Which LLM Has the Largest Context Window? The Definitive 2026 Comparison

The Largest Context Windows in 2026

Every frontier LLM context window, ranked by size. This table covers the models you'll actually encounter when evaluating context capacity. Note the gap between "advertised" and "practical" for several models.

A few things stand out from this table:

• The top three by raw window size (LTM-2-Mini, Llama 4 Scout, MiniMax-Text-01) are either not publicly available, require exotic hardware, or use specialized architectures that behave differently from standard transformers.
• The models most developers actually use daily (Gemini 2.5 Pro, GPT-4.1, Claude Opus 4.6) cluster in the 200K-1M range.
• Pricing varies 50x for the same window size category. DeepSeek R1 at $0.55/M input is 9x cheaper than Claude Opus 4.6 at $5.00/M, though their context windows differ (164K vs 200K).
• Open-source options (Llama 4 Scout, Llama 4 Maverick) offer large windows at no per-token API cost, but require significant self-hosting infrastructure.

For details on how token limits translate to real-world document sizes, see our token limit guide. Roughly: 1M tokens is about 750K words, or around 3,000 pages of dense text. The entire Harry Potter series is approximately 1.1M words, so GPT-4.1's 1M token window could hold most of it in a single prompt.

What These Windows Mean in Practice

Context Window Growth: From 512 to 100 Million Tokens

Context windows have expanded 200,000x in seven years. The trajectory is not linear. Each generation roughly 4-10x the previous, with two major jumps: Claude 2's leap to 100K in 2023, and the million-token era starting with Gemini 1.5 in 2024.

The first six years (2018-2023) saw ~200x growth: 512 to 100K. The next two years (2024-2025) saw ~1,000x more: 100K to 100M. Hardware improvements (longer attention spans, efficient attention mechanisms like lightning attention and ring attention) enabled this acceleration. But as the next section shows, raw capacity has outpaced the models' ability to use that capacity effectively.

The Race by Provider

Different providers have pursued different strategies in the context window race. Google has led on raw window size, shipping 2M tokens first with Gemini 1.5 Pro and maintaining that lead with Gemini 3 Pro. OpenAI initially lagged (GPT-4 launched at 8K, later expanded to 128K) but made a large jump to 1M with GPT-4.1. Anthropic has been conservative, keeping Claude at 200K standard while offering 1M in beta, prioritizing reasoning quality over window size. Meta has pushed the frontier for open-source models with Llama 4 Scout's 10M claim.

What Enabled the Growth

Standard transformer attention is O(n^2) in sequence length. A 2M token context requires 4 trillion attention computations per layer. Several architectural innovations made this feasible:

• Ring Attention distributes the sequence across multiple devices, each computing attention on its local segment and passing KV states to neighbors. This lets you scale linearly with the number of GPUs.
• Lightning Attention (used by MiniMax-Text-01) replaces softmax attention with a linear approximation that scales O(n) instead of O(n^2), making 4M+ tokens computationally viable on a single node.
• Mixture of Experts (MoE) keeps total parameters high but active parameters low. Llama 4 Scout has 109B total parameters but only activates 17B per token, reducing the per-token compute cost at long contexts.
• KV Cache Quantization compresses key-value caches from FP16 to INT4 or INT8, reducing the memory footprint of long contexts by 2-4x without significant quality loss.

These techniques solve the compute and memory problems. They do not solve the attention quality problem, which is why models can accept millions of tokens but cannot reason over them with full accuracy.

Advertised vs. Effective Context: What the Benchmarks Show

The number on the model card is not the number you get. Multiple independent benchmarks converge on the same finding: effective context is substantially smaller than advertised context. This section covers the three most important evaluations.

RULER Benchmark (NVIDIA)

NVIDIA's RULER benchmark tests models on synthetic tasks that require attending to specific information at various positions within long contexts. The results show a consistent pattern: accuracy declines as context length increases, even for models explicitly trained for long context.

The degradation is not a cliff but a slope. Performance starts declining well before the stated limit. A model scoring 96.5 at 4K and 66.6 at 128K has lost nearly a third of its reasoning capability. The 128K context is "supported" but not equally utilized. The effective context, the portion where the model maintains close to full accuracy, is substantially smaller than what the spec sheet advertises.

RULER tests four task categories: needle-in-a-haystack retrieval, multi-key retrieval, variable tracking, and aggregation. Models that maintain accuracy on single-needle retrieval (the task most providers benchmark on) still degrade on multi-key and aggregation tasks. Single-needle is the easiest long-context task. Real applications usually require attending to multiple pieces of information simultaneously.

"Context Length Alone Hurts" (arXiv 2510.05381)

This paper found something more alarming: even adding whitespace tokens degrades performance. The content doesn't need to be distracting. The mere presence of additional tokens in the context window hurts model output quality.

Llama-3.1-8B's HumanEval score dropped from 57.3% to 9.7% at 30K tokens. Mistral went from 34.8% to literal zero. These models technically support these context lengths. They just can't code at them.

The implication for anyone choosing a model based on context window: a model with a 128K window that you fill to 30K may perform significantly worse than the same model with a 16K window filled to 8K. The tokens you don't send matter as much as the tokens you do.

Chroma Context Rot Study

The Chroma context rot study tested all 18 frontier models and found degradation across every single one. No model was immune. But the most counterintuitive finding: models performed better on shuffled text than coherent text. Randomly rearranging the input paragraphs produced higher accuracy than presenting them in logical order.

Summary: The Effective Context Gap

Three independent evaluations converge on the same conclusion. RULER shows 50-65% effective utilization. The "Context Length Alone Hurts" paper shows complete performance collapse on code tasks at 30K tokens. Chroma shows universal degradation across all 18 frontier models. The pattern is consistent regardless of provider, architecture, or training approach.

