DeepSeek V3 pricing for customer support bot

Quick start

Call DeepSeek V3 with the OpenAI Python SDK pointed at api.deepseek.com:

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.deepseek.com/v1",
    api_key=os.environ["DEEPSEEK_API_KEY"],
)
response = client.chat.completions.create(
    model="deepseek-v3",
    max_tokens=1000,
    messages=[{"role": "user", "content": "Your prompt here"}]
)
# Cost: ~$0.001910 per call at standard rates
# Input:  $0.27/MTok
# Output: $1.10/MTok

How DeepSeek V3 compares on cost

Per-call cost for all models scoring 50+ on customer support bot benchmarks. Hover for details.

Cheapest viable option: GPT 5.6 Luna at $0.001800/call (6% less, 85/100 quality).

What drives customer support bot cost

Support bots combine RAG's retrieved context with a chatbot's growing history, so they carry both cost curves at once. Volume is high and margins per conversation are thin, which makes the per-call rate matter more than in almost any other use case here. Caching the knowledge base and product context is the single highest-leverage change.

For DeepSeek V3 specifically, that means output tokens cost 4.1x more than input, at $1.10 vs $0.27 per MTok. At this use case’s 3,000/1,000 token split, output accounts for 58% of the $0.001910 per-call cost.

DeepSeek V3 quality benchmarks for customer support bot

Benchmark data for customer support bot is pending for DeepSeek V3. Test against your own evaluation suite before moving to production.

Benchmark data is not yet available for DeepSeek V3. We recommend testing against your own evaluation criteria.

About DeepSeek V3

Prices are off-peak rates from api-docs.deepseek.com (read 2026-10-11). Peak-hour prices are 2x the listed input and output rates — Tokeven does not model time-of-day tiers; off-peak is used as the base. Cache hit: $0.07/MTok input. No batch discount published. Not yet benchmarked, so never recommended.

Other models from DeepSeek

You can compare DeepSeek V3 head-to-head against any other model on our model comparison pages, or track how pricing has changed over time on the pricing history tracker.

Frequently asked questions