Graspeo vs Router by Ramp: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Graspeo and Router by Ramp — 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
Router by Ramp
Ramp
Ramp's LLM gateway routes each request to the cheapest model meeting your quality bar, cutting inference costs ~40% behind one endpoint and one bill.
Key features
- Cost-Aware Automatic Routing: Every request is matched to the lowest-cost model that still meets your performance requirements, reported to cut inference spend by about 40% on average.
- One Key for Every Model: Closed and open-source models from vetted providers sit behind a single endpoint, key and invoice.
- Rolling Strategy Updates: New cost-saving routing strategies and newly benchmarked default models roll in automatically without changing your integration.
- Score Versus Spend Reporting: Built-in benchmarking shows metric distributions and model summaries so you can see quality and cost side by side.
- Flex Tier Routing Share: A tunable split between default and flexible routing lets you dial how aggressively requests are shifted to cheaper models.
- US-Hosted Providers with ZDR: All vetted providers are US-hosted, with zero-data-retention options for sensitive workloads.
- Switchyard Integration: Works with Switchyard for model and provider routing, surfaced directly in the CLI's cost display.
- One-Command CLI Setup: Install and configure with a single curl command from agents.ramp.com, with an agent-friendly copy-paste flow.
Best for
- Trimming Production Inference Spend: Route high-volume, low-difficulty requests to cheaper models while keeping frontier models for the hard ones — Delphi reports a 92% model cost reduction across billions of tokens.
- Multi-Provider Consolidation: Replace separate OpenAI, Anthropic and open-model integrations with one endpoint and one bill.
- Model Benchmarking Before Migration: Test candidate models against your real workloads and compare score against spend before switching defaults.
- Finance and Engineering Alignment: Give CFOs a single, attributable AI spend line while engineers keep the best model for each workload.
- Compliance-Constrained Deployments: Keep inference on US-hosted providers with zero-data-retention options for regulated data.
- Agent Cost Control: Cap the runaway token spend of long-running agent loops by routing their routine steps to cheaper models automatically.