Claude vs OpenAI for production agents: how we choose per task

The short answer

We choose Claude or OpenAI task by task, and write each choice and its reason into the plan before the build starts. On the enterprise knowledge platform we shipped, both APIs ran side by side in the streaming document chat, and each job's choice came down to document context, tool-call predictability and the bill.

Nobody should take a model recommendation from an agency without seeing the reasoning, so this article is the reasoning. It covers the five things we weigh, what each one looked like on a real project, and a table you can apply to your own. It does not contain benchmark numbers. Public benchmarks move every few months and rarely resemble a production workload; a question set from your own users is the only test that matters, and we run one on every project.

What a production agent needs from a model

A production agent needs five things from its model: tool calls that match the schema every time, a context window that holds the job, caching that makes repeated prefixes cheap, latency the interface can hide, and a deployment path the client's security team will accept. Raw intelligence matters less once the model is good enough for the task.

That ordering is deliberate. An agent that fails one tool call in fifty is an agent somebody has to babysit; a model that is slightly more eloquent and fails that call is the wrong model. The vendors differ on each of these in ways that matter per task, which is why the answer is a table and not a name.

Tool calling

Both vendors' current models call tools well, and both offer a way to make the model's arguments match the declared schema. In our builds we turn strict schema validation on wherever it is offered and validate the arguments ourselves before anything runs, so a malformed call becomes an error the model can correct rather than an action that happens wrong.

The difference we noticed was in how each model behaves when the tool list is long or the descriptions are vague. Neither does well; one of them guesses more. Our fix was the same on both: short tool lists, descriptions written for a model to read, reads and writes as separate tools. On the agent platform built in LangGraph, the graph was model-agnostic by design, so the model inside a node could be swapped and tested against the same tool contracts. That is the arrangement we recommend when the choice is not obvious: make it cheap to change.

Long context and document work

For document-heavy work, the knowledge platform taught us that the model's context window is a ceiling and the context budget is the decision. Both vendors' current models have windows large enough for any chat turn a knowledge product will send. What differed was how each behaved when retrieved passages were long and many, and how each priced the repeated prefix.

The platform's chat ran on both. The plan recorded which jobs went where and why, and the question set from the client's users was re-run after every change so the choice was checked against real answers rather than assumed. Claude's explicit prompt caching, with breakpoints you place at the stability boundaries of the prompt, suited the document chat well, because the stable part of the prompt was large and we could decide exactly what to cache. We cover that mechanism in detail in our article on prompt caching and context budgeting.

Caching and cost

Claude's caching is explicit: you mark up to four breakpoints and everything before each one is cached for five minutes by default, or an hour as an option, with reads billed at a fraction of normal input and writes at a premium. OpenAI caches to long prompts without markup. Explicit control is more work and more predictable; we prefer it.

Cost is per task, and it is the cost of a finished job that counts rather than the cost of a request. A cheaper model that needs a second request to correct a tool call is not cheaper. The written plan names the model per task with an estimate of requests per job, and we log the usage fields on every request in production so the estimate is checked within the first week of real traffic. Price lists change; the mechanism of checking does not.

Latency and voice

Latency matters most where a person is waiting, above all on a voice call. The agent platform put voice on LiveKit and ElevenLabs in front of LangGraph agents, and the lesson was that the model's response time is one term in a sum: speech recognition, the model, the tool calls, speech synthesis and the network each take their share.

We kept the model's share small by giving voice-path workers one job each and moving slow tools into background jobs the agent could report on later.

Both vendors now ship voice-specific offerings and agent SDKs of their own. We have built voice on a pipeline that separates the pieces, because it lets us choose each piece and swap any of them, and because the same tools and state serve the text interface and the voice one. A vendor's integrated voice stack is quicker to start and harder to leave.

Deployment and procurement

Claude is available through Anthropic's API, Amazon Bedrock, Google Cloud and Microsoft Foundry, which matters when a client's data agreements or cloud commitments decide where the model may run. OpenAI's models are available through OpenAI and through Microsoft's cloud. For an enterprise buyer the procurement path can decide the vendor before any technical factor does, and that is legitimate.

The knowledge platform ran on the direct APIs. For clients standardised on AWS we build the same code behind Bedrock, which is one reason we keep the model behind an interface in every build.

The decision table

One row per kind of task we have shipped, with what we weigh and what we chose.

Questions we get about the choice

Should we use one vendor for everything? Only if procurement forces it. Using both is normal for us and costs little when the model sits behind an interface. The knowledge platform ran both in the same product.

Which agent SDK should we use, Claude's or OpenAI's? Neither, unless you are committed to that vendor. We build agents in LangGraph, which is model-agnostic, and expose tools through MCP, which is an open protocol. Both vendors' SDKs are fine products that happen to end in lock-in.

How do you test the choice on our workload? With a set of real questions or tasks from your users and the expected results, run against each candidate with the same tools and prompts. It takes a few days and settles arguments that would otherwise last the whole project.

Will the choice still be right in a year? Probably not for every task, which is why the plan records the reasoning and the code keeps the model behind an interface. Re-running the question set against a new model is an afternoon.

What we would do on your project

We write down the tasks your agent performs, weigh each against the five factors above, name a model per task with the reason, and build a small test set from your real work to check the choice before the build starts. That document is the discovery sprint, from $2,500. Book a free architecture call and bring the question you have been arguing about. The platform that ran both vendors is the enterprise knowledge platform, and the Claude API and MCP development service covers the kind of build this article describes.