What the OpenAI API is for
The OpenAI API lets an application use OpenAI models for text, reasoning, vision, audio, and tools, depending on the endpoint and model. It is not a finished support workflow. Your team still chooses the model, supplies context, stores or passes conversation history, and writes the code around it.
For an application that needs a model to retrieve an order, decide whether a refund is allowed, and then issue a refund, the model API is one part of the system. The application has to connect the order system, constrain what the model may request, perform the eligible action, and show a customer what really happened.
What the Anthropic API is for
Anthropic's Claude API offers Claude models through the Messages API and related platform features. It has the same basic boundary: a model response is not an action in your systems. The application owns the customer data, integrations, permissions, and action handling around it.
The Messages API is stateless. Each request includes the conversation history the model needs. That can suit an application that wants explicit control over what travels with each turn, but it also makes history selection and token use part of the application's design.
What to compare in the OpenAI API and Anthropic API
Start with the job your application must complete. A chatbot answer, a document-analysis tool, and a support agent that can update an order are different evaluation problems. The API that feels easiest in a small prototype may not make the operating work disappear.
Conversation state and tool loops
OpenAI supports manual history and several conversation-state options. With the Responses API, an application can replay prior items, chain responses, or use a Conversations object to persist messages and tool results. Persisting state does not remove the need to understand what context the model receives or what it costs.
With Anthropic, the Messages API expects the application to send the relevant history. When moving between providers, do not assume that changing a model name is enough. Preserve the messages, tool identifiers, tool results, and system instructions that the next turn needs, then test the translated request.
Both providers can return multiple custom-tool requests in one response. OpenAI's function-calling guide and Anthropic's parallel-tool guide both describe a loop where the application runs tools and returns the results to the model.
That does not make every operation safe to run together. For a damaged-order ticket, reading an order record and finding the refund policy may be independent. Issuing a refund must wait for the eligibility decision and any required approval.
| Question for your application | What to check |
|---|---|
| Did the model request a tool? | Parse the returned call and match its identifier to the result you send back. |
| May that action run? | Check identity, permission, policy, and approval requirements before execution. |
| Did it work? | Inspect the actual service response, not the model's final wording. |
| Could a retry repeat it? | Use idempotency or another control that prevents duplicate refunds or messages. |
Safety and evaluation
Do not reduce the safety decision to an OpenAI-versus-Anthropic label. A customer-support workflow needs controls in the application around either model: least-privilege access, clear boundaries for actions, escalation paths, and tests that include the cases the normal prompt does not cover.
For example, evaluate a damaged delivery where the customer asks for a refund outside the usual window. Check whether the system distinguishes the ordinary policy from an exception, requests the right evidence, routes a decision to a human where needed, and reports only actions confirmed by the order system. Run the same cases against each candidate configuration before publishing it to customers.
Pricing and operating cost
Do not use a one-time model-price table as a buying decision. Both OpenAI pricing and Anthropic pricing vary by model and can include input, output, cached tokens, and feature-specific charges. Chat subscriptions are separate from API billing.
Price one representative workflow instead:
- Send the same tickets, policies, tool definitions, and history to each candidate model.
- Record input, output, cache, and any paid-tool usage for the full request loop.
- Measure answer quality, latency, failed actions, and the cost per completed workflow.
- Repeat the test when you change models, prompts, tools, or context length.
This gives a team an evidence-based cost comparison. It also catches a common mistake: selecting the cheapest token rate before measuring the extra calls or retries the workflow needs.
Build with a model API or operate a teammate?
Use a model API when you are building a custom product and need to own the application layer. That means accepting responsibility for the integrations, access control, prompt and tool design, evaluations, deployment, and ongoing monitoring.
If your immediate goal is a support workflow, eesel provides a ready-to-work support teammate instead. The choice is not that one model provider is automatically better for support. It is whether your team needs to build and operate the application around a model, or configure the support teammate that already works with your eesel workspace.
Operate an eesel support teammate through the CLI
The eesel CLI puts that existing teammate in a terminal. It is the same agent as the dashboard: changes made in either place apply to the same workspace. A support lead can run it directly, a script can consume its JSON output, and a coding agent such as Claude Code, Codex, or Cursor can use those commands for a scoped configuration task.
