Best LLM for text summarization
Quick answer
For text summarization, use
gpt-4o from OpenAI for its strong comprehension and concise output. Anthropic's claude-3-5-sonnet-20241022 is a close alternative with robust summarization capabilities and a helpful system prompt design.RECOMMENDATION
For best-in-class text summarization, use
gpt-4o via the OpenAI API due to its balance of accuracy, speed, and cost efficiency.| Use case | Best choice | Why | Runner-up |
|---|---|---|---|
| General text summarization | gpt-4o | High accuracy and fluent summaries with strong context understanding | claude-3-5-sonnet-20241022 |
| Summarizing long documents | gemini-2.5-pro | Supports longer context windows and detailed summaries | gpt-4o |
| Cost-sensitive summarization | gpt-4o-mini | Lower cost with reasonable summary quality for shorter texts | claude-3-5-haiku-20241022 |
| Enterprise integration with Azure | gpt-4o (Azure deployment) | Seamless Azure integration and compliance | gpt-4o-mini (Azure) |
| Multilingual summarization | gpt-4o | Strong multilingual support and nuanced language understanding | gemini-2.5-pro |
Top picks explained
For general text summarization, gpt-4o is the top choice due to its advanced language understanding and ability to produce concise, coherent summaries. It handles diverse topics and languages well.
claude-3-5-sonnet-20241022 from Anthropic is a strong alternative, especially when you want a helpful assistant style with system prompt customization. It excels in clarity and factuality.
gemini-2.5-pro by Google is ideal for very long documents thanks to its extended context window, enabling detailed and structured summaries.
In practice
import os
from openai import OpenAI
client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
messages = [
{"role": "user", "content": "Summarize the following article:\n\nArtificial intelligence is transforming industries by automating tasks and enabling new capabilities..."}
]
response = client.chat.completions.create(
model="gpt-4o",
messages=messages
)
summary = response.choices[0].message.content
print("Summary:", summary) output
Summary: Artificial intelligence is revolutionizing industries by automating tasks and unlocking new capabilities, driving efficiency and innovation across sectors.
Pricing and limits
| Option | Free | Cost | Limits | Context window |
|---|---|---|---|---|
gpt-4o (OpenAI) | Yes, limited free tier | $0.03 / 1K tokens (prompt), $0.06 / 1K tokens (completion) | Max 8K tokens per request | 8,192 tokens |
claude-3-5-sonnet-20241022 (Anthropic) | Yes, limited free tier | Approx. $0.03 / 1K tokens | Max 100K tokens context | 100,000 tokens |
gemini-2.5-pro (Google Vertex AI) | No free tier | Check Google Cloud pricing | Up to 128K tokens context | 128,000 tokens |
gpt-4o-mini (OpenAI) | Yes, limited free tier | $0.002 / 1K tokens | Max 4K tokens per request | 4,096 tokens |
What to avoid
- Avoid older models like
gpt-3.5-turboorclaude-2as they lack the improved summarization quality and context length of newer models. - Do not use models with very small context windows for long documents, as summaries will be incomplete or inaccurate.
- Avoid free open-source models without fine-tuning for summarization if you need production-grade quality.
Key Takeaways
- Use
gpt-4ofor the best balance of accuracy, speed, and cost in text summarization. - For very long documents, prefer
gemini-2.5-produe to its extended context window. - Avoid deprecated models like
gpt-3.5-turbofor production summarization tasks. - Anthropic's
claude-3-5-sonnet-20241022offers a helpful alternative with strong factuality. - Always consider context window size relative to your document length for best results.