Claude’s “Paste the Whole Thing” Advice, Plus a Real-World Test of LLM Context Windows

This afternoon, while going through the MCP docs, I noticed that Claude’s official documentation says “MCP development can be assisted by LLMs.” And in that documentation it actually tells you to copy the entire doc set into the Claude conversation. A single one of those doc libraries already runs over 50,000 characters, and much of it is just an index. That obviously isn’t something you’d paste into a conversation — yet the docs say to paste it straight in.

I was endlessly curious — is Claude really that strong?

Since I mostly use Doubao, I tried it in the Doubao app, and it was obvious that Doubao’s context was blown.

Doubao knew exactly what was going on, too. It replied right away:

It seems the content you provided got cut off in the middle of the “Build an MCP client” section, specifically in the Python tab where an image is referenced but not fully included. However, based on the information available, I can provide a summary and key points about the Model Context Protocol (MCP) and the client-building tutorial.

And Builder in Trae also says it only supports prompts up to 6,000 characters.

Of course, in normal everyday use, when you’ve got that much text, you’d put it in a document and then ask your question. That’s the basics.

But since it says, hey — you can just paste it right into the conversation?! Well then, a war chest this rich 😜 — I’ve never fought one in my life, and I wanted to find out too.

Since I can’t use Claude myself, I was really puzzled — is Claude that strong? You can just drop a 100,000-character document index into a prompt and send it?

Well, out of curiosity, I had an AI tally up how big the context windows are for every mainstream commercial and open-source LLM on the market right now. Here are the results:

AI LLM Context Window Info Roundup — Web Version:

November 2025 AI LLM Context Window Info Roundup

AI LLM Context Window Info Roundup — Text Version:

With the rapid development of AI technology, large language models (LLMs) have become a core driver of digital transformation across industries. As of November 2025, the global AI LLM landscape shows a pattern of open source and commercialization developing in parallel, with model capabilities continually breaking new ground—especially on the key metric of the context window.

The context window determines how much text a model can process, directly affecting what it can do in long-document analysis, complex conversations, code understanding, and similar scenarios. From a few hundred tokens in the early days to tens of millions of tokens today, this technical leap has not only improved models’ “memory” but opened up entirely new possibilities for AI applications. This article systematically reviews the major AI LLMs released as of November 2025, focusing on their specific context window parameters, to give developers and enterprise users a comprehensive technical reference.

1. Open-Source LLM Context Window Analysis

1.1 Meta Llama Series

Meta’s Llama series has long been the benchmark for open-source LLMs. Llama 3.1, released in 2024, supports a 128K token context window, enough for roughly 50,000 characters of input. Its market performance, however, was less than stellar: a month after release it had only 3.6 million downloads on Hugging Face, far below Llama 3’s 5.8 million in its first month.

The real breakthrough came with the Llama 4 series (released April 2025), which includes three versions, each with a markedly different context window:

Llama 4 Scout’s 10-million-token context window is achieved through interleaved Rotary Position Embedding (iRoPE) technology, a breakthrough that lets it handle extremely long documents or conversation histories. Notably, Llama 4 is Meta’s first model to adopt a mixture-of-experts (MoE) architecture, and it also has native multimodal capabilities.

1.2 Alibaba Qwen Series

Alibaba’s Qwen series represents China’s open-source LLMs, and it shipped several versions in 2025, showing off powerful long-text handling:

Qwen 3 base series:

• The standard version supports a 32K token context window

• Mid-to-large models (8B and up) extend to 128K tokens

• Smaller models (0.6B, 1.7B) support 32K context by default

Qwen 3 enhanced versions:

• Qwen3-Coder: 256K tokens of native context, extendable to 1M tokens via YaRN

• Qwen3-235B: 262,144 tokens (about 256K) context length

• Qwen3-VL (vision-language model): 256K tokens natively, extendable to a million tokens

Qwen’s technical highlights include:

• A sliding window strategy with a window length of 4096, where each token can see the preceding 4096 tokens of context

• Ultra-long context extension via YaRN (Yet Another RoPE Extension)

