Author's Note: An in-depth comparison of GPT-5.4 and GPT-5.3 Codex programming capabilities, featuring benchmark data from SWE-Bench, Terminal-Bench, and 4 other tests to help you choose the best programming model.
GPT-5.4 has just been released, and the first question on many developers' minds is: Do I still need GPT-5.3 Codex? After all, GPT-5.4 is touted as the "first unified model combining programming, reasoning, and computer control," while GPT-5.3 Codex is OpenAI's flagship model specifically built for coding.
Core Value: This article uses hard data from 6 benchmark tests, combined with a comprehensive comparison of pricing, context, and suitable scenarios, to help you make the clearest choice.
GPT-5.4 vs GPT-5.3 Codex Programming Core Takeaways
| Comparison Dimension | GPT-5.4 | GPT-5.3 Codex | Winner |
|---|---|---|---|
| SWE-Bench Pro | 57.7% | 56.8% | GPT-5.4 |
| Terminal-Bench 2.0 | 75.1% | 77.3% | GPT-5.3 Codex |
| Toolathlon | 54.6% | 51.9% | GPT-5.4 |
| BrowseComp | 82.7% | 77.3% | GPT-5.4 |
| OSWorld | 75.0% | 74.0% | GPT-5.4 |
| Input Price | $2.50/M | $1.75/M | GPT-5.3 Codex |
The One-Sentence Conclusion for GPT-5.4 vs GPT-5.3 Codex Programming
GPT-5.4 leads comprehensively on combined benchmarks, but GPT-5.3 Codex is still stronger and cheaper for pure coding tasks. Which one you choose depends on your use case—whether you're just writing code or working in a mixed programming + other workflow.
OpenAI's official recommendation is also clear: Start with GPT-5.4 for most tasks, and use GPT-5.3 Codex for pure programming-intensive tasks.
GPT-5.4 vs GPT-5.3 Codex Detailed Programming Benchmark Analysis
SWE-Bench Pro: GPT-5.4 Edges Ahead
SWE-Bench Pro is a more challenging, private codebase variant specifically designed to resist benchmark data contamination. GPT-5.4 leads GPT-5.3 Codex by a narrow margin of 57.7% to 56.8%, a difference of about 1 percentage point.
This gap isn't huge, but considering GPT-5.4 is a general-purpose model and not specialized for programming, outperforming a coding expert model on SWE-Bench Pro itself speaks volumes about the depth of its integrated coding capabilities.
Terminal-Bench 2.0: GPT-5.3 Codex Takes a Clear Lead
Terminal-Bench 2.0 is a hardcore test of pure terminal programming ability. GPT-5.3 Codex leads by 2.2 percentage points with a score of 77.3% vs. 75.1%—this is the benchmark where GPT-5.3 Codex wins most decisively.
This result makes perfect sense: GPT-5.3 Codex is specifically optimized for "agentic coding," giving it a natural advantage in vertical scenarios like pure code generation, code completion, and terminal operations.
Toolathlon and BrowseComp: GPT-5.4 Dominates
In tests involving tool calling (Toolathlon 54.6% vs. 51.9%) and browser interaction (BrowseComp 82.7% vs. 77.3%), GPT-5.4 comes out on top across the board. This reflects GPT-5.4's advantage in comprehensive "beyond-coding" agent capabilities—calling tools, operating browsers, and collaborating across applications.
GPT-5.4 vs GPT-5.3 Codex Programming Pricing & Specs Comparison
Price difference is a core concern for many developers. Here's a complete specs comparison of the two models:
| Spec Dimension | GPT-5.4 | GPT-5.3 Codex | Difference |
|---|---|---|---|
| Input Price | $2.50/M tokens | $1.75/M tokens | Codex is 30% cheaper |
| Output Price | $15.00/M tokens | $14.00/M tokens | Codex is 7% cheaper |
| Cached Input | $0.25/M tokens | Not Public | GPT-5.4 supports it |
| Context Window | 1,050K tokens | 400K-1M tokens | GPT-5.4 is larger |
| Max Output | 128K tokens | Not explicitly public | — |
| Computer Use | ✅ Native Support | ❌ Not Supported | GPT-5.4 Exclusive |
| Tool Search | ✅ Saves 47% Tokens | ❌ Not Supported | GPT-5.4 Exclusive |
| Positioning | General-Purpose Flagship | Programming-Specialized | Different Focus |
GPT-5.4 vs GPT-5.3 Codex Real-World Programming Cost Calculation
While GPT-5.3 Codex has a lower per-unit price, GPT-5.4 has two mitigating factors:
- Fewer Reasoning Tokens: OpenAI officially states that GPT-5.4 "solves the same problems with significantly fewer reasoning tokens," meaning actual costs could be similar or even lower.
