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@parisneto parisneto released this 20 Sep 00:58
· 102 commits to main since this release
v0.5.3
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v5_release_notes_infographic_2026-09-17

🚀 Anticharon v0.5.3: The Pricing Engine v2 Release

The final milestone before our public debut!

I am We are thrilled to announce Anticharon v0.5.3, a massive architectural leap that completely rewrites how token economics and model pricing are evaluated. Gone are the days of flat-rate assumptions. Welcome to Pricing Engine v2: cache-aware, policy-compliant, and empirically grounded.

🤖 Give Your Agents Financial Model Pricing Awareness on OpenRouter.ai

Stop letting flat-rate pricing dictate your agent's intelligence. That other more "expensive" model you did not tested, might actually be cheaper than you or your agent thinks. The data to proper calculate is complex and provider fallback, privacy policies and real-world prompt caching affects it.

Anticharon is an Agent-to-Agent (A2A) intelligence engine built to expose the illusion of flat rates Model pricing on OpenRouter.ai (I use and ❤️ them).

Ask your agents to test safely, ephemerally in under a few seconds:

uvx --from git+https://github.com/parisneto/anticharon anticharon mcp

to attach Anticharon as an MCP Server with Tools, Prompts and our llms.txt
You will start to have the data and power to autonomously calculate cache-adjusted token costs, avoid 🐌 CREEPING INFLATION of older models, and safely route to cheaper sibling models.

It's free, fun and safe. I love to get a report up from my agent at 10am, like that :

--------------------------------------------------------------------------

📊 RELATIVE PRICE SPECTRUM ($/1M Tokens):
  ▲ Cheaper
    deepseek/deepseek-v4-flash-0731  $0.01169   ███ 🏆 [BEST]
    qwen/qwen3.7-flash               $0.01195   ███
    google/gemini-2.5-flash-lite     $0.01604   ████
    openai/gpt-5.6-luna              $0.03267   █████████ ★ [DEFAULT]
    google/gemini-3.1-flash-lite     $0.04084   ███████████
    minimax/minimax-m2.7             $0.07699   ████████████████████
    google/gemini-3.8-flash          $0.12140   ████████████████████████████████
    google/gemini-3.7-flash          $0.12140   ████████████████████████████████
  ▼ More Expensive
--------------------------------------------------------------------------

🚨 ALERTS & WARNINGS:
  🟢 [DROP]  Model openai/gpt-5.6-luna price dropped 22.1% vs 7-day MA. Current: $0.03267/1M.
  🟢 [DROP]  Model google/gemini-3.8-flash price dropped 40.7% vs 7-day MA. Current: $0.12140/1M.
  🟢 [DROP]  Model qwen/qwen3.7-flash price dropped 58.5% vs 7-day MA. Current: $0.01195/1M.
  🟢 [DROP]  Model google/gemini-3.1-flash-lite price dropped 56.5% vs 7-day MA. Current: $0.04084/1M.
  🟢 [DROP]  Model google/gemini-2.5-flash-lite price dropped 58.1% vs 7-day MA. Current: $0.01604/1M.
  💡 [TIP]   Model deepseek/deepseek-v4-flash-0731 ($0.01169/1M) is cheaper than configured default openai/gpt-5.6-luna.
==========================================================================

And later I realize that my adult 😉 choices for opting for Zero Data Retention (ZDR) 👮 by adding argument --zdr in the
lovely TUI CLI terminal or whatever agents do over MCP and json keys, to learn that the previous winner, requires my
prompts to train it's model :

qwen/qwen3.7-flash                 $0.01195      $0.02879     -58.5%    
    ↳ advertised: $0.0300 in / $0.1300 out /1M  |  policy **(ZDR): unroutable**

Important Note : this is actual real data as of Sept 19th 2026 and the ZDR enforcement, depends on the provider level, not the model name which we can talk more in another release.

🌟 Highlights & Core Features

1. 🧠 Pricing Engine v2: The 3-Tier Model

Anticharon no longer looks at a single "listed" price. It now evaluates the market through a sophisticated 3-tier lens:

  • 🏷️ Advertised Rates: The baseline manufacturer/provider listed price.
  • ⚡ Effective Cache-Aware Rates: Real-world prices weighted by your actual prompt-caching hit rates. (Because paying for a 95% cached Anthropic prompt shouldn't be calculated at the 0% flat rate!)
  • 🔒 Zero Data Retention (ZDR) Rates: Strict policy-compliant prices. When you use the --zdr flag, the engine routes around non-compliant providers and surfaces the true cost of privacy.

2. 📅 28-Day Historical Backfill & Intelligence

Why guess tomorrow's price when you can measure last month's?

  • Anticharon now backfills 28 days of historical pricing data directly from /stats/effective-pricing.
  • Graceful degradation ensures that newly listed models (🌱 NEWLY_TRACKED) are still evaluated safely without hallucinated data.
  • Empowers the Analytical Intelligence profiles (🛡️ STABLE, ⚡ VOLATILE, 📈 PROMO_ENDED) with deep, empirical foundations.

3. 🛡️ Pre-Flight Advisory Auditor

As we prepare for the public stage, we've integrated a rigorous, fully automated Advisory Auditor agent into the release pipeline:

  • 📜 100% Permissive Licenses: Verified via pip-licenses.
  • 🦠 Zero Known Vulnerabilities: Scanned with pip-audit.
  • 🔑 Zero Leaked Secrets: Full git history scanned via gitleaks detect --redact.
  • 📂 Repository Exposure Review: Explicit human-in-the-loop validation of exposed assets (git ls-files).

4. 🧪 Deterministic Behavior-Based Testing

We evolved our testing directives to guarantee reliability without external network flakiness.

  • Over 160 offline, deterministic tests proving parsing, normalization, and failure degradation.
  • Real-world trimmed JSON payloads (like gpt-5.6-luna) used as fixtures to ensure API schema changes are caught before they break your agents.

🛠️ Deep Dive: The PE2 Validation Gauntlet

This release survived an independent 6-round audit from our Reviewer Agents. Key wins include:

  • PE2-001: Unconstrained effective prices and ZDR policy prices no longer artificially collapse together. You always get an apples-to-apples comparison.
  • PE2-002: Missing or partial provider pricing no longer fabricates a $0 endpoint.
  • PE2-003: Policy-unknown endpoints degrade gracefully instead of being falsely reported as confirmed non-compliant.
  • PE2-006: Ironclad network failure paths ensuring timeouts or HTTP errors degrade to safe, documented empty states instead of crashing your upstream pipelines.

🤝 Co-Authored By The Team

This release was a masterclass in human-agent collaboration. The architectural shift, meticulous regression testing, and release audits were driven in tandem by the multi-agent ensemble:

  • Páris Piedade Neto
  • Claude Sonnet 5
  • Codex
  • Antigravity

Ready to optimize your agents? Run uv run anticharon check and watch the new engine spin up!

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