Prime Intellect

What it does

Prime Intellect is a platform for training, evaluating, and serving AI models. Users package tasks as reinforcement learning environments using the open-source Verifiers library and Prime CLI, then run hosted evaluations and managed RL training against them. Outputs include trained models, evaluation results on a public leaderboard, and models served through an OpenAI-compatible API. GPU compute and secure sandboxes for code execution are also available.

The Environment Hub lets users find or publish community RL environments, run evals on hosted models, and move into managed training. Its distinct strength is the integrated stack, spanning environment development, training, inference, and GPU capacity, built on open-source libraries. Prime Hosted Models, starting with GLM-5.3, run on Prime infrastructure alongside gateway access to third-party models.

The platform suits ML engineers and research teams that want to post-train agents and models on their own workflows. Hosted training and inference require an account at app.primeintellect.ai, and on-demand GPUs are billed per hour. Dedicated inference and large GPU clusters are arranged by contacting sales.

What it can do

• Create RL environments from tasks with the open-source Verifiers library and Prime CLI (init, develop, eval, push)
• Run hosted evaluations on open-source models with a public leaderboard
• Run managed RL training on the Environment Hub
• Serve Prime Hosted Models (GLM-5.3) and third-party models through one OpenAI-compatible API
• Rent on-demand GPUs and reserved clusters with Slurm/Kubernetes orchestration and Grafana dashboards
• Execute code securely in sandboxes built for RL

Works with

• Verifiers library
• prime-rl async RL framework
• Prime CLI
• OpenAI-compatible API

Example workflows

Build and evaluate a custom RL environment

Input: a task description and dataset. Steps: initialize an environment with Prime CLI, develop it on the Verifiers library, run evals on a hosted model, then push it to the Environment Hub. Output: a published environment with evaluation results.

Serve a model through the inference API

Input: an API key and a model choice (GLM-5.3 or a third-party model). Steps: send requests to the OpenAI-compatible endpoint. Output: model completions via the same API interface.

Before you choose

• Full pricing for hosted training, evaluations, and inference is not published; only sample GPU hourly rates appear.
• Dedicated inference and large GPU clusters require contacting the team for quotes.
• Use of the Environment Hub and open-source libraries is separate from paid hosted compute.

Listed under

• Coding
• Sandboxes and infrastructure
• AI developers
• Evaluations
• GPU compute
• LLM inference
• Model training
• Reinforcement learning

Get started

The site offers a Start Training entry point and a pip install prime command for the CLI.

1. Create an account at app.primeintellect.ai.
2. Install the Prime CLI with pip install prime.
3. Initialize an RL environment and run an eval, or make a first inference request using the API.

Sample GPU prices are listed per hour (e.g. H200 at $1.99/HR, H100 at $2.43/HR); hosted training, evaluation, and inference rates are not confirmed.

We couldn’t find published prices for Prime Intellect. See their pricing page ↗

Sources for this overview

Product pages, documentation, and pricing checked 29 Oct 2026. Each source supports part of the overview; availability and prices can change.

• Prime Intellect - The Open Superintelligence Stack ↗www.primeintellect.ai
• Introduction - Prime Intellect Docs ↗docs.primeintellect.ai

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primeintellect.ai: product, pricing and policy pages

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knowledge_base

Last checked 29 Oct 2026. Every field keeps its source and date; unknown means we couldn’t confirm it.