io.net Adds Reflection Beam 1.0 to AI Platform

io.net has added Reflection AI’s Beam 1.0 to its inference infrastructure, making the newly released open-weight model available without a waitlist for developers building coding and agentic applications. According to an official io.net announcement, Beam combines 501 billion total parameters with 23 billion active parameters per token and a 1-million-token context window, while inference can now be served through io.net’s distributed GPU infrastructure.

Beam itself was released by Reflection AI on October 16 rather than developed by io.net. The official model repository provides BF16, FP8 and NVFP4 checkpoints under the Apache 2.0 license. Its open-weight release allows developers to download, modify and self-host the model instead of being limited to a proprietary inference API.

New open model. No waitlist.

Reflection Beam 1.0 is live on @ionet.

Built for coding and agents. 501B parameters, 23B active, 1M context window.

Start building today. pic.twitter.com/DTOdoBhVtZ

— io.net (@ionet) October 24, 2026

Beam Targets Coding and Long-Horizon Agents

Reflection describes Beam as a sparse mixture-of-experts model optimized for software engineering, reasoning and multi-step agent workflows. Although the model contains 501.3 billion parameters, approximately 23.4 billion are active for each token, reducing inference compute relative to activating the entire network for every request.

The model accepts text input and supports function calling, structured outputs and configurable reasoning through compatible serving environments. Its 1,048,576-token context window is designed for workloads such as large codebases, extended tool histories and long-running agent tasks. The large context capacity expands how much information an application can provide in one request, but it does not guarantee that every deployment will efficiently use the full window.

The addition extends io.net’s recent push beyond raw GPU rentals toward managed AI services. Earlier this month, the network expanded IO Intelligence support for open-source AI tools, allowing external applications to direct model requests toward its inference layer through familiar APIs. Beam adds a frontier-scale open model to that growing catalog rather than introducing a separate agent platform.

io.net has also developed dynamic GPU provisioning for autonomous AI agents, enabling compatible software to scale compute capacity up and down as workloads change. Serving Beam complements that infrastructure by giving developers a large model that can consume distributed GPU capacity for inference while agent logic remains in the surrounding application.

OpenRouter Adds a Standardized Access Route

Beam is also available through OpenRouter, where io.net currently appears as one of several providers serving the same Reflection model. The io.net endpoint is presently listed at roughly $0.24 per million input tokens and $0.98 per million output tokens, with separate cached-input pricing. Those rates are provider-specific and can change independently of Reflection’s model weights or other OpenRouter endpoints.

OpenRouter’s provider layer is important because access through the platform does not inherently mean a request runs on io.net. Developers can allow OpenRouter to select between available providers or explicitly configure routing preferences. Using io.net therefore requires selecting its provider when infrastructure provenance matters, rather than assuming every Beam request on OpenRouter reaches the decentralized GPU network.

The deployment fits a wider shift among decentralized compute projects toward higher-level AI services. Render, for example, has proposed a Forth AI gateway for routing inference workloads, while other networks are developing model-access layers above their underlying compute markets. The competitive layer is increasingly moving from supplying GPUs toward making those GPUs usable through standardized model APIs and agent tooling.

io.net still uses blockchain infrastructure primarily for coordination, payments and incentives rather than executing large neural networks directly on Solana. Beam inference runs on GPU hardware connected to io.net, not inside Solana smart contracts, preserving the distinction between decentralized compute coordination and blockchain execution.

For now, the measurable development is that developers can immediately access a 501B-parameter open-weight model through io.net without joining a model waitlist. The stronger adoption signal will come from sustained inference volume, GPU utilization and recurring developer workloads rather than model availability alone.