Which LLM has the biggest context window
gpt-4o with support for up to 128k tokens. Other notable models like claude-3-5-sonnet-20241022 and gemini-2.5-pro also offer large context windows up to 100k tokens, enabling extensive document processing and long conversations.RECOMMENDATION
gpt-4o which supports up to 128k tokens, ideal for processing very long documents or extended chat sessions.| Use case | Best choice | Why | Runner-up |
|---|---|---|---|
| Long document analysis | gpt-4o | Supports up to 128k tokens, enabling processing of entire books or large reports in one pass. | claude-3-5-sonnet-20241022 |
| Extended chat sessions | gpt-4o | 128k token window allows maintaining long conversational context without losing earlier messages. | gemini-2.5-pro |
| Multi-document summarization | claude-3-5-sonnet-20241022 | Up to 100k tokens context window, excellent for aggregating insights across many documents. | gpt-4o |
| Codebase understanding | gpt-4o | Large context window helps analyze large codebases or multiple files simultaneously. | claude-3-5-sonnet-20241022 |
Top picks explained
gpt-4o leads with a massive 128k token context window, making it the best choice for any task requiring very long input sequences, such as entire books or extended conversations. claude-3-5-sonnet-20241022 and gemini-2.5-pro offer up to 100k tokens, which is still exceptional and suitable for multi-document tasks and long chats.
Choose gpt-4o when you need the absolute maximum context size. Use claude-3-5-sonnet-20241022 for tasks that benefit from Claude's reasoning and safety features with a large window. gemini-2.5-pro is a strong alternative with competitive context size and multimodal capabilities.
In practice
Use gpt-4o to process a long document by passing it as a single message within the 128k token limit. Here's a Python example using the OpenAI SDK v1+:
import os
from openai import OpenAI
client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
long_text = """Your very long document text here..."""
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": long_text}]
)
print(response.choices[0].message.content)Summary or response based on the long document text
Pricing and limits
| Option | Free availability | Cost | Token limit | Context window |
|---|---|---|---|---|
gpt-4o | Freemium (check OpenAI pricing) | Approx. $0.03 / 1K tokens (varies by usage) | 128k tokens | 128k tokens |
claude-3-5-sonnet-20241022 | Freemium (check Anthropic pricing) | Varies, typically competitive | 100k tokens | 100k tokens |
gemini-2.5-pro | Freemium (Google Cloud pricing applies) | Varies by usage | 100k tokens | 100k tokens |
What to avoid
Avoid older or smaller models like gpt-4o-mini or gpt-4o-mini for tasks needing large context windows, as they typically support only up to 8k or 32k tokens. Using these will force you to chunk inputs, losing context continuity.
Also, beware of models with undocumented or limited context sizes, which can cause truncation or errors when exceeding limits.
How to evaluate for your case
Benchmark your use case by testing your longest expected input against the model's token limit. Use tokenizers like tiktoken to count tokens before sending requests. Measure latency and output quality when approaching the context window limit to ensure performance meets your needs.
import tiktoken
encoder = tiktoken.encoding_for_model("gpt-4o")
text = "Your long input text here"
token_count = len(encoder.encode(text))
print(f"Token count: {token_count}")Token count: 12345
Key Takeaways
- Use
gpt-4ofor the largest 128k token context window in 2026. - Models like
claude-3-5-sonnet-20241022andgemini-2.5-prooffer up to 100k tokens, suitable for most long-context tasks. - Avoid smaller models with limited context windows to prevent input truncation and context loss.
- Always measure token usage with a tokenizer before sending requests to avoid exceeding limits.