Tools
29 model, cost, benchmark, routing, context, and infrastructure decision tools.
29 standalone tools
Every page is indexable at one canonical URL.| Tool | Decision | Output |
|---|---|---|
| Model Selection10 tools | ||
| Benchmark Comparison | Inspect like-for-like published results without blending incompatible metrics or versions. | Common benchmark rows with version, metric, scores, direction, publisher, and source recency. |
| Coding Model Selector | 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. |
| Embedding Model Selector | 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. |
| Long-Context Models | 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. |
| Model Router | 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. |
| Model Selector | 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. |
| Price vs Performance | 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. |
| Reasoning Models | Shortlist reasoning models while keeping output-token economics and evidence coverage visible. | Reasoning-capable candidates with performance rank, efficiency rank, context, and workload cost. |
| Structured Outputs | 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 | Create a sourced shortlist for image understanding, document analysis, and multimodal prompts. | Vision-capable candidates with context, price, provider coverage, and benchmark rank. |
| Token and API Costs10 tools | ||
| API Usage Calculator | Translate request-level product assumptions into monthly token demand and spend. | Requests, input tokens, output tokens, and list-price cost per day and month. |
| Batch API Savings | Quantify the value of trading immediate responses for a batch discount. | Realtime baseline, blended batch scenario, monthly savings, and deferred token volume. |
| Caching Savings | Separate reusable prefix economics from total input volume before adopting prompt caching. | Uncached baseline, cached scenario, monthly savings, and effective input rate. |
| Coding Agent Cost | 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. |
| Embedding Cost | 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 | 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. |
| LLM Cost Calculator | 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. |
| Monthly AI Spend | 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. |
| Prompt Cost | 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. |
| Token Counter | 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. |
| Infrastructure and Migration5 tools | ||
| API vs Subscription | 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. |
| Fine-Tuning Cost | 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. |
| GPU Inference Cost | 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 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. |
| Self-Host vs API | 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. |
| Performance and Operations4 tools | ||
| Context Window | 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. |
| Latency Comparison | 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 | 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. |
| Rate Limit Calculator | 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. |
Method
One Catalog, Distinct Decisions
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.