• On Hugging Face’s open-source model leaderboard, all top-ten open-source LLMs are derivative models fine-tuned from Qwen open-source models

1.3 DeepSeek Series

The DeepSeek series showed strong performance and flexible context configurations in 2025:

Details:

• Base version: DeepSeek V1 has a context length of 65,792 tokens, max input of 57,344 tokens, and max output of 8,192 tokens

• Standard version: DeepSeek V3 and R1 support 128K tokens of context by default

• Extended version: Through services like PPIO, the context window can be extended to 160K tokens, with max output also extended to 160K tokens

• Special note: DeepSeek models have both thinking and non-thinking modes, and both support 128K context

1.4 Moonshot Kimi Series

The Kimi series saw a significant context window upgrade in 2025:

Kimi K2 base version (released July 2025):

• Supports a 128K token context length

• Can handle roughly 250,000 Chinese characters or 100,000 English words

• Uses an MoE architecture with 1 trillion parameters

Kimi K2 enhanced version (updated September 2025):

• kimi-k2-0905-preview: context length extended to 256K tokens

• Smoothly handles about 200,000 Chinese characters—equivalent to an entire novella or professional report

• Builds on the 0711 version with stronger agentic coding and context understanding

Kimi K2’s technical highlights:

• MLA attention combined with the SwiGLU activation function, achieving an 89.8% IFEval strict-mode score within a 128K context window

• Supports ToolCalls and JSON Mode, with breakthrough gains in code generation and agent capabilities

1.5 Mistral Series

Mistral AI rolled out several high-performance models in 2025:

Mistral Small 3.1/3.2 (released March 2025):

• Supports a 128K token context window

• 24 billion parameters; runs on a single RTX 4090 or a Mac with 32GB of memory

• Inference speed of 150 tokens per second

• Outperforms competitors such as GPT-4o Mini and Gemma 3

Mistral Magistral series (released June 2025):

• Magistral-Small and Magistral-Medium

• Billed as “Europe’s first reasoning LLM”

• Trained with pure reinforcement learning

Mistral Code (released June 2025):

• A coding assistant built for enterprise developers

• Supports JetBrains and VS Code

• In private beta

1.6 Other Important Open-Source Models

MiniMax-M2 (released October 2025):

• Ranked top five globally and first among open-source models overall on the authoritative Artificial Analysis (AA) leaderboard

• Truly reached the stage of “competing with the world’s top models”

• Extremely low API cost

Baidu ERNIE 4.5 (released March 2025):

• Will be officially open-sourced on June 30, 2025

• Context window extended to 128K tokens, about 1 million characters

• Uses an 18-layer transformer architecture with 16 attention heads

• Uses as little as 2.1GB of VRAM on a single card (after INT4 quantization)

IBM Granite series:

• Fully open source under the Apache v.2 license

• Granite 34B: 34 billion parameters, free and open source

Prime Intellect model (released November 22, 2024):

• Trained a 10B model in a decentralized way

• On November 30, open-sourced everything: the base model, checkpoints, post-trained model, data, and more

• The goal is to scale the model to frontier scale and ultimately achieve open-source AGI

2. Commercial LLM Context Window Analysis

2.1 OpenAI GPT Series

OpenAI shipped several major versions in 2025, each with different context windows:

GPT-4.5 “Orion” (released February 27, 2025):

• Internally codenamed “Orion”; OpenAI’s largest model to date

• Context window of ≥128K tokens, same as GPT-4o

• Some sources say it supports 256K tokens (about 500,000 characters)

• Has a higher “emotional intelligence”, excelling at writing polish, coding, and practical problem-solving

• Follows a technical path of scaling up unsupervised learning

GPT-4.1 series (released April 15, 2025):

• Includes three versions: GPT-4.1, GPT-4.1 mini, and GPT-4.1 nano

• Supports a million-token context window

• The smallest, fastest, and cheapest models in OpenAI’s history

2.2 Anthropic Claude Series

Anthropic’s Claude series made major breakthroughs in 2025:

Claude Opus series:

• Claude Opus 4: supports a 200,000 token (200K) context window

• The total available context window represents the maximum capacity for storing conversation history and generating new output

