AI Model Comparisons
Choosing GPT-5.5, GPT-5.4, and GPT-5.3 Codex for real workloads
A practical framework for developers and teams deciding when to use GPT-5.3 Codex, GPT-5.4, or GPT-5.5 in production work.
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Contents
OpenAI shipped three major GPT-5 milestones in quick succession during 2026: GPT-5.3-Codex in February, GPT-5.4 in March, and GPT-5.5 on April 23 (with API availability on April 24). For most teams, the hard part is no longer access. The hard part is selecting the right model tier for delivery speed, reliability, and budget.
This article compares GPT-5.3 Codex, GPT-5.4, and GPT-5.5 in one decision framework. The data and positioning below follow official OpenAI releases, Help Center pages, and API documentation visible as of April 30, 2026.
1. Release intent and positioning: what each generation was built to solve
| Model | Official release date | Core positioning (condensed) | Strongest fit |
|---|---|---|---|
| GPT-5.3-Codex | 2026-02-05 | Codex-first model optimized for agentic coding execution | Terminal-heavy engineering, debugging, long code chains |
| GPT-5.4 | 2026-03-05 | First unified frontier model deeply combining reasoning, coding, computer use, and tool search | Cross-tool delivery, multi-file projects, mixed technical/knowledge work |
| GPT-5.5 | 2026-04-23 (API on 2026-04-24) | Current flagship, tuned for complex real-world tasks with stronger planning and self-checking | High-complexity agentic workflows and end-to-end project execution |
The product direction is clear. GPT-5.3-Codex focused on coding specialization, GPT-5.4 shifted toward integrated execution, and GPT-5.5 pushed further into autonomous multi-step work. If your workload crosses coding, research, and operations, the gap between 5.3 and 5.4/5.5 is usually meaningful.
2. Specs, benchmark signals, and cost boundaries
2.1 API-facing specs and pricing
| Dimension | GPT-5.3-Codex | GPT-5.4 | GPT-5.5 |
|---|---|---|---|
| Typical model ID | gpt-5.3-codex | gpt-5.4 | gpt-5.5 |
| Context window | 400,000 | 1,050,000 | 1M |
| Max output | 128,000 | 128,000 | 128,000 |
| Input price (per 1M tokens) | $1.75 | $2.50 | $5.00 |
| Output price (per 1M tokens) | $14.00 | $15.00 | $30.00 |
Pricing note: values above reflect official pricing pages visible on 2026-04-30 and are for reference only. Real spend varies with reasoning depth, tool usage, retry rates, and workflow design.
2.2 Benchmark trends that matter in practice
| Benchmark / metric | GPT-5.3-Codex | GPT-5.4 | GPT-5.5 |
|---|---|---|---|
| Terminal-Bench 2.0 | 77.3% | 75.1% (same-basis comparison shown in GPT-5.5 release material) | 82.7% |
| OSWorld-Verified | 74.0% (updated release-basis value) | 75.0% | 78.7% |
| GDPval (wins or ties) | 70.9% | 83.0% | 84.9% |
These numbers do not replace production evaluation, but the trend is consistent: GPT-5.3-Codex remains strong for coding execution, GPT-5.4 is the major generalist step-up, and GPT-5.5 currently leads when task complexity and autonomy requirements increase.
3. What changes in day-to-day execution
3.1 Coding-intensive delivery
If your work is dominated by command-line workflows, bug fixing, and predictable refactors, GPT-5.3-Codex still delivers strong cost efficiency. GPT-5.4 tends to outperform when project context spans multiple modules and documentation. GPT-5.5 is most valuable when you need fewer intervention points across long execution chains.
3.2 Computer use and agent continuity
GPT-5.4 established a higher baseline for computer-use scenarios. GPT-5.5 improves continuity in longer tasks and handles multi-step intent tracking more reliably, which matters when teams expect the model to keep moving with less operator steering.
3.3 Research and knowledge workflows
For workflows that combine retrieval, synthesis, analysis, and output generation, GPT-5.5 generally offers the strongest ceiling. GPT-5.4 is often the best balance point for mixed daily work. GPT-5.3-Codex is typically better positioned as a coding execution layer than as the primary model for broad knowledge workflows.
4. A deployment pattern that works for most teams
- Route repeatable, well-bounded coding tasks to GPT-5.3-Codex for cost control.
- Keep GPT-5.4 as the default model for mixed engineering and knowledge workflows.
- Escalate to GPT-5.5 for high-stakes, long-context, multi-tool tasks where failure or rework costs are high.
This model-routing approach is usually more effective than trying to standardize on one model for every workload. It aligns capability with risk and keeps spending proportional to task complexity.
5. Conclusion
The progression from GPT-5.3-Codex to GPT-5.4 and GPT-5.5 marks a shift from specialized coding assistance to broader execution intelligence. Teams that define clear routing thresholds now can adopt new model upgrades faster, with less operational friction and fewer cost surprises.