Quantized Execution: Deep Reasoning at 75% Less Cost

Here is an uncomfortable truth about deep reasoning models: they are extraordinarily expensive, and most of that expense is wasted.

When you send a complex task to a frontier model — analyze this codebase, plan this migration, evaluate this architecture — the model spends roughly 80% of its tokens on context gathering. Reading files. Summarizing structures. Building a mental map. Only the remaining 20% goes toward actual reasoning: synthesis, trade-off analysis, the decision.

You are paying premium prices for a model to do the equivalent of reading documentation. That is like hiring a principal engineer to write grep commands.

Quantized execution fixes this by matching the right model tier to the right phase of work.

The Three Tiers

Tier 1: Seeds (Cheap and Fast)

Seeds are lightweight model instances optimized for one job: find information and bring it back. A seed reads a file, extracts the relevant section, and returns it. No analysis. No reasoning about trade-offs.

Seeds are cheap in both token cost and latency. Fifty of them run in parallel for less than one deep reasoning call. Each handles a narrow slice: one reads the auth middleware, another the database schema, another the deployment config.

Cost per seed: ~10 tokens of output. 50 seeds = 500 tokens for Tier 1.

Tier 2: Synthesizers (Mid-Tier)

Synthesizers take raw context from seeds and structure it. A synthesizer receives output from five seeds — auth middleware, session management, token validation, rate limiting, error handling — and produces a single structured analysis of the authentication system.

Mid-tier models: smart enough to identify patterns, not the most expensive option.

Cost per synthesizer: ~100 tokens of output. 10 synthesizers = 1,000 tokens for Tier 2.

Tier 3: The Deep Reasoner (One Call)

One deep reasoning model receives all ten synthesizer outputs. It has a complete, pre-organized picture — not raw files, but distilled knowledge about each subsystem and their interconnections.

This is where actual reasoning happens. The deep model evaluates trade-offs, considers edge cases, weighs architectural implications, and produces the final plan. It does this in a fraction of the normal tokens because context gathering is already done.

Cost for the deep reasoner: ~1,000 tokens of output. 1 call = 1,000 tokens for Tier 3.

The Math

Same quality. Seventy-five percent less cost. Often faster, because seeds and synthesizers run in parallel.

A Real Example: Codebase Migration Analysis

Walk through how quantized execution handles analyzing a 200-file TypeScript codebase for a framework migration.

Phase 1: Seed Deployment

Fifty seeds fan out in parallel. Seeds 1-10 scan route handlers for endpoint signatures and middleware chains. Seeds 11-20 map service dependencies and database calls. Seeds 21-30 catalog test coverage and fixtures. Seeds 31-40 scan configs, builds, and deployments. Seeds 41-50 analyze the import graph and version constraints.

Each returns a small, structured payload. Facts, not analysis.

Time: under 2 seconds. Cost: 500 tokens.

Phase 2: Synthesis

Ten synthesizers receive grouped seed outputs. Synthesizer A produces an API surface summary. B produces a dependency graph with coupling analysis. C maps test coverage with risk flags. D profiles environment and deployment. E through J handle cross-cutting concerns: error handling, auth flows, database patterns, caching, performance-critical paths.

Each produces a schema-conformant analysis with sections, scores, and flagged concerns.

Time: under 3 seconds. Cost: 1,000 tokens.

Phase 3: Deep Reasoning

One deep model receives all ten synthesis documents and produces: a phased migration strategy with dependency ordering, a risk assessment with mitigations, a realistic effort estimate, and a compatibility matrix for existing tests.

Time: under 5 seconds total. Cost: 1,000 tokens.

Total: 2,500 tokens, 5 seconds, one comprehensive migration plan. Running the same analysis through a single deep model would consume 10,000+ tokens as it reads files, backtracks, re-reads, and summarizes internally.

Why This Changes the Economics

AI-intensive workflows do not make one deep reasoning call per day. They make hundreds. At scale, the difference between 10,000 and 2,500 tokens per task is the difference between a viable product and one that bleeds money.

But cost is only half the story. When a deep model gathers its own context, it chooses what to read and what to skip. Those choices are often suboptimal — the model does not know what it does not know. Seeds eliminate this through exhaustive parallel scanning. The deep model receives a complete picture, not a self-selected subset.

Fewer hallucinations. Fewer missed edge cases. Fewer recommendations that collapse when they hit a part of the codebase the model never read.

Better results, faster delivery, seventy-five percent cheaper. That is not incremental improvement. It is a category shift.

What's Next

Quantized execution is built into Codmir's agent orchestration layer. Every complex task automatically routes through the seed-synthesizer-deep pipeline, optimizing cost and quality without manual configuration.

If you are running AI workloads and watching token bills climb, this is the architecture that brings them back to earth. Start building with Codmir and see the cost difference for yourself.