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Does Codex 5.4 Cost More Than 5.3? Token Usage Analysis

Cowrie
Cowrie
Dev @ Bswen

I’ve been seeing conflicting reports about GPT-5.4’s costs. OpenAI claims it’s “more efficient,” but Reddit users say it “chews up usage.” I decided to dig into the actual numbers.

The Confusion

Here’s what developers are hearing:

  • OpenAI says: “GPT-5.4 uses significantly fewer tokens”
  • Users report: “I hit my usage limit way faster”
  • Pricing shows: Higher per-token costs

Which is true? As it turns out, both can be right depending on how you use the model.

The Pricing Reality

| Model           | Input ($/1M) | Output ($/1M) | Context  |
|-----------------|--------------|---------------|----------|
| GPT-5.4         | $2.50        | $15.00        | 1.05M    |
| GPT-5.3-Codex   | $1.75        | $14.00        | 400K     |

GPT-5.4 costs about 43% more per input token. That’s the baseline. But the real question is: how many tokens do you actually use?

The Cost Equation

Total Cost = (Tokens Used) x (Price per Token)

Two factors matter:

  1. Tokens Used - 5.4 can use fewer tokens for the same task
  2. Price per Token - 5.4 costs more per token

The net effect depends on your efficiency gain:

| Token Reduction | Net Cost Effect        |
|-----------------|------------------------|
| 0% (same)       | 5.4 costs 43% MORE     |
| 30% fewer       | 5.4 costs ~10% MORE    |
| 50% fewer       | 5.4 costs ~15% LESS    |

If 5.4 uses half the tokens, you actually save money despite the higher per-token price.

Why Some Users Pay More

I identified three reasons Reddit users report higher costs:

1. Thinking Mode Overuse

reasoning_effort_example.py
# WRONG: Using high for everything
reasoning_effort='high'  # For "fix this typo"


# RIGHT: Match effort to task
reasoning_effort='low'     # Simple fixes
reasoning_effort='medium'  # Standard work
reasoning_effort='high'    # Complex refactors

Using high or xhigh for simple tasks burns tokens unnecessarily.

2. Context Window Temptation

5.4’s 1M context window is tempting. But:

  • Context above 272K tokens = 2x input pricing
  • Loading 300K tokens costs double what 200K costs

3. Scope Creep

5.4 is more capable, so users ask it to do more. That’s not a cost increase - that’s assigning harder work.

Where 5.4 Actually Saves Money

Surgical Edits

5.4 makes minimal changes instead of rewriting entire files:

# 5.3 output: +148 -146 (rewrote the file)
# 5.4 output: +2 -0 (surgical fix)

Fewer output tokens = lower costs.

Fewer Retries

5.4 follows instructions better. I’ve seen:

  • 5.3: 3 iterations to get it right
  • 5.4: 1 iteration, done first try

Each retry is a full API call you don’t have to pay for.

Real Cost Comparison

I ran a comparison on a bug fix task:

| Metric           | 5.3-Codex | 5.4 (Optimized) | 5.4 (Unoptimized) |
|------------------|-----------|-----------------|-------------------|
| Context loaded   | 100K      | 100K            | 300K              |
| Input cost       | $0.175    | $0.25           | $0.60             |
| Output tokens    | 2,000     | 500             | 3,000             |
| Output cost      | $0.028    | $0.0075         | $0.045            |
| Retries          | 2         | 0               | 1                 |
| Total cost       | $0.61     | $0.26           | $1.29             |

Optimized 5.4 costs 58% less than 5.3. Unoptimized 5.4 costs 112% more.

When to Use Each Model

Use GPT-5.4 when:

  • Complex multi-file changes
  • You need surgical, minimal edits
  • Tasks requiring both reasoning and coding
  • You’ll monitor and optimize usage

Stick with GPT-5.3-Codex when:

  • Simple, well-defined tasks
  • Cost is the primary constraint
  • Your 5.3 prompts are already optimized
  • Pure terminal/shell coding work

Quick Optimization Tips

  1. Start with medium thinking mode - not high
  2. Keep context under 272K - avoid the 2x pricing tier
  3. Be specific in prompts - fewer iterations needed
  4. Monitor your usage - set cost alerts in OpenAI dashboard

The Bottom Line

Does 5.4 cost more? It depends on how you use it.

  • Per-token: 43% more expensive
  • Token efficiency: Can use 30-70% fewer tokens
  • Net result: Optimized usage costs less; unoptimized costs more

The users reporting that 5.4 “chews up usage” are likely using it unoptimally. With proper configuration, GPT-5.4 can reduce your costs while delivering better results.

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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