DeepSeek V3 vs GPT-4o-mini

High-value light models head-to-head: DeepSeek V3 vs OpenAI GPT-4o-mini. Full breakdown of input/output token cost, support/Q&A throughput, Function Calling stability, and TTFT — so teams can pick a high-concurrency, low-cost primary model and cut daily OpEx.

Official benchmarks

Scenario recommendations

One Base URL and API key — switch models with the model parameter only:

import os
from openai import OpenAI

# One CallAI key — route freely between DeepSeek V3 and GPT-4o-mini
client = OpenAI(
    api_key="sk-live-your-callai-key",
    base_url="https://api.callaiapi.com/v1"
)

def query_model(model_id: str, prompt: str):
    response = client.chat.completions.create(
        model=model_id,
        messages=[{"role": "user", "content": prompt}],
        stream=True
    )
    for chunk in response:
        print(chunk.choices[0].delta.content or "", end="", flush=True)
    print("\n" + "="*40 + "\n")

# Model 1: DeepSeek V3
print(">>> Output from: DeepSeek V3")
query_model("deepseek-chat", "Briefly analyze the trade-offs of this architecture")

# Model 2: GPT-4o-mini
print(">>> Output from: GPT-4o-mini")
query_model("gpt-4o-mini", "Briefly analyze the trade-offs of this architecture")

FAQ