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
- 2Use the Vercel AI Gateway SDK or REST API to call
reflection/beam— see the documentation for request format. - 3
Code Examples
Install
pip install openaiAPI key
AI_GATEWAY_API_KEYModel ID
reflection/beamcreator/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
| Type | Price (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