Shift Log: Two Models on the Support Queue

The support queue needs a model that resolves ordinary requests without creating extraordinary cleanup. For this selection, put Claude Haiku 4.5 and Gemini 2.5 Flash head to head on the same tickets, knowledge base, and tools. The decision is not which model writes the friendliest demonstration. It is which one completes your workflows safely, quickly, and at an acceptable total cost.

This is a deployment comparison, not a claim of measured superiority. Without results from your queue, naming an unconditional winner would be guesswork. The verdicts below identify which results should determine the assignment.

Put both candidates on the same support shift

Build a test set from anonymized historical conversations. Include straightforward policy questions, incomplete order details, conflicting documentation, angry customers, and requests that require a human. Keep a separate holdout set that nobody uses to tune the prompts.

Give Claude Haiku 4.5 and Gemini 2.5 Flash identical instructions, retrieved documents, tool permissions, and output requirements. Test the exact configurations you would deploy. If one candidate gets better retrieval or more retries, you are comparing systems rather than isolating model behavior.

• Score whether the answer follows the supplied policy and cites the correct supporting material.
• Record whether the model asks for missing information instead of inventing account details.
• Check whether tool calls use valid arguments and respect authorization boundaries.
• Measure end-to-end response time, including retrieval, tools, retries, and fallback handling.
• Track total cost per successfully resolved conversation, including review and escalation work.

Routine questions reward consistency over eloquence

For password-reset guidance, delivery questions, and documented product instructions, both candidates should face a narrow task: find the relevant policy, answer clearly, and avoid unsupported additions. A longer response is not automatically a better resolution.

Compare the models on repeated runs and paraphrased questions. A candidate that answers correctly once but changes the policy interpretation when the customer becomes impatient is not ready for unattended service. Reviewers should score responses without seeing the model name.

The routine-queue verdict is to choose the lower-cost candidate only after it meets your correctness and escalation requirements. If Claude Haiku 4.5 qualifies and Gemini 2.5 Flash does not, choose Claude. Reverse that decision if the results reverse. If both qualify, use measured cost and response time to break the tie.

Account actions make tool discipline the deciding round

Refunds, subscription changes, and account recovery require a different comparison. Writing a plausible explanation is not enough; the model must recognize when an action is permitted, request necessary details, and stop when authorization is missing.

Test attempts to bypass policy, instructions embedded in retrieved content, duplicate submissions, and failed tool responses. Authorization, eligibility checks, and transaction limits belong in application code, not just in the prompt. Neither candidate should be able to grant itself permission.

The sensitive-workflow verdict is to prefer demonstrated restraint over a small cost advantage. If neither model reliably stays within the workflow, keep both out of autonomous actions. Let the bot explain the next step or prepare a draft for an agent instead.

Busy queues expose costs hidden by short demos

Run both candidates through realistic conversation lengths and concurrent traffic. Measure customer-visible delay rather than treating initial output speed as the whole experience. A quick first sentence does not help if the customer waits through unnecessary tool calls afterward.

Include handoff quality in the comparison. An escalation should summarize the issue, verified facts, attempted steps, and unresolved question. Poor summaries shift work onto agents and can erase apparent inference savings.

Keep the relay separate from the selection verdict

Relay365 fits when you want pay-as-you-go access to multiple model families through a common relay desk. Its OpenAI SDK compatibility can simplify a shared test harness, although you should still verify each candidate's tool behavior and supported parameters. A common interface does not make models interchangeable.

Create a Relay365 account if that setup fits your evaluation workflow, then run a capped pilot before expanding traffic.

Close the shift with a bounded production assignment

The final verdict is straightforward: assign routine tickets to the qualifying candidate with the better measured operating cost; assign sensitive workflows only where safety controls and evaluation results justify them. If neither qualifies, retain human handling. Recheck the decision after model, prompt, retrieval, or policy changes. The winner earns a queue assignment, not permanent trust.