GPT-4o vs Claude 3.5 —
which AI works best for
customer support chatbots?

We've deployed customer support chatbots on both GPT-4o and Claude 3.5 Sonnet for clients across retail, e-commerce and services. After analysing 50,000+ real conversations, here's our honest verdict — no marketing fluff, just production numbers.

Accuracy on domain-specific tasks

On FAQ-style tasks — return policies, delivery windows, product specs, store timings — both models perform above 90% accuracy when given a well-structured system prompt and knowledge base. The difference appears in nuanced, multi-part questions requiring the model to hold context across a long conversation thread.

Claude 3.5 Sonnet edges ahead on complex reasoning tasks that require synthesising multiple pieces of information. GPT-4o is slightly better at structured data extraction — parsing customer messages to pull out order IDs, dates, and product names.

Tamil & regional language support

This is often the deciding factor for our South Indian client base. Tamil-English code-switching is common — customers naturally blend both languages mid-sentence. Here's what we observed across 8,000+ mixed-language conversations:

• Claude 3.5 Sonnet maintains context correctly when the language switches mid-conversation approximately 87% of the time
• GPT-4o drops context or responds in English when Tamil is introduced, approximately 22% of the time
• Both models handle fully Tamil queries reasonably well — the gap is in code-switching, not monolingual Tamil
• Claude's responses in Tamil are more natural and less machine-translated in tone

Latency comparison

Both models are fast enough for chat UX. The gap only matters in voice-first deployments where every millisecond is perceptible.

Cost at scale — 50,000 conversations/month

Assuming an average conversation of 800 input tokens + 200 output tokens per turn, 4 turns per conversation:

Hallucination rate on product data

We tested both models by feeding them a 50-product knowledge base and asking 200 questions about products not in the knowledge base — testing whether they fabricate answers or admit they don't know.

• Claude 3.5 Sonnet: correctly refused or said "I don't have that information" 94% of the time
• GPT-4o: correctly refused 88% of the time — fabricated plausible but false answers 12% of the time
• Both models improve significantly when the system prompt explicitly instructs "do not answer questions not covered in the knowledge base"
• With a good system prompt, both models reach 97%+ correct refusal rate

Integration and developer experience

• Both have excellent Python and Node.js SDKs with streaming support
• OpenAI's function calling and structured outputs are slightly more mature for complex tool-use workflows
• Anthropic's system prompts tend to be more reliably followed — Claude is less likely to "break character"
• GPT-4o supports vision natively in the same model; Claude 3.5 Sonnet also supports images equally well
• Both support tool use / function calling — the syntax differs but capabilities are equivalent