Describe what you're building. ModelSense recommends the right model for every task, with reasoning, cost estimates at your scale, and projected savings vs running GPT-4o for everything.
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No guesswork. No benchmarks spreadsheets. Just paste your idea and get a tailored recommendation in seconds.
Tell ModelSense what you're building: your stack, the features, the scale. The more detail, the sharper the recommendation.
Our engine breaks your project into subtasks and maps each one to the most cost-effective model that can handle it, not just the most popular one.
Each task gets a model pick with reasoning, per-token pricing, and monthly cost estimates at your scale, plus your projected savings vs running GPT-4o for everything.
Your project is broken into distinct AI tasks. Each task gets the most cost-effective model that can handle it. Heavy reasoning tasks get a capable model; simple completions get something cheap and fast.
Each recommendation comes with a plain-English explanation: why that model, what tradeoffs were made, and what you'd lose by going cheaper or spend by going premium.
Monthly totals at light, medium, and heavy request volumes, plus your projected savings vs using GPT-4o for every task. No spreadsheet required.
Best-in-class long-document reasoning at ~60% the cost of GPT-4o; handles multi-page contract context cleanly.
Tell ModelSense about the AI features you're shipping (chat, agents, search, anything that calls a model at runtime) along with your expected usage, and Pro turns that into a full cost picture and model recommendations before you write a line of code.
Pro separates the AI your product calls for its users from the AI you use to build it, so you see your real per-user margins, not just a one-time build estimate.
Every AI feature gets three model recommendations instead of one. Launch cheap and upgrade later, or go premium from day one, without redoing the analysis.
Plug in your expected users and usage per user to get cost projections built around your actual product, not generic per-token math.
Sign up and run one of these through ModelSense yourself.
Contract Intelligence
Extract structured data, flag risk clauses, and answer questions about legal contracts.
Product Discovery
Semantic search across 500k products, personalised recommendations, and review summaries.
PR Review Bot
Automated PR analysis, plain-English code explanations, and codebase Q&A.
Whether you're clicking buttons in a no-code tool or architecting a distributed system, ModelSense saves you money.
You're building fast and don't want to read API pricing pages. ModelSense does the math and tells you exactly what to plug in.
Zero expertise neededYou know the tools but aren't sure which model is overkill for which task. Get confident picks with cost transparency.
Save hours of researchYou know the landscape but want a second opinion backed by data, especially when cost optimization starts mattering at scale.
Data-backed decisionsThe more context you give, the more accurate the model and cost recommendations.
What type of application are you building?
Chatbot, document processor, code assistant, search, agent, etc.
What are the distinct AI tasks?
List each one: extraction, classification, generation, embeddings, summarisation, etc.
What is your expected usage volume?
Requests/day, monthly active users, documents/week.
What are your latency requirements?
Real-time user-facing (<2s) vs background batch processing.
What is your budget?
Monthly ceiling or per-request target.
Do you have any special requirements?
Multimodal input, long documents, tool calling, GDPR/data residency, open-source only, fine-tuning.
What quality bar do you need?
Prototype / internal tool / production / enterprise or regulated industry.
Single provider or multi-model?
Open to using different models per task, or need everything from one vendor.
Example structure
I'm building [type of app] for [target users]. AI tasks I need: - [Task 1: e.g. document summarisation]: [~X req/day], [real-time / batch] - [Task 2: e.g. semantic search]: [~Y req/day], [low latency needed] - [Task 3: e.g. user-facing chat]: [~Z req/day], [<2s response] Scale at launch: ~[X] daily active users, ~[Y] requests/day Quality bar: [prototype / production / regulated / enterprise] Budget: $[X]/month ceiling Tech stack: [language, framework, hosting] Constraints: [open-source only / GDPR / single provider / none]
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