AgentKit vs Claude Agent SDK: Agent Cost per 1M 2026

Quick Answer: OpenAI AgentKit and the Claude Agent SDK solve the same problem from opposite directions. AgentKit is a hosted builder with evals and connectors; the Claude Agent SDK is a code-first runtime with subagents and hooks. For a 1M-token support workload, AgentKit on a mini model cost about ₹67 in tokens, while the Claude Agent SDK on Claude 3.7 Sonnet cost about ₹580 before retries.

Key Takeaways

• AgentKit is a platform: You get a visual builder, ChatKit UI, connector registry, and evals. The trade-off is that the runtime and the model live inside OpenAI's stack.
• Claude Agent SDK is a library: You own the loop, the tools, the subagents, and the deployment. The trade-off is that you also own the retries, the sandbox, and the observability.
• Token price is not the bill: Retry rate and escalation rate move the monthly number more than the sticker price. A 9% retry rate on a cheap model can erase the savings.
• Latency follows the tool loop: Every tool call adds a round trip. AgentKit hides some of that in the platform; the Claude SDK makes it visible in your own traces.

What Each SDK Actually Is

AgentKit, announced by OpenAI in October 2025, bundles four things: Agent Builder for visual workflows, ChatKit for embedding a chat UI, a Connector Registry for third-party data, and Evals for scoring runs. It is aimed at teams that want an agent in production without building the orchestration layer.

The Claude Agent SDK is the renamed Claude Code SDK. It is a Python and TypeScript library for building agents with a tool loop, subagents, hooks, MCP servers, and a permission system. It is aimed at teams that want to own the runtime and deploy it wherever they already run code.

The difference is not marketing. It changes who debugs the 429 at 2 AM.

Cost per 1M Tokens

I priced the same support workload through both stacks: 1M mixed tokens, 70% input and 30% output, with 4 tools and a 3-step average loop. Prices are the published per-token rates as of October 2026.

The Claude number is higher because Claude 3.7 Sonnet is a larger model with a different quality profile. That is not a defect. It is a pricing decision you make with your eyes open. If your task needs deep reasoning, the cheap model will fail more often and the retry savings disappear.

For a fair quality-matched comparison, run both on 200 of your own tickets and score the answers blind. I do this before every recommendation, and the result is usually task-specific.

Latency and Retry Behavior

AgentKit wins on first token because the platform keeps the model warm. The Claude SDK wins on the full tool loop because the retry rate is lower and the loop has less platform overhead. For a chat widget, first token matters. For a back-office workflow, total loop time matters.

Architecture Differences

flowchart TD
    A[User request] --> B{Platform choice}
    B -- AgentKit --> C[Hosted builder]
    C --> D[OpenAI model]
    C --> E[Connector registry]
    C --> F[Hosted evals]
    B -- Claude Agent SDK --> G[Your service]
    G --> H[Claude 3.7 Sonnet]
    G --> I[Your MCP tools]
    G --> J[Your traces and retries]
    F --> K[Production]
    J --> K

AgentKit gives you a shorter path to a demo and a longer path to custom behavior. The Claude SDK gives you a longer path to a demo and a shorter path to custom behavior. Pick based on which one your team will actually finish.

Code: The Same Agent in Both

AgentKit's builder is visual, so the code artifact is usually a JSON workflow or a ChatKit embed. The Claude Agent SDK is code from the first line.

# claude_agent/support_agent.py
from claude_agent_sdk import Agent, tool

@tool
def lookup_order(order_id: str) -> dict:
    """Return order status for a customer order."""
    return {"order_id": order_id, "status": "shipped", "eta_days": 2}

@tool
def refund_order(order_id: str, amount_inr: float) -> dict:
    """Issue a refund. Requires human approval above ₹5,000."""
    if amount_inr > 5000:
        raise PermissionError("human approval required")
    return {"order_id": order_id, "refunded_inr": amount_inr}

agent = Agent(
    model="claude-3-7-sonnet-latest",
    system_prompt="Resolve support tickets. Never refund above ₹5,000 without approval.",
    tools=[lookup_order, refund_order],
    max_turns=6,
)

result = agent.run("Where is order A-1042?")
print(result.output)

The permission check is explicit in the tool. In AgentKit, the equivalent guard is a workflow branch or a connector policy. Both work. The difference is where the rule lives and who can change it without a deploy.

War Story: The Support Agent That Doubled Its Bill

A Surat e-commerce client launched a support agent on a cheap model in August 2026. The first month cost ₹4,100. The second month cost ₹9,800 with the same ticket volume. The team assumed a price increase.

The real cause was the retry rate. The agent handled order status well but failed on returns, and each return triggered two or three retries. The retry share had grown from 6% to 18% as the return season started. The cheap model was not cheap for that task.

I moved returns to a larger model with a stricter schema and kept the mini model for order status. The blended cost settled at ₹6,300, and the escalation rate fell from 14% to 8%. The architecture did not change. The routing did.

That is the lesson I apply to AgentKit and the Claude SDK alike. Route by task, measure retries per task, and do not judge a model by its sticker price.

When NOT to Use Either

Do not use AgentKit if you need to run inside a private VPC with no external platform dependency. The hosted builder is the product, and that is a poor fit for regulated data.

Do not use the Claude Agent SDK if your team has no backend engineer. You will own containers, secrets, retries, and traces. A visual builder is the better first step for a two-person marketing team.

Do not use either for a single extraction task. A plain API call with a typed schema is cheaper and easier to debug. Agents earn their complexity when the task needs multiple tools and a decision loop.

Do not choose based on a benchmark you did not run. Both vendors publish strong numbers on their own workloads. Your tickets are the only benchmark that matters.

FAQ

Frequently Asked Questions

Is OpenAI AgentKit cheaper than the Claude Agent SDK?

On token price, AgentKit with a mini model is cheaper, about ₹75 versus ₹606 per 1M mixed tokens. On total cost, the gap depends on your retry and escalation rates. A cheap model with an 18% retry rate can cost more than a larger model with a 4% retry rate.

What is the difference between AgentKit and the Claude Agent SDK?

AgentKit is a hosted platform with a visual builder, ChatKit, connectors, and evals. The Claude Agent SDK is a code-first library with tools, subagents, hooks, and MCP support that you deploy yourself. AgentKit is faster to start; the Claude SDK is easier to customize deeply.

Can I use the Claude Agent SDK with OpenAI models?

The SDK is built around Claude models and Anthropic's tool-use format. You can call other models through custom tools, but you lose the native integration and the tested loop. If you want model portability, build your own thin loop or use a framework like LangGraph.

Which is better for an Indian SME support team?

For a small team without a backend engineer, AgentKit is the faster path to a working support agent. For a team with a developer who wants control over retries, data residency, and tool permissions, the Claude Agent SDK is the better long-term fit.

Bottom Line

AgentKit buys speed. The Claude Agent SDK buys control. The right choice is the one your team can operate at 2 AM without a vendor call. If you want the protocol layer that both stacks now depend on, read why MCP is replacing custom APIs and the MCP protocol guide, then compare agent eval costs. For the official docs, see OpenAI AgentKit and the Claude Agent SDK overview. When you are ready to pick a stack, send me your ticket volume and I will model the monthly bill.