Which AI Model Is Best for Sales Conversations? Claude vs GPT vs Open Models

For most sales conversations, any current flagship or mid-tier model from Anthropic (Claude) or OpenAI (GPT) does well. The bigger wins come from matching the model to the job: a fast, cheap model for simple steps, a stronger one for objections and nuanced replies, and a backup from a different provider in case one goes down. Open models via providers like OpenRouter are a good option when cost matters most.

ReplySetter supports Anthropic, OpenAI and 350+ models through OpenRouter, so I've run the same sales flows across a lot of models. Models change every few months, so instead of a ranking that will be out of date soon, here's how I think about choosing.

What matters in a sales conversation model

1. Instruction-following. Does it stick to the step, ask one question at a time and respect "never" rules?
2. Tone. Does it sound natural, or like customer service?
3. Honesty under uncertainty. Does it admit when it doesn't know, or invent?
4. Tool use. Does it reliably call the calendar, CRM and handoff tools at the right moment?
5. Speed. Reply latency matters on SMS and DMs.
6. Cost. Per conversation, at your volume.
7. Languages, if you sell internationally.

How the model families compare, in my experience

These are general tendencies I've seen in testing. They shift between model versions, so test with your own conversations.

Anthropic (Claude)

• Strong at following detailed instructions and staying in character.
• Tone tends to be natural and warm with good tone instructions.
• Reliable tool use in multi-step flows.
• Larger models cost more; smaller ones (the Haiku class) are fast and cheap.

OpenAI (GPT)

• Strong all-rounders, widely used, very good tool calling.
• A range from small, cheap "mini" models to large reasoning models.
• Tone can lean toward generic assistant language without good character instructions.

Open models (via OpenRouter and others)

• Often much cheaper.
• Quality varies a lot by model; the best ones are good for routine steps.
• More testing needed for tone and rule-following.

Reasoning models: when to use them

Reasoning (or "thinking") models spend extra time thinking before answering. They're better at tricky situations, but slower and more expensive. In my setups:

• Use more reasoning for: objection handling, complex qualification, deciding whether to hand off.
• Use less or none for: greetings, simple questions, confirmations, routing.

ReplySetter lets you set the thinking level per character, so you can dial it up only where it helps.

Mix models across steps

You don't need one model for everything. A setup I like:

This often cuts costs a lot without hurting quality where it matters. See how much an AI appointment setter costs.

Always have a backup

Every AI provider has outages and rate limits. If your setter depends on one model and it fails, leads wait. Backup models from a different provider fix that: if the primary fails or times out, the next one answers.

In ReplySetter, each character can have up to five backup models across providers, with a timeout that triggers the next one. See any model.

Why bring your own key?

Using your own API key with the provider means:

• No markup on AI usage.
• You choose the provider and model.
• You see exact costs per conversation.
• Your data agreement is directly with the provider.

ReplySetter works this way on every plan, and keys are encrypted at rest.

How to choose for your business

1. Start with a mid-tier model from Anthropic or OpenAI.
2. Run 20 test conversations with real leads' messages.
3. Try a second model on the same conversations and compare tone, accuracy and tool use.
4. A/B test the two in production on real leads, measuring booking rate. See A/B testing.
5. Move simple steps to cheaper models once you're happy.
6. Add a backup from a different provider.