• The input phase includes all previous conversation history plus the current user message

• The output phase generates a text response that becomes part of future input

Claude Sonnet series:

• Claude Sonnet 3.7: the first hybrid reasoning model, launched February 2025

• Claude Sonnet 4: supports a context window of up to 1 million tokens

• Claude Sonnet 4.5: also supports a 1-million-token context window, with context awareness

Claude’s technical highlights:

• Supports Extended Thinking; thinking blocks are not passed along as input tokens in subsequent turns

• Effective context window calculation: context_window = (input_tokens - previous_thinking_tokens) + current_turn_tokens

• The 1M-token context window is currently a beta feature; requests over 200K tokens are automatically billed at premium rates (2x pricing for input, 1.5x for output)

2.3 Google Gemini Series

Google’s Gemini series shipped several versions in 2025, showing off powerful long-text handling:

Gemini 2.5 Pro:

• Supports a 1 million token context window, handling about 750,000 characters or 1,500 pages of text

• Plans to roll out a 2 million token context window

• On the Vertex AI platform, supports up to 1,048,576 input tokens and 65,535 output tokens

• Uses bidirectional token budget allocation—for instance, in a 256K window, 180K tokens go to input and 76K to output

Gemini 2.5 Flash:

• The first hybrid reasoning model, supporting a 1M token context window with thinking budgets

• Excellent multimodal capabilities, handling images, audio, and video

Gemini 2.5 Flash-Lite:

• Supports a 1 million token context length

• Extremely low cost: just $0.01 per million tokens

• Response times down to milliseconds

2.4 Chinese Commercial Models

Baidu ERNIE Bot series:

• ERNIE Bot 4.0: Deeply tied to the government and enterprise market; by 2025 covers 98% of ministry compliance standards

• ERNIE Bot 4.5 Turbo:

• Context window extended to 128K tokens, equivalent to processing 200,000 characters of text in one go

• A native multimodal LLM, enabling cross-modal reasoning across text, images, audio, and video

• Math reasoning accuracy of 89.3%

• 500+ plugins open, with daily calls exceeding 1.5 billion

• After going fully free in April 2025, users surpassed 500 million

Alibaba Tongyi Qianwen:

• Tongyi Qianwen Turbo: Balances performance and cost, suited to high-frequency calls

• Pricing: ¥0.002 per 1K input tokens, ¥0.006 per 1K output tokens

ByteDance Doubao:

• Doubao LLM V1.6: offers up to a 256K token context

• Launched Flash inference, with first-token latency of about 10ms

• Pricing: ¥0.0008 per 1K tokens for the general model (32K window), ¥0.005 per 1K tokens (128K window)

• The lowest price in the industry: ¥1 processes 1.25 million tokens (about 2 million Chinese characters)

2.5 Other Commercial Models

xAI Grok series:

• Grok 3: released February 17, 2025, with major improvements

• Grok 4:

• Supports a 256,000 token (256K) context window

• The Grok 4 Heavy version also supports 256K tokens

• Some sources say Grok 4 Fast supports an ultra-long 2 million token context (equivalent to 1,500 pages of documentation)

• Inference speed of 344 tokens per second

• Its signature humorous response style draws on The Hitchhiker’s Guide to the Galaxy

Cohere Command series:

• Command R:

• Supports a 128,000 token context length

• High accuracy on retrieval-augmented generation (RAG) and tool-use tasks

• Low latency and high throughput

• Supports 10 key languages

• Command A:

• Context length reaches 256,000 tokens (256K), twice that of its predecessor

• Equivalent to 600 pages of text

• Generates 156 tokens per second—1.75x GPT-4o and 2.4x DeepSeek-V3

• Runs on just two GPUs

3. Context Window Technical Analysis and Trends

3.1 Mainstream Context Window Standards

From the analysis above, 128K tokens has become the mainstream context window standard for LLMs in 2025. This standard has been widely adopted across both open-source and commercial models:

Open-source camp:

