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Build with GPT-5.5 freeGPT-5.5 vs Gemini 1.5 Pro for Summarisation
Which AI model is better for summarisation? We compare GPT-5.5 and Gemini 1.5 Pro 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 | Gemini 1.5 Pro |
|---|---|---|
| Provider | OpenAI | |
| Model Type | text | text |
| Context Window | 1,000,000 tokens | 1,000,000 tokens |
| Input Cost | $5.00/ 1M tokens | $3.50/ 1M tokens |
| Output Cost | $30.00/ 1M tokens | $7.00/ 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.
Gemini 1.5 Pro
Google1. Breakthrough long-context window up to 1,000,000 tokens
- Can process 1 hour of video, 11 hours of audio, 700k+ words, or 100k+ lines of code in a single prompt.
- Supports advanced retrieval, reasoning, summarization, and cross-document tasks.
- Achieves 99% retrieval accuracy on 1M-token Needle-In-A-Haystack tests.
2. Strong multimodal reasoning across video, audio, images, and text
- Can analyze long videos (e.g., full silent films), track events, infer causality, and identify small details.
- Handles large complex documents like manuals, transcripts, and books.
3. High-performance reasoning and problem solving
- Comparable to Gemini 1.0 Ultra across many benchmarks.
- Excels at code reasoning, multi-step explanations, and large-scale codebase analysis.
4. Advanced code understanding and generation
- Performs problem-solving on codebases exceeding 100,000 lines.
- Capable of cross-file reasoning, debugging guidance, API comprehension, and generating structured code improvements.
5. Efficient Mixture-of-Experts (MoE) architecture
- Activates only relevant expert pathways per input.
- Enables faster training, lower latency, and more efficient serving.
- Dramatically improves scalability and inference speed.
6. Exceptional in-context learning capabilities
- Learns new tasks directly from long prompts without fine-tuning.
- Demonstrated by learning to translate a low-resource language (Kalamang) from a grammar manual.
7. High-fidelity multimodal understanding
- Reads, analyzes, and reasons about long PDFs, code repositories, images, and videos together.
- Enables new classes of applications: legal analysis, scientific review, codebase audits, long-form content generation, etc.
8. Safety and reliability first
- Undergoes extensive ethics, safety testing, and red-teaming.
- Improved representational safety and reduced hallucinations compared to previous generations.
9. Available for developers and enterprises
- Accessible via AI Studio and Vertex AI.
- Supports future pricing tiers for expanded context windows.
- Designed for real enterprise-scale workloads.
10. Widely capable mid-size model
- Positioned between Gemini Pro and Gemini Ultra generations.
- Well-balanced: reasoning, multimodality, long-context, and speed.
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, Gemini 1.5 Pro remains a strong option. If reduction ratio without information loss is a higher priority than raw performance, or if your team is already using Google's tooling, Gemini 1.5 Pro 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 Gemini 1.5 Pro 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 Gemini 1.5 Pro 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 Gemini 1.5 Pro. 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 Gemini 1.5 Pro?
Gemini 1.5 Pro is cheaper at $3.50/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 Gemini 1.5 Pro?
Yes. Both models can power summarisation applications. With Appaca, you can build a summarisation app using either GPT-5.5 or Gemini 1.5 Pro - 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, Gemini 1.5 Pro may still meet your needs at a lower cost.