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When you use an AI coding agent, you pay for the work behind the visible answer: model input and output across multiple calls, eligible cached input, and sometimes separate tool or runtime charges. The useful comparison is the total cost of completing the same coding task to the required quality and latency—not one model’s token price or one response.
What does a developer pay for when using an AI coding agent?
A coding agent may inspect files, run commands, receive tool results, revise its plan, and call the model again before it finishes. Each model call can incur charges. OpenAI’s agent observability guide recommends estimating usage across all calls, rather than treating the final visible answer as the whole bill.
A practical accounting framework is:
Task cost = the sum, across model calls, of applicable input, cached-input or cache-write, and output charges, plus separate metered tool, runtime, or other charges.
This is a framework, not a universal billing formula. Providers define usage categories differently. Check each provider’s usage object and rate definitions before calculating: cached tokens may already be included in the input total, and reasoning tokens may be reported within output usage rather than as a separately priced category.
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Four token categories to distinguish
- Input tokens: content sent to the model, such as instructions, conversation history, code, and tool results.
- Cached input: eligible repeated input billed at a cached-input rate when the provider’s caching rules are met. It may be shown as a subset of input usage, not an additional quantity to add on top.
- Output tokens: content generated by the model. Depending on the provider’s reporting, this can include reasoning tokens.
- Reasoning tokens: model usage associated with reasoning. Check whether the provider reports or prices it separately; for OpenAI’s usage example, reasoning tokens are included in output tokens.
OpenAI’s token guidance explains why a nominal rate alone is not enough to compare costs: “A lower price per million tokens does not necessarily produce a lower total cost: models can tokenize the same text differently and generate different amounts of output or reasoning.” See OpenAI’s explanation of tokens and counting.
Why can a coding task cost more than the final answer suggests?
The harness—the software coordinating model calls and tools—can repeatedly send context and add tool definitions, names, descriptions, and schemas. Tool-use blocks and returned results also matter. In coding work, command output, errors, and large file contents may add substantial input. Anthropic’s pricing documentation gives model-specific examples of system-prompt and tool-definition costs; the exact overhead varies by provider, model, tool version, and harness.
To understand a bill, inspect both the number of calls and the usage for each call. A short final response may follow many model turns and tool interactions. Conversely, a longer response does not by itself establish that the task was more expensive: the input mix, model rates, caching, and separate meters all affect the total.
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For the OpenAI Agents API specifically, OpenAI says the API adds no separate Agents API fee and that users pay for tokens and tools used, as described in its Agents API announcement. That statement should not be generalized to other products, vendors, plans, or third-party tools.
How does prompt caching change the bill?
Prompt caching can lower the rate for eligible repeated input, but it is conditional and still billed. It reuses model-side state for a matching prefix; it does not replay an old answer. The model still processes new input to produce a new response. The rendered prefix must match under the provider’s rules, and changes to earlier context, tools, formatting, reasoning settings, or context management can prevent reuse. Cache lifetime and eligible prefix requirements also depend on the provider and model. OpenAI describes the mechanics in its prompt-caching guide.
OpenAI’s guide says eligible cached input can receive up to a 95% discount. That is a maximum described by the guide, not a guaranteed discount for every model, token, or request; verify live model rates and eligibility. As OpenAI’s agent observability guide puts it, “A high cached-input percentage does not measure savings on the total task cost.” A task can have a large cached share yet remain costly because of output, uncached input, repeated calls, or separate tool charges.
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Which costs are outside token arithmetic?
Some services add charges that are not ordinary model-token rates. For example, OpenAI’s API pricing page describes eligible hosted-container sessions—including Hosted Shell and Code Interpreter—as billed by the minute with a five-minute minimum. Google Cloud’s Agent Platform pricing page lists a separate charge for certain grounding queries. Whether either charge applies depends on the product, tool, account terms, and plan you actually use.
As displayed on official pages accessed October 7, 2026, Google Cloud lists $14 per 1,000 grounding queries above an included 5,000 monthly queries for Grounding with Google Search / Web Grounding for Enterprise. Check the current Google Cloud Agent Platform pricing page and your account terms to confirm applicability. Tool, runtime, search, and other meters should be tracked separately from token usage.
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What do current published rates show—and what do they not show?
The following are official list-price examples visible on October 7, 2026. They are dated snapshots, not a stable market ranking or evidence of a typical developer’s spend. Rates can change; check the live pricing pages before estimating a workload.
| Provider and item | Published example as of October 7, 2026 | Qualification |
|---|---|---|
OpenAI, gpt-5.3-codex Standard Fast API rates |
$1.75 per 1 million input tokens; $0.175 per 1 million cached input tokens; $14.00 per 1 million output tokens | Rates shown in the accessed OpenAI API pricing page; check current model and rate details at OpenAI API pricing. |
| OpenAI eligible hosted-container sessions | Billed by the minute, with a five-minute minimum per eligible session | Pricing page says container pricing includes Hosted Shell and Code Interpreter. Eligibility and current terms are on OpenAI API pricing. |
| Google Cloud, specified grounding queries | $14 per 1,000 queries above 5,000 included monthly queries | Applies to the listed Grounding with Google Search / Web Grounding for Enterprise pricing example; product and account terms govern. See Google Cloud Agent Platform pricing. |
| OpenAI eligible cached input | Up to 95% discount | Maximum described in the guide, not a universal per-model rate or guaranteed task saving. See OpenAI prompt caching. |
These figures show why two comparisons can mislead: the cheapest input rate may pair with more output or reasoning, while a low cached-input rate only applies to qualifying tokens. Official documentation establishes billing mechanics and these list-price examples; it does not establish an average coding-agent bill, comparative coding quality, or a universal savings percentage.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you compare coding agents or model APIs?
Compare the same representative task under the same completion criteria. Record the actual provider, model, harness, tools, and billing plan; a metered API and a hosted coding-agent subscription are not necessarily billed or scoped alike. Judge both whether the task reaches the required quality and how long it takes. The cheapest run is not the most efficient if it fails, requires costly retries, or misses the needed latency.
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- Usage mix: uncached input, cached input, output and reasoning usage, and tool-result volume.
- Tool and runtime charges: per-call tools, hosted execution minutes, search or grounding, and any other separate meters.
- Cache behavior: eligible prefix size, matching requirements, cache lifetime, and actual cache-read usage.
- Task outcome: whether the result meets your quality requirements and latency target.
- Plan scope: whether the usage is metered API access, a hosted agent plan, or another subscription, and what the current terms include.
A practical measurement procedure
- Define a representative task. Use a real coding task with a clear success condition, such as passing specified tests or producing an accepted change, and set the latency you need.
- Record the configuration. Note provider, exact model, harness and version, tools, relevant settings, and billing plan so that a later comparison is meaningful.
- Capture every call’s usage. Collect input, cached input, output, and reasoning fields as reported, plus call count and the tool interactions that return content to the model.
- Include separate meters. Add eligible runtime, search, grounding, or other charges using the applicable plan and pricing definitions; do not assume every tool is included or token-billed.
- Check the result and elapsed time. Count retries and failed runs, then compare only completed tasks that meet the same quality and latency requirements.
- Recheck live rates and terms. Apply the current provider pricing to the observed usage, preserving the provider’s definitions for cached input and reasoning rather than double-counting them.
This produces a task-level comparison you can inspect and repeat. There is no evidence-based universal winner from token prices alone: the actual bill depends on the workload and the complete setup.
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