How we tested
Took 500 real dialogs from three niches: beauty salon (200), dental (150), online store (150). All dialogs — real, from our customers' production projects in 2025-2026. Names and data anonymized.
Metrics: 1) answer accuracy (does AI reply to what the client is asking), 2) Ukrainian fluency, 3) tool calling success rate (how often AI correctly invokes functions), 4) context retention across 10+ messages, 5) response speed (latency), 6) cost per 1000 dialogs.
Tested Claude Sonnet 4 and GPT-4o (as of April 2026). Both with identical prompts and identical knowledge bases. No biasing toward either model.
Ukrainian language quality
Claude Sonnet 4: 97% of dialogs — no machine feel. Only 3% — client could guess it was AI (too formal). 0% russian-isms. Natural sentence structure.
GPT-4o: 84% — no machine feel. 11% — client could guess. 3% — explicit russian-isms ('podzvonyty' instead of 'zatelefonuvaty,' 'khochu' instead of 'bazhayu' in formal context). Sometimes 'translated from English' feel.
Conclusion: for businesses where clients 'hear' the language (beauty, medical, education) — Claude is clearly better. For more utilitarian niches (e-commerce, delivery) — difference is less noticeable.
Tool calling — critical for CRM assistants
Tool calling — when AI shouldn't just answer but execute a concrete action: create a record, update status, send a reminder. For business AI this is the foundation of functionality.
Claude Sonnet 4: 96% tool calling accuracy. So out of 100 situations needing a function call — it calls correctly with right parameters in 96. Failures — more often in edge cases (client says 'next Monday evening' — AI doesn't always parse local time correctly).
GPT-4o: 89% accuracy. Failures more common in compound requests ('book for tomorrow but if morning is busy — then the day after in the evening'). Sometimes calls functions with empty params.
Conclusion: for CRM assistants a 7% gap is hundreds of lost or messed-up records per month. Claude clearly wins.
Long-context handling
Claude Sonnet 4: 1M-token context window, effectively no limit on dialog length. In tests on dialogs 30+ messages long — remembers details from the very start without 'forgetting.'
GPT-4o: 128K-token context. Enough for typical business dialogs. But in long sessions (10+ messages with history) starts to 'forget' details — client mentioned an allergy in message 2, AI suggests it as a product in message 15.
In 2026 it's a less noticeable difference because most business dialogs are short (3-7 messages). But for b2b with long negotiations or medical with complex cases — Claude wins.
Price and speed
Claude Sonnet 4: $3 per 1M input + $15 per 1M output tokens. Speed: ~50-80 tokens/sec.
GPT-4o: $2.5 per 1M input + $10 per 1M output. Speed: ~80-120 tokens/sec.
For 1000 typical dialogs (5-10 messages each): Claude ~$8-12, GPT ~$6-10. Cost gap — 20-30%. For small business — €15-25/mo difference.
Conclusion: GPT is noticeably cheaper and faster. If budget is tight and 'Claude-level' quality isn't critical — GPT is rational. If every 5th dialog means a sale — Claude pays for itself.
Niche recommendations
Beauty salons, medical, education, b2b with large checks: Claude. Language quality and tool calling are critical here.
Online stores with typical questions (where's my order, delivery status): GPT. Cheaper, enough for most scenarios.
Cafes, fitness, simple services: GPT-4o-mini (even cheaper). 90% of GPT-4o quality at 5× lower price.
Content generation (email blasts, product descriptions, posts): GPT. Stronger at creative.
Voice assistants with transcription: combo Whisper + Claude. Whisper transcribes, Claude composes the reply.
At MTDK ai we default to Claude. For budget cases we offer GPT-4o-mini. For some tasks (generating email reminders) we run both in parallel.