LLM API Pricing Monitor & Tracker

What this Actor scrapes

Seven LLM API providers, one schema — authoritative source pricing pages only:

1. OpenAI — openai.com/api/pricing/ (Cloudflare-protected; we handle the challenge via fingerprint rotation + proxy) 2. Anthropic — docs.anthropic.com model comparison table (not the Webflow marketing page) 3. Google (Gemini API) — ai.google.dev/gemini-api/docs/pricing 4. Mistral — mistral.ai/pricing (Next.js RSC payload — JSON extracted via regex) 5. Groq — groq.com/pricing (server-rendered HTML table) 6. Together AI — www.together.ai/pricing (Webflow list items) 7. DeepSeek — api-docs.deepseek.com/quick_start/pricing (Docusaurus markdown table)

What we handle for you

• Seven providers, one schema — drop straight into a spreadsheet or BI tool with no per-provider normalization on your end.
• Per-1k auto-conversion — every *_usd_per_1m field is normalized regardless of how the provider lists prices on the page.
• Per-provider isolation — one provider failure does not abort the run; the other providers still produce data.
• We handle the blocks — curl-cffi browser TLS fingerprint impersonation rotates across Chrome and Firefox profiles so the target sees real-browser traffic, not Python.
• We rotate proxies on blocks — when a provider page pushes back, we cycle to a fresh residential exit IP and retry before surfacing a partial-success status.
• We retry with exponential backoff on 429 / 503 with Retry-After honoured — up to 5 attempts per provider.
• Pydantic v2 validation — input and output are model-validated; invalid input fails fast with a clear error before any network call.
• Filter knobs — drop deprecated/legacy models, enforce a minimum context window, or restrict the provider list to just the ones you care about.
• Companion to huggingface-hub-scraper as the *AI Stack Intelligence* suite — pair pricing with hub-side model adoption signals.

Use cases

• FinOps cost dashboard — schedule weekly runs, write to a named dataset, and chart input_usd_per_1m over time per model. CloudZero reports the average AI company spends $85k/month on LLMs — a 10% saving is meaningful.
• AI buyer comparison sheets — feed the JSON into a spreadsheet and rank by (input_usd_per_1m + output_usd_per_1m * 2) weighted for the typical 2:1 output/input ratio of your workload.
• Procurement bake-offs — when evaluating Anthropic vs Google vs OpenAI for a new feature, pull the current pricing snapshot rather than relying on a stale screenshot in a PDF.
• Cache-aware budgeting — DeepSeek and Anthropic publish cache-hit pricing separately; this Actor surfaces it (cache_read_usd_per_1m) so you can model the effective rate after caching.
• Batch-mode arbitrage — OpenAI and Anthropic offer ~50% discounts via their batch APIs; surface batch_input_usd_per_1m / batch_output_usd_per_1m directly in your dashboard.
• Investor research — track price-per-token compression at the frontier (Groq, Together, DeepSeek) versus the incumbents (OpenAI, Anthropic, Google) over quarters.
• Open-source pricing leaderboards — feed a public site that ranks the cheapest model meeting a context-window or quality threshold.

Input

Paste this into the Apify Console, or send it as the run input over the API. Proxy settings are on by default; you rarely need to touch them.

{
  "providers": [],
  "includeLegacyModels": false,
  "useProxy": false
}

Output

One row per result, schema-validated before it is written. Export JSON, CSV, Excel or XML from the run, or read it over the API.

providerprovider_display_namemodel_idmodel_display_namepricing_unitinput_usd_per_1moutput_usd_per_1mcache_read_usd_per_1mcache_write_usd_per_1mbatch_input_usd_per_1mbatch_output_usd_per_1mcontext_window_tokensmax_output_tokenstiersource_urlscraped_at

{
  "provider": "anthropic",
  "provider_display_name": "Anthropic",
  "model_id": "claude-sonnet-4-6",
  "model_display_name": "Claude Sonnet 4.6",
  "pricing_unit": "per_1m_tokens",
  "input_usd_per_1m": 3.0,
  "output_usd_per_1m": 15.0,
  "cache_read_usd_per_1m": null,
  "cache_write_usd_per_1m": null,
  "batch_input_usd_per_1m": null,
  "batch_output_usd_per_1m": null,
  "context_window_tokens": 1000000,
  "max_output_tokens": 64000,
  "tier": "standard",
  "source_url": "https://docs.anthropic.com/en/docs/about-claude/models/all-models",
  "scraped_at": "2026-05-16T13:40:00.000Z"
}

Pricing

You pay only for results that land. Cap any run with maxTotalChargeUsd. See pricing & billing for worked examples.

Limitations

• OpenAI Cloudflare challenge — openai.com/api/pricing/ sits behind Cloudflare's JavaScript challenge. We rotate browser fingerprints and residential proxy IPs to clear it; when the challenge cannot be cleared, OpenAI rows are skipped and the run continues with the other six providers (status message indicates partial success). A Camoufox fallback path is planned if the challenge becomes consistent.
• Text-token models only — image generation, speech-to-text, embeddings, and other modality prices are out of scope for v1.
• Authoritative pages only — Azure OpenAI, AWS Bedrock, Vertex AI, and OpenRouter pass-through prices are out of scope.
• No historical tracking — schedule runs and export to your own storage for time-series. Apify's default dataset retention is 7 days on the free plan.
• Provider page restructures — pricing pages change occasionally. Fields that cannot be parsed emit null rather than crashing; the QA fixture (groq + deepseek) catches parser breakage on every publish.