable material first: system rules, coding standards, product documentation, tool definitions, and large reference files. Put dynamic data after that stable prefix: the current user question, account-specific details, timestamps, and task parameters. When using cache controls, mark the end of the reusable section according to the Claude-compatible API format supported by your client.

• Keep tool schemas stable across requests.
• Do not inject changing metadata into the system prompt.
• Reuse the same document formatting and ordering.
• Track cache-read tokens separately from ordinary input tokens in logs.
• Test repeated requests to confirm that cache usage actually appears in API usage data.

Myth: Lower API cost means lower model quality

Fact: Lower cost can come from efficient routing and pay-as-you-go pricing rather than a downgraded model. 59API is designed as a low-cost relay for native official-quality Claude and GPT model access, helping teams avoid unnecessary platform overhead while keeping familiar integrations. Configure your application with the 59API base URL, https://api.59api.com, select the model suited to the task, and keep your existing OpenAI SDK or compatible workflow where appropriate.

For reliable cost control, log model name, input tokens, output tokens, cache creation tokens, cache read tokens, latency, and task success rate. Then compare cost per successful task instead of comparing token prices alone. A cheaper model that needs retries may cost more than a stronger model that solves the request on the first attempt.

If you want a practical way to test Claude pricing with your own workload, sign up for 59API, run a small benchmark across models, and use the resulting token data to set sensible defaults and budgets.

Pricing · Docs