Graspeo vs Switchyard: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Graspeo and Switchyard — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Graspeo
Graspeo
Free AI quiz generator that creates quizzes from PDFs, text, or YouTube videos in seconds.
Key features
- Multi-Source Quiz Generation: Generates quizzes from uploaded PDFs, pasted text, or YouTube video links by extracting key points and converting them into questions.
- Instant Creation: Produces complete quizzes in seconds to accelerate preparation of practice tests and classroom assessments.
- Starter Credits: New users receive 100 free credits to experiment with quiz creation without immediate payment or commitment.
- Student & Teacher Focus: Outputs are tailored for study, homework, classroom use, and test preparation to support both learners and educators.
- Web-Based Accessibility: Operates entirely online so users can create quizzes from any device with a browser without installing software.
- Generate quizzes from PDFs
- Generate quizzes from plain text
- Generate quizzes from YouTube videos
- Instant quiz creation via web interface
- Starter allocation of 100 free credits
Best for
- Converting lecture PDFs into practice quizzes for classroom assignments and homework.
- Transforming YouTube lecture videos into question sets for revision and active learning.
- Generating self-assessment quizzes from textbook excerpts or pasted notes to guide study sessions.
- Rapid creation of formative assessments for teachers to evaluate student understanding after a lesson.
- Building question banks from multiple PDF chapters for targeted exam preparation and review.
- Teachers creating assessments from lecture slides or readings
- Students generating practice quizzes from textbooks or notes
- Test-preparation users converting study materials into question sets
- Converting YouTube lecture/video content into interactive quizzes
Switchyard
NVIDIA
An open-source Rust proxy and library that routes LLM traffic across models and providers while preserving native OpenAI and Anthropic API compatibility.
Key features
- Protocol Translation: Converts between OpenAI Chat Completions, OpenAI Responses and Anthropic Messages formats so clients keep their native API while any backend serves the request.
- Multi-Backend Routing: Spreads traffic across vLLM, NVIDIA NIM, Ollama and any OpenAI-compatible endpoint, letting you point an existing coding agent at an open-source model without changing the agent.
- LLM Classifier Router: Uses request content to decide whether a given turn needs the weak or the strong model tier, cutting spend on turns that do not need frontier capability.
- Stage Router: Routes most turns from signals already in the conversation — tool results, errors, conversation stage — so no extra model call is needed to make the decision.
- Escalation Router: Runs every turn on the weak tier first, then has a judge read that answer and decide whether the same request should be re-sent to the strong tier.
- Random Routing for A/B Tests: Applies a fixed traffic split across targets for benchmarking, baselines and cost experiments.
- Operational Metrics: Exposes Prometheus metrics for requests, errors, latency, token counts and the overhead added by routing itself.
- Server or Library Deployment: Run it as a standalone Rust proxy configured by routes.toml, or embed switchyard-libsy in your own application so it decides the target and hands the model call back to you.
Best for
- Pointing Coding Agents at Open Models: Serve Claude Code or Codex from vLLM, NIM or Ollama without the agent knowing the API changed.
- Cost/Performance Optimization: Send routine turns to a cheap weak-tier model and reserve the strong tier for turns a classifier or judge says need it.
- Model A/B Benchmarking: Split traffic on a fixed ratio across two models to compare quality, latency and cost on real production requests.
- Provider Migration and Failover: Keep application code on one API shape while swapping or mixing the providers behind it.
- Embedding Routing in an Agent Runtime: Drop the routing algorithms into an existing gateway or agent framework via the library path without adopting a new HTTP stack.
- Operational Visibility: Track per-route latency, error rates and token spend through Prometheus to find which routes are actually costing money.