Using Beam on Vercel AI Gateway

Implementation guide · Beam · Reflection AI

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Vercel AI Gateway exposes Beam through model ID reflection/beam. Use the setup steps, sourced pricing, capabilities, and official provider links below to validate this route before deployment.

Last refreshed 2026-10-16. Next refresh: weekly.

Quick Start

  1. 1
    Create an account at Vercel AI Gateway and generate an API key.
  2. 2
    Use the Vercel AI Gateway SDK or REST API to call reflection/beam — see the documentation for request format.
  3. 3
    You'll be billed $1.30/1M input, $4.30/1M output tokens. See full pricing.

Code Examples

Install
pip install openai
API key
AI_GATEWAY_API_KEY
Model ID
reflection/beam

creator/model-name e.g. kwaipilot/kat-coder-pro-v2

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="reflection/beam",
    messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)

Pricing on Vercel AI Gateway

TypePrice (per 1M)
Input tokens$1.30
Output tokens$4.30

Capabilities

ReasoningJSON / Tool useStructured Outputs

About Beam

Beam is an open-weight sparse mixture-of-experts language model with 501B total and 23B active parameters, built for coding, reasoning, and long-horizon agentic work and trained from scratch. It is text-only, with a 1M-token context window, up to 128K output tokens, five reasoning effort levels (low to max, default medium), tool calling, and structured outputs, and a June 30, 2026 knowledge cutoff.

Model Specs

Released2026-10-05
Parameters501B (23B activated)
Context1M
ArchitectureMixture of Experts
Knowledge cutoff2026-06

Provider

Vercel AI Gateway

Vercel