- Tool Search Saves 47%: For agent workflows that frequently call tools, GPT-5.4's token consumption is significantly reduced.
Conclusion: If your tasks are primarily pure code generation and completion, GPT-5.3 Codex is more cost-effective. If you're dealing with a mixed workflow involving programming + tool calling + browser operations, GPT-5.4 might actually offer better real-world cost efficiency.
Pricing Reference: Both models can be accessed via APIYI at apiyi.com, with prices synchronized to the official rates. Sign up and use immediately, with a top-up of $100 or more receiving a 10%+ bonus credit.
GPT-5.4 vs GPT-5.3 Codex: Differences in Programming Design Philosophy
To make the right choice, you need to understand the design intent behind each model.
GPT-5.3 Codex: Built for "Agentic Programming"
When GPT-5.3 Codex was released in February 2026, OpenAI's positioning was crystal clear—it's a "highly productive intern" level programming partner. Core characteristics:
- Autonomous Task Completion: Doesn't need step-by-step hand-holding; give it a task and it runs with it.
- Self-Correction Loop: Write code → run tests → find errors → fix → test again. The whole cycle happens automatically.
- Interruptible and Redirectable: You can interrupt it at any time, change direction, and it won't lose context.
- 25% Faster than GPT-5.2 Codex: Speed optimization was a core selling point.
GPT-5.4: A Unified Whole of Programming, Reasoning, and Control
GPT-5.4 isn't just a simple programming model upgrade; it's OpenAI's attempt at a "grand unification"—stuffing programming capability, deep reasoning, computer control, and specialized knowledge all into one model. Core characteristics:
- Integrated Codex Programming: OpenAI explicitly states GPT-5.4 "integrates the cutting-edge coding capabilities of GPT-5.3 Codex."
- Native Computer Use: Can directly control a computer interface, not just generate code.
- Specialized Knowledge Work: GDPval 83.0%, 87.3% accuracy on investment banking tasks.
- Simplified Model Selection: OpenAI hopes GPT-5.4 will replace multiple specialized models, reducing choice paralysis.
GPT-5.4 vs GPT-5.3 Codex: A Guide to Choosing the Right Model for Your Programming Scenario
OpenAI's official documentation provides clear model selection advice:
| Use Case | Recommended Model | Reason |
|---|---|---|
| Most Codex Tasks (Default) | GPT-5.4 | Strongest overall capability, OpenAI's recommended default choice. |
| Mixed Workflows (Programming + Planning + Writing) | GPT-5.4 | Cross-domain capabilities far exceed Codex. |
| Pure Programming-Intensive Tasks | GPT-5.3 Codex | Higher Terminal-Bench score (77.3%), specifically optimized for coding. |
| Real-time Pair Programming | GPT-5.3 Codex Spark | Extremely fast response (1000+ tokens/s) – Pro tier exclusive. |
| Budget-Sensitive Programming Tasks | GPT-5.3 Codex | Input pricing is 30% cheaper. |
| Large Codebase Analysis | GPT-5.4 | Largest context window (1.05M tokens). |
| Frontend UI Development | GPT-5.4 | Community feedback indicates its UI code is more polished and feature-complete. |
| Backend Automation Agents | GPT-5.4 | Native Computer Use + Tool Search capabilities. |
GPT-5.4 vs GPT-5.3 Codex: Developer Community Feedback
Real-world feedback from the developer community:
- Cursor Team (Lee Robinson): "GPT-5.4 is currently leading in our internal benchmarks. Engineers find it more natural, more decisive, and it doesn't hesitate when faced with ambiguous problems."
- Reddit Developer Consensus: GPT-5.3 Codex is stronger for rapid iteration and implementation loops; for complex system design and architectural planning, the preference leans towards other models.
- Frontend Development Scenarios: GPT-5.4 is considered "significantly better at complex frontend coding tasks, producing more aesthetically pleasing and functionally complete results."
