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GPT-5.3 Codex vs Claude Sonnet 4.6: Refactoring Benchmark Shows 3x Cost Difference

Cowrie
Cowrie
Dev @ Bswen

The Problem

I needed to choose an AI coding assistant for a large refactoring project. The same model (Claude Sonnet 4.6) was available through two different harnesses: OpenCode and Claude Code. Plus there was GPT-5.3 Codex via OpenCode.

Which one should I use? The answer surprised me.

What I Tested

I ran the same prompt across three configurations:

Test Prompt
Analyse codebase thoroughly for simplification and deduplication opportunities

The three test configurations:

  • GPT-5.3-Codex via OpenCode harness
  • Claude Sonnet 4.6 via OpenCode harness
  • Claude Sonnet 4.6 via Claude Code harness

Same model, same prompt, different results.

The Results

Benchmark Comparison
┌─────────────────┬───────────────┬──────────────────┬─────────────────────┐
│ Metric          │ GPT-5.3-Codex │ Sonnet (OpenCode) │ Sonnet (ClaudeCode) │
├─────────────────┼───────────────┼──────────────────┼─────────────────────┤
│ Cost            │ $1.44         │ $3.18            │ $3.85               │
│ Time            │ 7 min         │ 15 min           │ 15 min              │
│ API Calls       │ 79            │ 157              │ 136                 │
│ Tokens          │ 4.9M          │ 7.5M             │ 6.9M                │
│ Cache Hit Rate  │ 95%           │ 95%              │ 88%                 │
│ Files Changed   │ 16            │ 8                │ 2                   │
│ Insertions      │ +91           │ N/A              │ N/A                 │
│ Deletions       │ -101          │ N/A              │ N/A                 │
│ Total Edits     │ 192           │ N/A              │ N/A                 │
└─────────────────┴───────────────┴──────────────────┴─────────────────────┘

GPT-5.3 Codex was:

  • 3x cheaper ($1.44 vs $3.18-$3.85)
  • 2x faster (7 min vs 15 min)
  • 8x more files changed (16 vs 2 with Claude Code harness)

Why This Matters

Cost at Scale

For 1000 similar refactoring tasks:

Annual Cost Projection
┌─────────────────────────┬──────────────┐
│ Configuration           │ 1000 Tasks   │
├─────────────────────────┼──────────────┤
│ GPT-5.3-Codex           │ $1,440       │
│ Sonnet 4.6 (OpenCode)   │ $3,180       │
│ Sonnet 4.6 (ClaudeCode) │ $3,850       │
└─────────────────────────┴──────────────┘


Savings with Codex: $1,740 - $2,410 per year

The Harness Effect

The most surprising finding: the same Sonnet 4.6 model performed differently depending on the harness.

Same Model, Different Performance
OpenCode harness:
- Cache hit: 95%
- Files changed: 8
- Cost: $3.18


Claude Code harness:
- Cache hit: 88%
- Files changed: 2
- Cost: $3.85

The harness matters. A 7% difference in cache hit rate translates to real money.

What “More Files Changed” Actually Means

Codex changed 16 files with 192 total edits. Is that good or bad?

I analyzed the output. Codex found:

  • Duplicate utility functions across modules
  • Repeated validation logic in services
  • Similar database query patterns that could be consolidated
  • Dead code in multiple locations

More files changed meant deeper analysis, not random edits. The AI identified patterns across the entire codebase.

The Efficiency Score

I calculated a simple efficiency metric:

Efficiency Calculation
Codex Efficiency = 192 edits / 7 minutes = ~27.4 edits per minute
Codex Cost per Edit = $1.44 / 192 = ~$0.0075 per edit


Claude Code Efficiency = 2 files changed / 15 minutes = minimal

Codex produced meaningful changes at 3/4 of a cent per edit.

Common Mistakes When Choosing AI Tools

I’ve made these mistakes myself:

1. Focusing only on model capabilities

The harness/agentic framework significantly impacts performance. Same model, different results.

2. Ignoring cache efficiency

Lower cache hit rates increase costs dramatically. Claude Code’s 88% vs OpenCode’s 95% seems small but compounds at scale.

3. Assuming newer models are always better

For refactoring specifically, Codex outperformed the newer Sonnet 4.6. Task-specific performance varies.

4. Overlooking token economics

More tokens doesn’t mean better results. Codex used 4.9M tokens and produced more changes. Sonnet via OpenCode used 7.5M tokens with fewer files changed.

When to Use Each Tool

Based on this benchmark:

Use GPT-5.3 Codex (OpenCode) when:

  • Cost efficiency matters
  • Large-scale refactoring is the task
  • You need deep codebase analysis
  • Time is a constraint

Use Claude Sonnet 4.6 (OpenCode) when:

  • You prefer Claude’s reasoning style
  • The task requires different capabilities than refactoring
  • Your team is already invested in the Claude ecosystem

Use Claude Code harness when:

  • You need the integrated IDE experience
  • The visual interface benefits your workflow
  • Cache efficiency is less critical (smaller projects)

The Takeaway

This benchmark taught me something important: tool selection isn’t just about the model.

The same Sonnet 4.6 model performed differently based on the harness. GPT-5.3 Codex, often considered the “budget” option, outperformed Claude in this specific task.

Before committing to a tool for a large project:

  1. Run your own benchmark with your actual codebase
  2. Test different harnesses with the same model
  3. Measure cache hit rates and token efficiency
  4. Evaluate the quality of changes, not just quantity

For my refactoring project, the choice was clear: GPT-5.3 Codex via OpenCode.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!

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