A curated view of recently released production models for enterprise AI teams. Compare context limits, cost profile, and best-fit workloads before rollout.
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623 models found
Nvidia 2026-04-28
NVIDIA: Nemotron 3 Nano Omni (free)
NVIDIA Nemotron™ 3 Nano Omni is a 30B-A3B open multimodal model designed to function as a perception and context sub-agent in enterprise agent systems. It accepts text, image, video, and...
Laguna M.1 is the flagship coding agent model from Poolside, optimized for complex software engineering tasks. Designed for agentic coding workflows, it supports tool calling and reasoning, with a 128K...
Laguna XS.2 is the second-generation model in the XS size class from Poolside, their efficient coding agent series. It combines tool calling and reasoning capabilities with a compact footprint, offering...
HappyHorse video editing supports advanced video editing through natural language instructions. It allows for local or global editing of video elements using up to 5 reference images.
Enter your team size, the models you use, and how often. We'll calculate what you're spending now, what you'll spend in 6 months, and where the money is actually going.
You get
A cost projection showing your current AI spend, 6-month forecast, and the top 3 areas where you're overspending.
Knowledge Hub
Model Selection FAQs
Start from workload requirements: reasoning depth, latency target, context window, tool-calling reliability, and budget envelope. Then run a controlled benchmark on your own prompts before broad rollout.
Usually no. Most teams run a model portfolio: one for high-quality reasoning, one for high-throughput tasks, and one for coding or workflow automation.
For fast-moving AI operations, review model choices monthly and rerun focused evaluations when new frontier releases appear.
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What Could Go Wrong?
5 questions about how your company uses AI today. We'll show you the risks most companies miss until it's too late.
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A risk breakdown with the 3 things you should fix first.
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