Done comparing? Build a summarisation app powered by GPT-5.4.
Build with GPT-5.4 freeGPT-5.4 vs GPT-5.2 Codex for Summarisation
Which AI model is better for summarisation? We compare GPT-5.4 and GPT-5.2 Codex on the criteria that matter most - with a clear verdict.
Why your summarisation LLM choice matters
Effective summarisation requires more than shortening text - it demands identifying what is genuinely important, preserving key nuance, and structuring the output for its intended use. For long documents, large context windows are essential: models that truncate input or hallucinate information they did not actually process are actively counterproductive.
Key evaluation criteria for summarisation
Side-by-Side Comparison
| Feature | GPT-5.4Winner | GPT-5.2 Codex |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Model Type | text | text |
| Context Window | 1,050,000 tokens | 400,000 tokens |
| Input Cost | $2.50/ 1M tokens | $1.75/ 1M tokens |
| Output Cost | $15.00/ 1M tokens | $14.00/ 1M tokens |
| Top pick for Summarisation |
Strengths for Summarisation
GPT-5.4
OpenAI1. Best Intelligence at Scale
- OpenAI positions GPT-5.4 as its frontier model for agentic, coding, and professional workflows.
- Built for complex professional work where stronger reasoning and higher answer quality matter.
2. Configurable Reasoning + Multimodal Input
- Supports configurable reasoning effort from none to xhigh, letting teams balance speed and depth.
- Accepts both text and image inputs while producing text output.
3. Massive Context for Long-Running Work
- 1.05M token context window supports very large codebases, documents, and multi-step workflows.
- Allows up to 128 k output tokens for long-form answers and larger generations.
4. Updated Knowledge & Broad Tool Support
- Knowledge cut-off of Aug 31 2025 keeps it current for newer frameworks and business context.
- Supports tools like web search, file search, code interpreter, hosted shell, computer use, and MCP in the Responses API.
GPT-5.2 Codex
OpenAI1. Optimized for Long-Horizon Coding Tasks
- OpenAI describes GPT-5.2 Codex as a highly intelligent coding model built for long-horizon, agentic coding work.
- Well suited to planning, refactoring, debugging, and multi-step implementation flows inside real codebases.
2. Adjustable Reasoning for Coding Work
- Supports configurable reasoning effort from low to xhigh depending on speed and quality needs.
- Accepts both text and image inputs while producing text output.
3. Large Context + Long Output
- 400 k token context window supports broad repository understanding and larger working sets.
- Allows up to 128 k output tokens for longer patches, code generation, and technical explanations.
4. Up-to-Date Model Snapshot
- Knowledge cut-off of Aug 31 2025 keeps it current with newer tools and frameworks.
- Supports streaming, function calling, and structured outputs for tool-driven coding workflows.
Verdict: Best LLM for Summarisation
For summarisation tasks, GPT-5.4 edges ahead based on its performance profile and design priorities. It scores higher on accuracy and completeness of key information - the criterion that matters most for summarisation workflows.
That said, GPT-5.2 Codex remains a strong option. If reduction ratio without information loss is a higher priority than raw performance, or if your team is already using OpenAI's tooling, GPT-5.2 Codex can deliver strong results for summarisation workloads.
With Appaca, you can build summarisation apps powered by either model and switch between them at any time - no rebuild required. Test what actually performs best for your users before committing.
You know GPT-5.4 wins for summarisation. Now build with it.
Most teams spend days comparing models and hours copy-pasting prompts. With Appaca, you build a dedicated summarisation app - powered by GPT-5.4 - in minutes. No code, no re-prompting, runs on any device.
Free to start. Switch models any time. No rebuild required.
Build a summarisation app with GPT-5.4 - freeFrequently asked questions
Is GPT-5.4 or GPT-5.2 Codex better for summarisation?
For summarisation tasks, GPT-5.4 has the edge based on its performance profile and design priorities. It ranks higher on accuracy and completeness of key information, which is the most important criterion for summarisation workflows. That said, both models can handle summarisation workloads - the best choice depends on your specific requirements and budget.
What are the key differences between GPT-5.4 and GPT-5.2 Codex for summarisation?
The main differences are in accuracy and completeness of key information, context window size for long document handling, structured output formats (bullets, sections). GPT-5.4 is developed by OpenAI and shares the same provider as GPT-5.2 Codex. Context window, pricing, and speed all differ - check the comparison table above for a side-by-side breakdown.
How much does it cost to use GPT-5.4 vs GPT-5.2 Codex?
GPT-5.2 Codex is cheaper at $1.75/million input tokens, versus $2.50/million for GPT-5.4. For summarisation workloads, the total cost difference depends on your average prompt length and volume.
Can I build a summarisation app with GPT-5.4 or GPT-5.2 Codex?
Yes. Both models can power summarisation applications. With Appaca, you can build a summarisation app using either GPT-5.4 or GPT-5.2 Codex - and switch between them at any time to find the model that performs best for your specific workflow, without rebuilding your product.
Which model should I choose if I care most about accuracy and completeness of key information?
GPT-5.4 is the stronger choice when accuracy and completeness of key information is your top priority. It ranks #4 overall for summarisation tasks. If cost or latency are constraints, GPT-5.2 Codex may still meet your needs at a lower cost.