Best LLM for long context in 2025
gpt-4o leads with up to 128k tokens, offering robust performance and broad API support. claude-3-5-sonnet-20241022 is a strong alternative with up to 100k tokens and superior coding benchmarks.RECOMMENDATION
gpt-4o for the best long context support with up to 128k tokens and reliable API integration, making it ideal for extensive document processing and RAG workflows.| Use case | Best choice | Why | Runner-up |
|---|---|---|---|
| Document summarization | gpt-4o | Supports up to 128k tokens, enabling entire books or large reports in one prompt | claude-3-5-sonnet-20241022 |
| Codebase understanding | claude-3-5-sonnet-20241022 | Superior coding benchmarks and 100k token context for large codebases | gpt-4o |
| RAG (Retrieval-Augmented Generation) | gpt-4o | High token limit and fast inference for seamless retrieval integration | gemini-1.5-pro |
| Multimodal long context | gpt-4o | Supports text and image inputs with extended context windows | gemini-1.5-flash |
| Cost-sensitive long context | gpt-4o-mini | Smaller model with moderate long context support at lower cost | mistral-large-latest |
Top picks explained
gpt-4o is the top choice for long context in 2025, offering up to 128k tokens, robust API support, and multimodal capabilities, making it ideal for large document processing and RAG workflows. claude-3-5-sonnet-20241022 excels in coding tasks with a 100k token context window and leads on coding benchmarks, making it perfect for large codebase understanding. gemini-1.5-pro is a strong alternative for retrieval-augmented generation with competitive context length and fast inference.
In practice
Example usage of gpt-4o for processing a long document with extended context:
from openai import OpenAI
import os
client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
long_text = """<Insert your long document text here, up to 128k tokens>"""
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": f"Summarize the following document:\n{long_text}"}]
)
print(response.choices[0].message.content)<summary text of the long document>
Pricing and limits
| Option | Free | Cost | Limits | Context window |
|---|---|---|---|---|
gpt-4o | No free tier | $0.03 / 1K prompt tokens, $0.06 / 1K completion tokens | Max 128k tokens per request | 128k tokens |
claude-3-5-sonnet-20241022 | No free tier | Check pricing at Anthropic.com | Max 100k tokens per request | 100k tokens |
gemini-1.5-pro | No free tier | Check pricing at Google Cloud | Max 64k tokens | 64k tokens |
gpt-4o-mini | No free tier | $0.01 / 1K tokens approx. | Max 32k tokens | 32k tokens |
What to avoid
- Avoid older models like
gpt-3.5-turboorclaude-2as they have limited context windows (4k-8k tokens) unsuitable for long context tasks. - Do not use smaller models without long context support for large documents; they truncate or lose context.
- Beware of models with unclear or limited API support for extended context, which complicates integration.
How to evaluate for your case
Benchmark your use case by preparing representative long documents or codebases and testing models for accuracy, latency, and cost. Use token counting tools to ensure your inputs fit within model limits. Evaluate API stability and integration ease. Consider running coding benchmarks if your use case involves code.
Key Takeaways
- Use
gpt-4ofor the longest context (128k tokens) and best overall API support in 2025. -
claude-3-5-sonnet-20241022is superior for coding tasks with 100k token context and top coding benchmarks. - Avoid deprecated models like
gpt-3.5-turboandclaude-2for long context needs. - Test your specific workload with representative data to confirm model fit and cost efficiency.