• The Qwen3 series treats 128k as the baseline context

• DeepSeek V3/R1’s official maximum context length is 128k

• Kimi K2’s context length is also 128k

• Llama 3.1 supports 128K context

• Mistral Small 3.1 supports 128K context

• Cohere Command R supports 128K context

Commercial model camp:

• GPT-4-Turbo and GPT-4o both support a 128k context window

• Gemini 1.5 Pro uses 128k as its standard window (higher available in the enterprise edition)

3.2 Ultra-Long Context Technical Breakthroughs

Although 128K is the mainstream standard, many models are pushing toward longer context windows:

Directions of technical breakthrough:

1. YaRN: For example, Qwen3-235B-A22B fuses YaRN to extend its context window from a native 32,768 tokens to 131,072 tokens

2. MoE architecture: Using mixture-of-experts to achieve more efficient long-text processing

3. Position encoding optimization: For example, Llama 4 Scout uses interleaved Rotary Position Embedding (iRoPE) to achieve a 10-million-token context

Ultra-long context models:

• Llama 4 Scout: 10 million tokens (the longest in the industry)

• Gemini 2.5 series: 1 million tokens (with plans to extend to 2 million)

• Claude Sonnet 4/4.5: 1 million tokens

• Grok 4 Fast: 2 million tokens (according to some sources)

3.3 Token Conversion Ratios and Cost Analysis

Understanding how tokens map to actual character counts is critical for cost control:

Token conversion ratios:

• 1 English character ≈ 0.3 tokens

• 1 Chinese character ≈ 0.6 tokens

• Chinese: 1 Chinese character ≈ 1 token

• English: 1 word ≈ 0.75 tokens

Cost difference analysis:

• Output tokens typically cost 2-5x more than input tokens

• The reason: output must be generated token by token, which is more computation-heavy, consuming roughly 3-5x the GPU compute of input

• Example: GPT-4 Turbo input (0.01/1K tokens, output )0.03/1K tokens; Claude 3 Opus input (0.015/1K, output )0.075/1K

3.4 How Context Window Affects Use Cases

Different context window lengths suit different use cases:

Short context (< 32K):

• Routine conversations

• Lightweight tasks

• Simple Q&A

Standard context (32K-128K):

• Long-document summarization

• Code generation

• Multi-turn conversations

• Complex reasoning

• Academic paper analysis

Ultra-long context (> 128K):

• Processing whole books

• Large codebase analysis

• Coordinated multi-document processing

• Ultra-long conversation history

• Genome sequence analysis

3.5 Technology Trends

1. From chasing length to chasing efficiency: The competitive focus will shift from raw “window length” to “information utilization efficiency”

2. The rise of specialized small models: In October 2025, Andrej Karpathy released the nanochat tool, which lets you train a ChatGPT-like model on a single GPU for under $100

3. Model serving optimization: High-performance inference engines like vLLM, SGLang, and TensorRT-LLM are advancing fast

4. Multimodal fusion: Unified handling of text, images, video, and audio is becoming the norm

5. Inference cost optimization: Bringing down the unit cost of inference is becoming a core driver

Conclusion

As of November 2025, global AI LLMs have made breakthrough progress in context window technology. From a few thousand tokens in the early days to tens of millions of tokens today, this technical leap has not only improved models’ “memory” but opened up entirely new possibilities for AI applications.

On the open-source side, Chinese players are rising. Models like Qwen, DeepSeek, and Kimi have reached internationally advanced levels in technical innovation and performance, showing particular strength in ultra-long context handling. Commercial models, meanwhile, pursue peak performance while meeting different user needs through tiered pricing.

Looking ahead, as technology continues to advance, we can expect:

• Longer context windows: moving from millions toward tens of millions and beyond

• Smarter context management: models will gain stronger context awareness and optimization

• More efficient inference: cutting inference costs through architectural innovation and engineering optimization

• Richer use cases: long-text processing, multimodal fusion, and agent interaction are set for explosive growth

For developers and enterprise users, choosing the right model requires weighing use case, budget constraints, and technical needs. In this era of rapidly evolving AI technology, staying attuned to technical trends and picking the most suitable tools and solutions will be the key to coming out ahead in fierce competition.