Here is a concrete workflow for the damaged-order exception above. First, use Node.js 18.17 or newer to sign in and confirm the workspace before touching a teammate:
npx @eesel/cli login
npx @eesel/cli whoami
npx @eesel/cli agents
Then point each inspection command at the intended teammate. Set TARGET_EESEL_AGENT to the ID or name returned by agents; this keeps the example copyable without relying on the saved default:
TARGET_EESEL_AGENT="paste-agent-id-or-name-here"
npx @eesel/cli status --agent "$TARGET_EESEL_AGENT"
npx @eesel/cli integrations --agent "$TARGET_EESEL_AGENT"
npx @eesel/cli instructions --agent "$TARGET_EESEL_AGENT"
Ask the coding agent for a read-only assessment: which sources are connected, whether their content has downloaded, and which standing instructions cover refunds and exceptions. The point is to understand the current teammate before proposing a change, not to let a coding agent alter accounts, permissions, or automation without review.
After a human approves a change, use --dry-run on the relevant write command to inspect the server call before sending it. Then start a new, named test conversation and ask the teammate to draft a reply to the approved scenario:
npx @eesel/cli new --name "damaged-order exception" --agent "$TARGET_EESEL_AGENT"
npx @eesel/cli chat "A customer says their order arrived damaged and asks for a refund outside the normal window. Draft a reply using the approved policy. State what evidence is missing and when a human decision is needed." --agent "$TARGET_EESEL_AGENT"
Review the reply for the policy boundary, missing evidence, and correct human handoff. A chat test drafts a response; it does not prove a refund happened. If the workspace has held actions, run npx @eesel/cli approvals --agent "$TARGET_EESEL_AGENT" and confirm the resulting action in the connected system before telling a customer it succeeded.
The CLI's setup and observation commands are free, while chat is billed work. That is another reason to scope the test and use the terminal's JSON output deliberately, rather than treating a long conversation as a free benchmark.
Making the choice
Choose between the OpenAI API and Anthropic API by running your real evaluation cases against the exact models and endpoint configurations you plan to use. Compare the completed workflow, not just a prompt demo or a headline token rate.
For a custom product, the APIs give you model access and the responsibility to build around it. For an existing eesel support workflow, the CLI gives people, scripts, and coding agents a way to operate the same teammate and workspace as the dashboard. Inspect first, review the proposed configuration, test the approved behavior, and verify real actions after they run.
If that second path fits your project, try eesel and start with the CLI guide.
Frequently asked questions
What is the main difference between the OpenAI API and Anthropic API?
Both let an application send model inputs and receive outputs, including tool requests on supported models. The practical differences are the endpoint and model you choose, message and tool formats, state-management options, pricing, and the surrounding features you need. Test those details against your own workflow instead of choosing on brand reputation.
Do OpenAI and Anthropic APIs execute tool calls automatically?
No. Both APIs can return requests for custom tools, but your application is responsible for executing them, checking permissions, returning results, and preventing duplicate side effects. Treat a model request to refund an order as a request for your application to evaluate, not proof that a refund happened.
How do OpenAI and Anthropic handle conversation state?
OpenAI offers manual history plus Responses API state options such as chained responses and Conversations. Anthropic's Messages API is stateless, so the application sends the history it needs. In either case, retain the exact tool calls and results required for the next turn.
How should I compare OpenAI API and Anthropic API costs?
Run the same representative workflow through the selected models and measure input, output, cached tokens, and paid tools or services. Use each provider's current price page; model rates and feature charges can change. Compare that total with the answer quality, latency, and successful completion rate you need.
Is Anthropic or OpenAI safer for customer support?
Neither provider label proves that a support workflow is safe. Define what the model may read and do, require approval where appropriate, and test normal tickets, edge cases, refusals, and failures. Verify the actual service response after an action instead of trusting a fluent final message.
Where does eesel CLI fit in an OpenAI API vs Anthropic API comparison?
The eesel CLI operates an existing eesel teammate in the same workspace as the dashboard. A person, script, or coding agent can inspect connected sources and instructions, then help configure and test a support workflow. It is not an inference endpoint that substitutes for either model API.
Can Claude Code, Codex, or Cursor use eesel CLI?
Yes. The CLI prints JSON, so coding agents can use its commands as part of a scoped setup task. Confirm the workspace and teammate first, ask for a read-only assessment before proposing a change, use --dry-run for a write command, and review the resulting configuration and replies before enabling customer-facing automation.
Is npx @eesel/cli officially documented?
Yes. The CLI documentation supports npx @eesel/cli with Node.js 18.17 or newer, and documents it as equivalent to the installed eesel command. Observing and setup commands are free; chat is billed work.
Article by
Kurnia Kharisma Agung Samiadjie
Kurnia is a software engineer and writer at eesel AI with two years of SEO experience, writing about AI tools, helpdesk software, and customer support. He pairs a developer's understanding of how these products are built with search-driven research into what actually ranks and resonates with the people searching for them.
