Claude API vs OpenAI API pricing: Which is actually cheaper for running background agents?

Building background agents sounds like a dream. You set up a system, let it run in the background, and it happily executes complex, multi-step tasks while you sleep. But if you have ever left an autonomous agent loop running overnight on a standard API key, you have probably woken up to a rather rude awakening on your billing dashboard.

Background agents are absolute token hogs. Because they must maintain state, read long execution histories, and repeatedly reference complex system instructions, they burn through inputs at an alarming rate.

If you are trying to decide whether to build your agent infrastructure on /platforms/claude or /platforms/openai, comparing the raw per-million-token cost on a marketing page is not going to cut it. You need to understand how prompt caching, state retention, and context windows actually affect the final bill. Let us run the real-world math to see which provider is actually cheaper when your agents start looping.

The Raw Numbers: Base Token Pricing

Before we look at the clever architectural tricks that save you money, we have to look at the raw, un-cached pricing. We will compare the heavyweights—Claude 3.5 Sonnet vs GPT-4o—and the lightweight speedsters—Claude 3.5 Haiku vs GPT-4o-mini.

| Model | Input Price (per 1M tokens) | Output Price (per 1M tokens) | | :--- | :--- | :--- | | Claude 3.5 Sonnet | $3.00 | $15.00 | | GPT-4o | $2.50 | $10.00 | | Claude 3.5 Haiku | $0.80 | $4.00 | | GPT-4o-mini | $0.150 | $0.600 |

On raw numbers alone, OpenAI wins the budget war handily. GPT-4o is slightly cheaper on inputs and significantly cheaper on outputs than Claude 3.5 Sonnet. In the lightweight tier, GPT-4o-mini absolutely decimates Claude 3.5 Haiku on price, costing a fraction of Anthropic's entry-level model.

If your agent only runs single-turn, isolated tasks, you can stop reading here. Use OpenAI. But background agents do not work like that. They operate in loops, pulling in the same system prompt, the same codebase context, and the same history over and over. This is where understanding what makes these autonomous loops tick pays off.

The Real Battlefield: Prompt Caching

Prompt caching is what makes agentic workflows financially viable. Instead of paying full price every time your agent reads its own history, you cache the static parts of the prompt and read them at a massive discount.

However, Anthropic and OpenAI handle prompt caching very differently.

Anthropic's Caching Model (Explicit) Anthropic allows you to explicitly define cache breakpoints in your API calls. * **Cache Write Cost:** 1.25x the base input token price (to write the cache initially). * **Cache Read Cost:** Just 10% of the base input token price. * **Lifespan:** The cache lasts for 5 minutes, resetting every time it is read.

This means if you are using Claude 3.5 Sonnet, your read tokens drop from $3.00 per million to an incredibly cheap $0.30 per million.

OpenAI's Caching Model (Automatic) OpenAI does not make you declare breakpoints. Instead, their system automatically caches prefixes of prompts that are longer than 1,024 tokens. * **Cache Write Cost:** No extra charge (you pay standard input rates to write the cache). * **Cache Read Cost:** 50% discount on the base input token price. * **Lifespan:** Managed automatically by OpenAI’s internal caching policies (typically lasting a few minutes to hours depending on activity).

With GPT-4o, your read tokens drop from $2.50 per million to $1.25 per million.

The Math of an Agent Loop

Let us look at a realistic scenario. Imagine a background agent that runs a 10-step loop to debug a codebase.

• System Prompt + Codebase Context: 30,000 tokens (this remains constant throughout the loop).
• New User Input / Executed Code Log per step: 1,000 tokens.
• Agent Output per step: 500 tokens.

Without caching, this agent would be an absolute disaster to run, because you would pay for that 30,000-token context 10 separate times.

Running this loop on Claude 3.5 Sonnet (with Explicit Caching) 1. **Step 1:** Writes the 30k context to cache ($3.00 * 1.25 = $0.1125), plus 1k input ($0.003) and 500 output tokens ($0.0075). **Cost: $0.123** 2. **Steps 2 to 10:** Reads the 30k context from cache 9 times ($0.30 * 9 = $0.081). Plus the accumulating conversation history (averaging ~5k input tokens per turn at standard rates: ~$0.135) and output tokens ($0.0075 * 9 = $0.0675). 3. **Total Estimated Cost:** **~$0.41**

Running this loop on GPT-4o (with Automatic Caching) 1. **Step 1:** Writes the 30k context (paid at standard rates: $2.50 * 0.03 = $0.075), plus 1k input ($0.0025) and 500 output tokens ($0.005). **Cost: $0.0825** 2. **Steps 2 to 10:** Reads the 30k context from cache 9 times, discounted by 50% ($1.25 * 9 = $0.1125). Plus the accumulating context at standard rates (~$0.1125) and output tokens ($0.005 * 9 = $0.045). 3. **Total Estimated Cost:** **~$0.35**

Even with Anthropic's massive 90% caching discount, GPT-4o's cheaper output tokens and cheaper base rates keep it highly competitive in medium-sized loops.

However, if your background agent has a massive system context—say, 100,000 tokens of documentation—and runs a fast 20-step loop, the 90% discount on Claude’s read tokens begins to completely outrun OpenAI's 50% discount. In massive-context, highly-iterative agent loops, Claude 3.5 Sonnet becomes significantly cheaper than GPT-4o.

The Low-Cost Alternative: Haiku vs Mini

If you do not need the reasoning power of a frontier model, you might look at the smaller models. Here, the math shifts dramatically.

GPT-4o-mini is so incredibly cheap ($0.15 per million inputs) that even without prompt caching, it is almost always cheaper than Claude 3.5 Haiku. Anthropic's decision to price Haiku at $0.80 per million input tokens makes it a tough sell for budget-conscious developers. Unless your background agent relies heavily on Claude's specific style of tool-calling, GPT-4o-mini is the undisputed king of low-cost background loops.

The Verdict

Choosing a platform depends entirely on how your agent operates:

• Choose Claude 3.5 Sonnet if your agents use incredibly large, static context windows (e.g., uploading a 100k-token repository or database schema) and perform highly iterative, rapid loops. The 90% prompt caching discount will save you a fortune. To get started, make sure you configure your cache breakpoints properly; you can check out how to structure these inputs in our /prompts guide.
• Choose GPT-4o if your agent loops are short, output-heavy, or use dynamic contexts that change too quickly to benefit from static caching.
• Choose GPT-4o-mini if you are building simple, high-volume agents on a budget. It is practically free.

For more detailed deep dives on model architectures and how they handle tool execution, explore our /glossary or head over to the Anthropic Support and OpenAI Support sites for help with API rate-limit troubleshooting.