CodexLog

Appearance

Tactical Model Selection ​

Tactical Model Selection with OpenAI Codex ​

A common mistake among developers is using the most expensive model (GPT-5 or GPT-5-Codex) for every task. While these models provide state-of-the-art reasoning, they are not always the most cost-effective or performant for routine development workflows.

OpenAI itself takes a tiered approach to model deployment: GPT-4o (fast + multimodal), GPT-4 Turbo (cheap + extended context), GPT-3.5 (efficient baseline), and GPT-5 Codex (specialized for coding tasks). Choosing wisely can dramatically reduce costs without sacrificing quality (OpenAI Pricing, 2025).

Model Selection Strategy ​

Context Window Considerations ​

  • GPT-3.5 Turbo: 16k context, lowest cost, good for simple or repetitive coding tasks.
  • GPT-4 Turbo: 128k context, 3x cheaper than GPT-4, well-suited for medium-complexity development and documentation (OpenAI Pricing Update, Nov 2023).
  • GPT-5: 200k context, higher reasoning depth, best for multi-step architectural analysis.
  • GPT-5-Codex: Optimized for agentic coding in IDEs/CLI, integrated with GitHub Code Review (OpenAI Codex Changelog, Sept 2025).

GPT-5 (Most Expensive, Highest Capability, 200k Context) ​

  • Complex architectural decisions requiring deep reasoning
  • Multi-step logical workflows with intricate dependencies
  • Creative problem-solving across entire codebases
  • Code reviews requiring architectural judgment
  • Complex refactoring spanning multiple systems

GPT-4 Turbo (Balanced Cost-Performance, 128k Context) ​

  • Ideal for large codebases where context length matters
  • Feature implementation and iterative development tasks
  • Debugging medium-complexity issues
  • Documentation drafting/editing
  • Extended development sessions without frequent resets

GPT-3.5 Turbo (Cheapest, Fastest) ​

  • Basic file reads and content extraction
  • Routine linting and formatting
  • Simple syntax validation
  • Small, localized bug fixes
  • Quick utility function generation

GPT-5 Codex (Hybrid Intelligence for Coding) ​

  • Specialized for IDE/CLI workflows
  • Supports GitHub PR reviews with execution checks
  • Can plan + execute multi-file refactors more efficiently than GPT-5 general
  • Generates Bash scripts, configs, and tests with faster iteration cycles

Cost Optimization Approach ​

  1. Set GPT-4 Turbo as the default model for daily development.
  2. Switch to GPT-5 only for critical reasoning and architecture tasks.
  3. Use GPT-3.5 Turbo for grunt work (formatting, quick edits).
  4. Leverage GPT-5 Codex for agentic workflows inside IDEs or GitHub.

This mirrors Anthropic's practice of balancing Opus vs. Sonnet vs. Haiku: don't default to the top-tier model.

According to OpenAI's official pricing, GPT-5 is ~5× more expensive than GPT-4 Turbo per token (OpenAI API Pricing, 2025), meaning tactical selection can reduce costs by 60–80% while retaining high output quality.

Context Window Strategy ​

  • Large projects → GPT-4 Turbo (128k context via API)
  • Deep reasoning → GPT-5 (200k context)
  • Simple tasks → GPT-3.5 Turbo for lowest cost
  • Code reviews + automation → GPT-5 Codex

Takeaway: By practicing tactical model selection, developers can optimize for both performance and cost. Instead of letting GPT-5 handle every task, mix Codex + Turbo + 3.5 strategically to achieve scalable, economical development workflows.