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TokenLab MCP vs Linum

A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.

TokenLab MCP compared with Linum
DetailTokenLab MCP logo TokenLab MCPLinum logo Linum
What it doesConnect TokenLab to Claude, Codex, Cursor and other MCP clients so agents can search models, compare prices, chat, generate images and videos Generative video models for animation
StageNot given Not given
FoundedNot given Not given
Based inUS US
Pricing modelFree Not given
PricingMCP service is free. Usage charges apply only when calling models at standard TokenLab pricing rates. Pricing for first-time recharge: starting at US$5 to US$100; first recharge of US$10 includes US$5 bonus Not given
Key features
  • Model search and catalog browsing with 6 query tools
  • Model pricing comparison without API key required
  • Chat completion with multiple model providers
  • Image generation and editing tools
  • Video generation and creation
  • File upload and management tools
  • Embedding and document reranking
  • Three tool profile options: catalog, core (32 tools), full (89 tools)
  • Hosted and local deployment options
  • Pyramid-JiT (P-JiT) architecture with 32×32 token reduction
  • 4× more aggressive token reduction than Linum v2
  • Converges in 11× fewer samples than Linum v2
  • 2B-parameter model with frozen Qwen text encoder
  • Pixel-space diffusion with multi-resolution readouts
  • PixelREPA alignment with DINOv3-L
  • Curriculum learning over noise schedule
What makes it different
  • Free MCP service with pay-as-you-go model pricing, no subscriptions or seat fees
  • No API key required for model search and price comparison with catalog tier
  • 32 default tools sufficient for most agent tasks including chat, image and video generation
  • Integrates with popular MCP clients: Claude Desktop, Codex, Cursor, VS Code
  • Works standalone without requiring TokenLab Skills installation
  • Low-cost training: full 2B model trained for just $21K in compute
  • Controls baked into model training rather than post-hoc fine-tuning
  • Pixel-space compression without separate VAE bottleneck
  • Apache 2.0 open release with full model weights and code
Free planNot given Not given
Open sourceNot given Not given
PlatformsNot given Not given
Public APINot given Not given
Community votes00
DR (Domain Rating by Ahrefs)15 −2 (down) since last check44 +1 (up) since last check
Trust Flow (Majestic)45
Citation Flow (Majestic)3432
Referring domains (Majestic)128397

4 details filled in for both companies.

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