FDM-1 vs Durable
Detailed side-by-side comparison to help you choose the right tool
FDM-1
Automation
Foundation model for computer use trained on 11-million-hour video dataset that can perform complex computer actions like CAD modeling, website navigation, and real-world tasks at 30 FPS.
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CustomDurable
Automation
AI platform that turns enterprise problems into production-ready automations and custom software without coding, generating real code rather than agent chains.
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CustomFeature Comparison
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FDM-1 - Pros & Cons
Pros
- âFirst computer-use foundation model trained on internet-scale video (11M hours), versus the largest open computer-use dataset of under 20 hours of 30 FPS video
- âNative 30 FPS video processing enables continuous control like smooth mouse movement and CAD operations rather than discrete screenshot-by-screenshot reasoning
- âHighly efficient video encoder compresses nearly 2 hours of footage into just 1M tokens, unlocking minute-scale context windows
- âUnsupervised training via the inverse dynamics model removes the bottleneck of expensive contractor-labeled screenshots
- âTest-time compute via OS checkpoints / forking VMs lets the model retry from validated intermediate states on long-horizon tasks
- âDemonstrably general â the same model performs CAD modeling, website fuzzing, and real-world driving without task-specific RL environments
Cons
- âNo public API, pricing page, or self-serve access â gated to enterprise and research partners
- âCapabilities are demonstrated through curated video clips rather than peer-reviewed benchmarks against established computer-use leaderboards
- âReleased February 23, 2026, so production track record, reliability, and safety guardrails are unproven at scale
- âInference at 30 FPS on minute-long video contexts implies significant GPU cost not disclosed publicly
- âNo documentation of supported operating systems, integrations, or developer tooling beyond the research blog post
Durable - Pros & Cons
Pros
- âGenerates real production code rather than brittle prompt-chained agents, resulting in software that performs like engineer-written systems
- âRequirements-first workflow lets non-technical stakeholders edit automations in plain English without filing engineering tickets
- âSelf-maintaining automations auto-fix API schema changes and rate limits, reducing long-term operational overhead
- âConnects to 50+ named enterprise integrations (Salesforce, Snowflake, HubSpot, Jira, Stripe, Datadog, etc.) plus any API-accessible system
- âVersion history with approval gates (e.g., v2.1.4, v2.1.3) provides audit trail suitable for regulated enterprise environments
- âAI is scoped only where it adds value, avoiding the nondeterminism problems of full LLM-agent architectures
Cons
- âEnterprise-only pricing with no public tiers, free trial, or self-serve signup â every evaluation requires booking a demo
- âNot suitable for solo developers, hobbyists, or small teams without procurement processes
- âNewer platform compared to established automation players like Zapier or Make, with a smaller documented customer base
- âRequires connected systems access upfront, which can slow initial onboarding through enterprise IT and security review
- âLess suitable for simple consumer workflows where a lightweight no-code tool would be faster to deploy
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