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Build with GPT-5.5 freeGPT-5.5 vs GPT-OSS 20B for Summarisation
Which AI model is better for summarisation? We compare GPT-5.5 and GPT-OSS 20B 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-5.5Winner | GPT-OSS 20B |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Model Type | text | text |
| Context Window | 1,000,000 tokens | 128,000 tokens |
| Input Cost | $5.00/ 1M tokens | $0.00/ 1M tokens |
| Output Cost | $30.00/ 1M tokens | $0.00/ 1M tokens |
| Top pick for Summarisation |
Strengths for Summarisation
GPT-5.5
OpenAI1. Strongest Agentic Coding Model
- State-of-the-art on Terminal-Bench 2.0 (82.7%), Expert-SWE (73.1%), and SWE-Bench Pro (58.6%), outperforming GPT-5.4 on complex coding tasks.
- Holds context across large systems, reasons through ambiguous failures, and carries changes through surrounding codebases with fewer tokens.
2. Higher Intelligence at GPT-5.4 Latency
- Co-designed, trained, and served on NVIDIA GB200/GB300 NVL72 systems to match GPT-5.4 per-token latency while performing at a significantly higher level.
- Uses fewer tokens to complete the same tasks, making it more efficient as well as more capable.
3. Powerful for Knowledge Work & Computer Use
- Scores 84.9% on GDPval (44 occupations) and 78.7% on OSWorld-Verified for autonomous computer operation.
- Excels at generating documents, spreadsheets, and reports; naturally moves across finding information, using tools, and checking output.
4. Scientific Research Co-Scientist
- Leading performance on GeneBench, BixBench, and FrontierMath; helped discover a new proof about Ramsey numbers verified in Lean.
- Strong enough to meaningfully accelerate progress at the frontiers of biomedical and mathematical research.
GPT-OSS 20B
OpenAI- Open-weight / Apache 2.0 licensed: you can use, modify, and deploy freely (commercially & academically) under permissive terms.
- Large model size (≈ 21B parameters) with Mixture-of-Experts (MoE) architecture: only ~3.6B parameters active per token, yielding efficient inference.
- Very long context window support: up to ~128 K tokens (or ~131 K tokens per some sources) enabling in-depth reasoning, long documents, or multi-turn context.
- Adjustable reasoning effort: you can trade latency vs quality by tuning “reasoning effort” levels.
- Efficient hardware requirements (for its class): designed to run on a single 16 GB-class GPU or optimized local deployments for lower latency applications.
- Strong for tasks such as reasoning, tool-use, structured output, chain-of-thought debugging: because the model is open and you can inspect its chain of thought.
- Flexibility: since weights are available, you can self-host, fine-tune, or deploy offline, giving more control than closed API models.
Verdict: Best LLM for Summarisation
For summarisation tasks, GPT-5.5 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-OSS 20B 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-OSS 20B 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-5.5 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-5.5 - 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-5.5 - freeFrequently asked questions
Is GPT-5.5 or GPT-OSS 20B better for summarisation?
For summarisation tasks, GPT-5.5 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-5.5 and GPT-OSS 20B 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-5.5 is developed by OpenAI and shares the same provider as GPT-OSS 20B. 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-5.5 vs GPT-OSS 20B?
GPT-OSS 20B is cheaper at $0.00/million input tokens, versus $5.00/million for GPT-5.5. For summarisation workloads, the total cost difference depends on your average prompt length and volume.
Can I build a summarisation app with GPT-5.5 or GPT-OSS 20B?
Yes. Both models can power summarisation applications. With Appaca, you can build a summarisation app using either GPT-5.5 or GPT-OSS 20B - 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-5.5 is the stronger choice when accuracy and completeness of key information is your top priority. It ranks #3 overall for summarisation tasks. If cost or latency are constraints, GPT-OSS 20B may still meet your needs at a lower cost.