atom-mcp-server

Cross-vendor price comparison for a specific model or model family.

Shows the same model (or family) priced across different vendors, sorted cheapest first. Essential for cost optimization and vendor selection.

Examples:

• "Compare Llama 3.1 70B pricing across vendors" → model_name="Llama 3.1 70B"

• "Cheapest GPT-4 family output pricing" → model_family="GPT-4", direction="Output"

• "Claude pricing comparison" → model_family="Claude"

AIPI (ATOM Inference Price Index) — chained matched-model price benchmarks for AI inference.

Returns 14 benchmark indexes across four categories:

• Modality (6): Text, Multimodal, Image, Audio, Video, Voice — what does this type of inference cost?

• Channel (4): Model Developers, Cloud Marketplaces, Inference Platforms, Neoclouds — where should you buy?

• Tier (3): Frontier, Budget, Reasoning — what's the premium for capability?

• Special (1): Open-Source — how much cheaper is open-weight inference?

Each index includes input, cached input, and output pricing per period.

These are market-wide benchmarks, not individual vendor prices. Use them to understand where the market is and how it's moving.

Fully public — available to all tiers.

Examples:

• "What's the current benchmark for text inference?" → index_category="Modality"

• "Show me all AIPI indexes" → (no params)

• "Neocloud pricing benchmark" → index_code="AIPI NCL GLB"

• "Channel pricing comparison" → index_category="Channel"

• "Open-source vs market pricing" → index_code="AIPI OSS GLB"

ATOM Inference Price Index (AIPI) market-level KPIs.

Returns 6 key performance indicators derived from live pricing data:

• Output Premium: how much more output tokens cost vs input

• Caching Savings: average discount for cached input pricing

• Open Source Advantage: price difference between open-source and proprietary

• Context Cost Curve: price multiplier for larger context windows

• Caching Availability: % of models offering cached pricing

• Size Spread: price ratio between largest and smallest models

These KPIs are available to all tiers — they demonstrate ATOM's market intelligence.

Aggregate AI inference market intelligence.

Returns total vendor/model/SKU counts, price distribution (median, mean, quartiles, min/max), and modality breakdown. Optionally filter by modality.

Examples:

• "AI inference market overview" → (no params)

• "Text model pricing statistics" → modality="Text"

• "Image generation market stats" → modality="Image"

Deep dive on a single AI model: technical specs + pricing across all vendors.

Returns model_registry data (context window, parameters, open-source status, training cutoff, model family) plus all SKU pricing across every vendor that offers this model.

Examples:

• "Tell me everything about GPT-4o" → model_name="GPT-4o"

• "Claude Sonnet 4.5 specs and pricing" → model_name="Claude Sonnet 4.5"

Full catalog for a specific vendor: all models, modalities, and pricing.

Returns vendor metadata (country, region, pricing page URL) plus every model and SKU they offer.

Examples:

• "What does Together AI sell?" → vendor="Together AI"

• "OpenAI's text model pricing" → vendor="OpenAI", modality="Text"

• "Amazon Bedrock catalog" → vendor="Amazon Bedrock"

List all AI inference vendors tracked by ATOM.

Returns vendor name, country, region, and pricing page URL. Vendors span four channel types: Model Developers, Cloud Marketplaces, Inference Platforms, and Neoclouds. Optionally filter by region or country.

Examples:

• "List all vendors" → (no params)

• "European AI vendors" → region="Europe"

• "Chinese AI vendors" → country="China"

Search and filter AI inference models across 40+ vendors and 1,600+ SKUs.

Query by modality (Text, Image, Audio, Video, Multimodal), vendor, creator, model family, open-source status, price range, context window, and parameter count.

Returns matching models with pricing. Free tier shows count + price range; paid tier shows full details.

Examples:

• "Find open-source text models under $1/M tokens" → open_source=true, modality="Text", max_price=0.001

• "What multimodal models does Google offer?" → vendor="Google", modality="Multimodal"

• "Models with 128K+ context window" → min_context_window=128000