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Build with GPT-5.5 freeGPT-5.5 vs DeepSeek R1 for Summarisation
Which AI model is better for summarisation? We compare GPT-5.5 and DeepSeek R1 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 | DeepSeek R1 |
|---|---|---|
| Provider | OpenAI | DeepSeek |
| Model Type | text | text |
| Context Window | 1,000,000 tokens | N/A |
| Input Cost | $5.00/ 1M tokens | N/A |
| Output Cost | $30.00/ 1M tokens | N/A |
| 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.
DeepSeek R1
DeepSeek1. Real-time reasoning and decision-making
- Built for scenarios that require instant output.
- Great for applications with fast-changing data.
2. Excellent for dynamic optimization
- Pricing adjustments, recommendations, routing, and system tuning.
3. Strong performance in finance and e-commerce
- Tracks market shifts.
- Updates predictions on the fly.
- Optimizes recommendations in real time.
4. High-speed pattern recognition
- Quickly interprets signals from streaming data.
- Useful in trading bots, alerts, and monitoring systems.
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, DeepSeek R1 remains a strong option. If reduction ratio without information loss is a higher priority than raw performance, or if your team is already using DeepSeek's tooling, DeepSeek R1 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 DeepSeek R1 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 DeepSeek R1 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 comes from a different provider than DeepSeek R1. 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 DeepSeek R1?
Pricing varies by plan and volume. Check each provider's current API pricing for exact per-token costs for your summarisation use case.
Can I build a summarisation app with GPT-5.5 or DeepSeek R1?
Yes. Both models can power summarisation applications. With Appaca, you can build a summarisation app using either GPT-5.5 or DeepSeek R1 - 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, DeepSeek R1 may still meet your needs at a lower cost.