Which LLM for your AI voice agent: GPT-4o-mini, Claude Haiku, Mistral, Llama — the honest grid
GPT, Claude, Mistral, Llama — each costs different, hallucinates different, and latencies differently. Here's the grid to pick the LLM that fits YOUR call flow, not the benchmark.
- agent vocal ia
- llm
- modele
- choisir
Picking the LLM is the most expensive and least-discussed decision in an AI voice agent deployment. Spending 5× more or seeing 30% more hallucinations comes down to this choice — not your prompt. Here's the honest grid by use, not a marketing leaderboard.
GPT-4o-mini — the default option#
Cost: ~$0.01-0.03 per 2-min conversation. Latency: 200-400ms per turn. Strength: nuanced understanding, follows complex instructions well. Weakness: can be verbose (tighten the script), sometimes hedges on French technical terms. Sweet spot: generalist agent, simple-to-medium bookings, B2C. The default pick for 70% of deployments.
Claude Haiku 3.5 — for long, nuanced conversations#
Cost: ~$0.02-0.05 per conversation. Latency: 250-450ms. Strength: excellent for negotiations, multi-turn corrections, emotional contexts (grief, emergency). More cautious on ambiguous questions. Weakness: a bit slower, sometimes too formal. Sweet spot: healthcare, vet, premium services, consultative B2B.
Mistral Large 2 / Voxtral — for native trilingual#
Cost: ~$0.008-0.02 per conversation. Latency: 150-350ms. Strength: excellent in French and better Arabic than anglo-centric competitors. Voxtral combines LLM + STT in one model, cutting end-to-end latency. Weakness: less trained on specific verticals. Sweet spot: trilingual (FR/AR/EN) flow, tight budget, latency-critical.
Llama 3.3 70B (self-hosted) — for on-prem#
Cost: variable, ~$0.005-0.015 per conversation after infra amortization. Latency: 300-700ms depending on hardware. Strength: no data leak to a third party (US healthcare/HIPAA, banking, defense). Weakness: GPU-cluster maintenance, not for SMBs. Sweet spot: large account with sovereignty constraints, dedicated infra budget.
The 3 costliest selection mistakes#
- Picking the 'best' model instead of the right one — paying 5× more for 3% extra quality on flows where 3% isn't visible.
- Testing on 10 calls and generalizing — you need 500-1000 calls to see a real hallucination pattern.
- Optimizing the LLM before the prompt — a bad prompt on GPT-4o > a good prompt on Haiku. Always the prompt first.
The 30-day rule#
Run your flow on GPT-4o-mini by default for 30 days. Analyze transcripts: which error patterns? Nuance lost → try Claude. Latency feels too long → try Mistral. Data leak impossible → Llama self-hosted. First month VocazAI free to run that test risk-free.