Best LLMs for Voice Agents 2026

Behaviour measured on multi-step tool scenarios over a live Speko route, first-token latency from one US region; latest capture 2026-10-09. First-token latency, task completion, fabrication and cost for voice-agent LLMs.

The LLM board publishes no composite score and no single ranking: first-token latency, task completion, fabrication, dead air and cost are each measured and ranked on their own, and the model that wins one axis is routinely mid-table on another. The sections below name a leader per axis.

Fastest first token

Cerebras gpt-oss-120b returns a first token in 139ms at the median (captured 2026-10-04) at $0.75 per million output tokens.

Most reliable

20 of the 28 measured models never invented a fact they were not given. Baseten DeepSeek-V4-Flash-0731 is the strongest of those on task completion while holding a real-time first-token measurement: 93.16% of the multi-step tool tasks, 36.8% dead air, 558ms p50 (captured 2026-10-04).

Best multilingual

Top scorer across the 15 languages: Baseten DeepSeek-V4-Flash-0731 (top in 6 of 15, never below 88.6% of scenarios, in the caller's language throughout) and OpenAI gpt-4.1-mini (top in 6 of 15, never below 78.07% of scenarios, in the caller's language throughout). Outside that pair, Gemini gemini-3.5-flash-lite has the highest floor: its weakest language still completes 85.09% of scenarios, in the caller's language throughout.

Numeric fidelity is its own failure axis: in the Arabic study, Alibaba qwen-turbo spoke tool-returned values back correctly 43% of the time. Full studies: Multilingual LLM.

Best value

Baseten DeepSeek-V4-Flash-0731 is the cheapest model on the board that never invented a fact it was not given: $0.26 per million output tokens, 558ms to first token, 93.16% task completion, with 36.8% dead air as the tradeoff (captured 2026-10-04). Grounding is not the only way to invent: check its tool-argument column before shipping it, which is a separate measurement and a separate failure. Alibaba qwen-turbo lists at $0.20 but completes 30.61% of tasks and fabricates on 90% of out-of-policy questions.

How we measured

Full board and column definitions: LLM board; multilingual task and numeric-fidelity studies: Multilingual LLM; raw dataset: /data.json.