GPT-5.4 vs GPT-5.3 Codex: Quick Start for Programming
Minimal Example: Switching Models in Codex CLI
# Method 1: Codex CLI Command Line Switch
# Using GPT-5.4 (Recommended Default)
codex --model gpt-5.4 "Refactor this function into an async version"
# Using GPT-5.3 Codex (Pure Programming Tasks)
codex --model gpt-5.3-codex "Fix all failing unit tests"
# Method 2: API Call Comparison
from openai import OpenAI
client = OpenAI(
api_key="YOUR_API_KEY",
base_url="https://vip.apiyi.com/v1"
)
# GPT-5.4: Suitable for Mixed Workflows
response = client.chat.completions.create(
model="gpt-5.4",
messages=[{"role": "user", "content": "Analyze this code and generate unit tests"}]
)
# GPT-5.3 Codex: Suitable for Pure Programming Tasks
response = client.chat.completions.create(
model="gpt-5.3-codex",
messages=[{"role": "user", "content": "Implement a high-performance LRU Cache"}]
)
Recommendation: Use the unified interface via APIYI apiyi.com to call both models. You don't need to switch API Keys or Base URLs, making it easy to compare results and choose the right model for your project's needs.
Frequently Asked Questions
Q1: Will GPT-5.4 completely replace GPT-5.3 Codex?
No, it won't completely replace it. OpenAI's official documentation still lists both as available Codex models. GPT-5.4 replaces GPT-5.3 Codex Spark as the "recommended default model," but GPT-5.3 Codex retains its place due to its cost-effectiveness for pure coding scenarios. For budget-sensitive, pure programming tasks, GPT-5.3 Codex remains the better choice.
Q2: How do I switch between these two models in the Codex CLI?
It's very simple. Use the /model command in the Codex CLI for a hot-swap: type /model gpt-5.4 or /model gpt-5.3-codex. You can also set a default model in ~/.codex/config.toml or specify it at launch with the --model parameter. The same API Key from APIYI apiyi.com works for both.
Q3: How can I quickly test and compare the programming results of both models?
Recommended steps:
- Visit APIYI apiyi.com to register an account and get a unified API Key.
- Prepare a typical programming task (e.g., "Implement an LRU Cache" or "Refactor an async function").
- Make calls using
model="gpt-5.4"andmodel="gpt-5.3-codex"separately. - Compare the quality, speed, and token consumption of the generated code.
Summary
Key conclusions on GPT-5.4 vs GPT-5.3 Codex programming capabilities:
- GPT-5.4 is Stronger Overall: Wins 4 out of 6 benchmarks (SWE-Bench Pro, Toolathlon, BrowseComp, OSWorld) and is OpenAI's recommended default choice.
- GPT-5.3 Codex is More Specialized for Pure Coding: Leads by 2.2 percentage points on Terminal-Bench (77.3%), remaining optimal for pure code generation and terminal programming.
- Significant Price Gap: GPT-5.3 Codex input is 30% cheaper ($1.75 vs $2.50), offering a major advantage for budget-sensitive scenarios.
- GPT-5.4 Exclusive Capabilities: Native Computer Use and Tool Search (-47% Tokens) are features GPT-5.3 Codex lacks.
In short: Most developers should use GPT-5.4; for pure coding where cost matters, use GPT-5.3 Codex. Both models are now available on APIYI apiyi.com with a unified interface for easy switching. Sign up and start using them today.
📚 References
-
OpenAI GPT-5.4 Announcement: GPT-5.4 core capabilities and benchmark data.
- Link:
openai.com/index/introducing-gpt-5-4/ - Description: Official launch blog, includes benchmarks like SWE-Bench Pro and Terminal-Bench comparisons.
- Link:
-
OpenAI GPT-5.3 Codex Announcement: Design philosophy of the agentic programming model.
- Link:
openai.com/index/introducing-gpt-5-3-codex/ - Description: Explanation of GPT-5.3 Codex's positioning, capabilities, and use cases.
- Link:
-
OpenAI Codex Model Documentation: Official model selection guide.
- Link:
developers.openai.com/codex/models/ - Description: Includes official usage recommendations for GPT-5.4 and GPT-5.3 Codex.
- Link:
-
OpenAI API Pricing Page: Latest model pricing information.
- Link:
openai.com/api/pricing/ - Description: Official price comparison for GPT-5.4 and GPT-5.3 Codex.
- Link:
Author: APIYI Technical Team
Technical Discussion: Feel free to discuss your experiences with GPT-5.4 and GPT-5.3 Codex in the comments. For more resources, visit the APIYI documentation center at docs.apiyi.com.
