Last refreshed 2026-06-29. Next refresh: weekly.
Why use GPT-4 Turbo on Vercel AI Gateway?
Vercel AI Gateway offers GPT-4 Turbo with pay-as-you-go pricing at $10.00/1M input tokens. Vercel AI Gateway is a unified AI proxy providing a single OpenAI-compatible API endpoint to 275+ models from 25+ providers including Anthropic, OpenAI, Google, Meta, Mistral, DeepSeek, xAI, Alibaba, Amazon, ByteDance, Cohere, MiniMax, MoonshotAI, KwaiPilot, Black Forest Labs, Recraft, Voyage AI, NVIDIA, and more.
Compare GPT-4 Turbo across 6 providers to find the best fit for your use caseInput / 1M
$10.00
Output / 1M
$30.00
Cache
Not sourced
Batch
Not sourced
Setup recipe
Python + curlInstall
pip install openaiAuth
export AI_GATEWAY_API_KEY=...Call
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["AI_GATEWAY_API_KEY"],Model ID
openai/gpt-4-turboRequest example
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["AI_GATEWAY_API_KEY"],
base_url="https://ai-gateway.vercel.sh/v1"
)
response = client.chat.completions.create(
model="openai/gpt-4-turbo",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Gotchas
- Use provider model ID "openai/gpt-4-turbo", not the LLMReference slug "gpt-4-turbo".
- creator/model-name e.g. kwaipilot/kat-coder-pro-v2
- The examples expect AI_GATEWAY_API_KEY; rename it only if your application config maps the new variable.
Compare GPT-4 Turbo Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| OpenAI API | $10.00 | $30.00 |
| Azure OpenAI | $10.00 | $30.00 |
| Salesforce Einstein Generative AI | — | — |
| OpenRouter | $10.00 | $30.00 |
| Replicate API | $5.00 | $15.00 |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $10.00 |
| Output tokens | $30.00 |
Capabilities
VisionMultimodalJSON / Tool useStructured OutputsCode Execution
About GPT-4 Turbo
OpenAI's high-performance variant of GPT-4 with 128K context window and improved reasoning. Widely used for production applications.
Get Started
Model Specs
Released2024-04-09
Parameters1.76T (8x222B MoE)*
Context128k
ArchitectureMixture of Experts
Knowledge cutoff2023-12