Tools
29 model, cost, benchmark, routing, context, and infrastructure decision tools.
29 standalone tools
Every page is indexable at one canonical URL.| Tool | Category | Question answered | Primary output |
|---|---|---|---|
| Model Selector | Model Selection | Turn workload constraints into a short, inspectable model shortlist instead of a universal best-model claim. | A ranked shortlist with fit reasons, estimated workload cost, sourced limits, and direct model links. |
| LLM Cost Calculator | Token and API Costs | Price one explicit token workload against an exact published provider endpoint. | A component-level cost breakdown using the selected endpoint's published currency and token units. |
| Token Counter | Token and API Costs | Get a fast planning estimate before sending a prompt or document to a tokenizer-backed API. | Estimated tokens, words, characters, context share, and input cost for a selected model. |
| Coding Agent Cost | Token and API Costs | Convert an agent's observed hourly token burn into a monthly cost scenario. | Daily and monthly input, output, and total API spend for the selected model. |
| Context Window | Performance and Operations | Turn a nominal context-window number into a practical fit check for a complete request envelope. | Required tokens, headroom, fit status, and remaining capacity across eligible models. |
| API Usage Calculator | Token and API Costs | Translate request-level product assumptions into monthly token demand and spend. | Requests, input tokens, output tokens, and list-price cost per day and month. |
| Rate Limit Calculator | Performance and Operations | Size an AI API quota from peak behavior rather than monthly averages. | Required requests per minute, tokens per minute, and concurrent requests with reserve capacity. |
| Caching Savings | Token and API Costs | Separate reusable prefix economics from total input volume before adopting prompt caching. | Uncached baseline, cached scenario, monthly savings, and effective input rate. |
| Batch API Savings | Token and API Costs | Quantify the value of trading immediate responses for a batch discount. | Realtime baseline, blended batch scenario, monthly savings, and deferred token volume. |
| Prompt Cost | Token and API Costs | Make the cost of a system prompt, tool schema, or repeated instruction visible before scale. | Estimated prompt tokens, per-call input cost, monthly recurring cost, and annualized spend. |
| Self-Host vs API | Infrastructure and Migration | Find the utilization and volume conditions under which a self-hosted inference cluster can beat an API on direct compute cost. | Monthly infrastructure cost, capacity, effective cost per million tokens, API alternative, and breakeven utilization. |
| GPU Inference Cost | Infrastructure and Migration | Normalize a GPU deployment into cost per million generated tokens. | Hourly and monthly spend, useful token capacity, effective token cost, and idle-cost share. |
| Model Router | Model Selection | Split routine and difficult requests across eligible models without pretending one model is optimal for every call. | Suggested economy and premium routes, traffic allocation, blended cost, and explicit routing rules. |
| Price vs Performance | Model Selection | Screen for models that combine useful published performance with acceptable token economics. | A sortable frontier-style table with benchmark score, coverage, workload cost, and cost per performance point. |
| Benchmark Comparison | Model Selection | Inspect like-for-like published results without blending incompatible metrics or versions. | Common benchmark rows with version, metric, scores, direction, publisher, and source recency. |
| Structured Outputs | Model Selection | Shortlist models for schema-constrained JSON and tool-integrated workflows. | Eligible models ranked by documented support, context fit, provider coverage, benchmark rank, and price. |
| Vision Model Selector | Model Selection | Create a sourced shortlist for image understanding, document analysis, and multimodal prompts. | Vision-capable candidates with context, price, provider coverage, and benchmark rank. |
| Long-Context Models | Model Selection | Filter the catalog by usable request-envelope requirements before comparing cost or benchmark rank. | Passing models ordered by context headroom, output capacity, price, and provider coverage. |
| Coding Model Selector | Model Selection | Shortlist models for code generation, repository agents, review, or debugging under explicit cost and context constraints. | Coding candidates with fit signals, published rank, context, estimated workload cost, and model links. |
| Reasoning Models | Model Selection | Shortlist reasoning models while keeping output-token economics and evidence coverage visible. | Reasoning-capable candidates with performance rank, efficiency rank, context, and workload cost. |
| Embedding Model Selector | Model Selection | Narrow embedding candidates for retrieval, clustering, or semantic search without mixing them with chat models. | Eligible embedding models with context, deployment options, providers, and known input pricing. |
| Embedding Cost | Token and API Costs | Separate one-time corpus embedding cost from recurring refresh and query demand. | Initial tokens and cost, monthly refresh and query cost, vector count, and raw vector storage estimate. |
| Image Generation Cost | Token and API Costs | Convert successful-image demand into actual generated attempts and monthly unit cost. | Generated attempts, API spend, storage spend, monthly total, and cost per accepted image. |
| Fine-Tuning Cost | Infrastructure and Migration | Separate the one-time fine-tuning run from iteration risk and recurring serving spend. | Tokens per run, training cost, experiment budget, monthly inference cost, and first-month total. |
| Model Migration | Infrastructure and Migration | Calculate payback for switching a production workload instead of comparing token prices alone. | Old and new monthly run rates, one-time migration cost, monthly savings, and payback period. |
| API vs Subscription | Infrastructure and Migration | Find the usage level where seat pricing and metered API pricing cross for a team. | Monthly subscription total, API alternative, per-user economics, and breakeven token volume. |
| Monthly AI Spend | Token and API Costs | Build a bottom-up monthly AI budget from product or team behavior. | Organization requests, token demand, per-user cost, monthly total, and annualized run rate. |
| Latency Comparison | Performance and Operations | Normalize latency measurements into user-visible wait time for the same output workload. | Time to first token, generation time, total latency, speedup, and requests-per-minute capacity for routes A and B. |
| Model Pricing History | Performance and Operations | Turn two price snapshots into a comparable workload impact instead of comparing headline rates in isolation. | Before and after monthly cost, absolute and percentage change, annual impact, and weighted effective rate. |
Architecture
One Catalog, Distinct Decisions
Model Selection
10 tools
Token and API Costs
10 tools
Infrastructure and Migration
5 tools
Performance and Operations
4 tools
Model-backed tools share canonical identifiers, provider coverage, context limits, current price observations, and benchmark mappings. Assumption-driven tools label editable inputs rather than presenting estimates as independent measurements.
Questions
AI Tool Questions
Are Model Markets tools free?+
Yes. The tools run without an account. Calculations happen in the browser except when a page loads the source-linked Model Markets catalog used for model facts, prices, and benchmark records.
Where do model prices and specifications come from?+
Model-backed tools reuse the canonical Model Markets catalog. Material values retain primary-source provenance and freshness on their linked model, provider, or benchmark pages. Missing values stay unknown.
Are calculator results exact invoices or performance guarantees?+
No. Results are transparent scenarios built from catalog values and editable assumptions. Exact billing, latency, quality, availability, discounts, rate limits, and contract terms must be verified with the selected provider and workload.