Why Bigger Context Windows Are Not Better

The data above points to a consistent conclusion: context window size is a misleading proxy for model capability. Four specific problems undermine the value of larger windows.

1. Lost-in-the-Middle

LLMs attend strongly to the beginning and end of their input. Content in the middle of long contexts gets progressively less attention, with 30%+ accuracy drops for mid-positioned information. If you put a critical document at position 500K in a 1M token window, the model is significantly less likely to reason about it correctly than if the same document were at position 1K.

This is not a training data problem or a model quality problem. It is an architectural limitation of how attention works. Positional encodings become less discriminative at large distances, and the softmax attention distribution flattens across many tokens, diluting the weight any single token receives. Techniques like ALiBi and RoPE mitigate but do not eliminate the effect. Every model in the comparison table above is subject to this limitation to some degree.

2. Signal Dilution

Every irrelevant token competes for attention with every relevant token. As context rot research demonstrates, adding noise tokens degrades performance on the signal tokens. A 200K window with 50K relevant tokens and 150K noise tokens performs worse than a 60K window with the same 50K relevant tokens and only 10K of lightweight framing.

This has a practical consequence for the "just use a bigger window" approach: if you stuff a 2M token window with an entire codebase hoping the model will find the relevant file, the model is worse at finding that file than if you had pre-selected the 10 most likely files and passed only those. More input does not mean more information when the attention mechanism cannot efficiently sort signal from noise.

3. The Llama 4 Scout Reality Check

Llama 4 Scout is the poster child for the gap between claims and performance. It advertises a 10M token context window with a 17B active / 109B total Mixture of Experts architecture. In practice:

• Requires 8xH100 GPUs for inference at full context
• Practical context limit is approximately 1.4M tokens, 7x smaller than advertised
• Scored 15.6% on Fiction.LiveBench, a long-context reasoning benchmark that tests comprehension and retrieval over novel-length texts
• The 10M number describes what the architecture can accept as input, not what it can reason about with maintained quality

4. Latency and Cost Scale with Context

Attention is quadratic in context length (or at best sub-quadratic with optimizations). Doubling your context roughly doubles your time-to-first-token and your per-request cost. A 2M token request is not just 2x more expensive than a 1M token request in dollars. It is also slower, which compounds in agentic loops where the model is called hundreds of times per session.

Consider a coding agent that averages 15 model calls per task. If each call processes 500K tokens instead of 200K (because the agent isn't compressing context), the session takes 2.5x longer in wall-clock time and costs 2.5x more. Over hundreds of tasks per week, the difference between "dump everything in the window" and "send only what matters" is measured in hours and thousands of dollars.

Cost to Fill: What Large Context Actually Costs

Large context windows are not free. Every token you send to the model costs money, and for applications that process documents repeatedly or run long agentic sessions, the cost of filling (and re-filling) context adds up. This is especially relevant given that much of what fills large contexts is low-signal noise the model cannot effectively use.

The price gap is striking: filling 1M tokens on Claude Opus 4.6 costs 50x more than Gemini 2.0 Flash. But cost per fill is just the beginning. The real expense comes from how many times you fill the window.

Cost Compounds in Agentic Loops

Consider an agentic coding session that reads 50 files, runs 30 tool calls, and iterates 20 times. Each iteration sends the full (or near-full) context to the model. At GPT-4.1 prices, a session that averages 500K tokens per call across 20 calls costs $20 in input tokens alone. If 60% of those tokens are noise that degrades performance, you're paying $12 for tokens that actively make the model worse.

At scale (100+ sessions per day for a team of developers), the difference between sending full context and compressed context is thousands of dollars per month. And the compressed version performs better because the model spends attention budget on signal instead of noise.

The Output Side

Input tokens are only half of the cost equation. Output tokens cost 2-5x more than input tokens for most frontier models. Claude Opus 4.6 charges $25.00 per 1M output tokens versus $5.00 input. GPT-4.1 charges $8.00 output versus $2.00 input. When large context prompts lead to longer model responses (which they often do, as the model attempts to address all the content in the window), the output cost multiplier amplifies the expense.

For cost optimization strategies beyond context management, see our LLM cost optimization guide.

The Alternative: Context Compaction Over Larger Windows

The research points in one direction: focused context outperforms large context. Instead of scaling the window and hoping the model uses it well, compress the context to keep only what matters. Three approaches demonstrate this.

CompLLM: Compression Beats Raw Context

CompLLM achieves 2x compression with 4x faster time-to-first-token and, on very long sequences, actually surpasses uncompressed performance. This result is counterintuitive but consistent with the degradation data above: if a model struggles with 200K tokens of mixed-quality content, giving it 100K tokens of high-quality content produces better output. The compressed input performs better because removing noise tokens lets the model focus on signal. More context is not always more information.

Retrieve-Then-Solve: From 35.5% to 66.7%

Instead of dumping an entire codebase into the context window, retrieve-then-solve approaches first identify which content is relevant to the current task, then include only that content. This approach improved Mistral from 35.5% to 66.7% at 26K tokens, nearly doubling performance by being selective about what enters the context.

The insight applies broadly: a two-stage pipeline (retrieve relevant content, then solve the task) consistently outperforms a single-stage pipeline (put everything in the window, hope the model figures it out). The retrieval step does not need to be perfect. Even a rough filter that eliminates 80% of irrelevant content dramatically improves the model's accuracy on the remaining 20%.

Retrieval and compaction are complementary, not competing strategies. First retrieve the relevant documents or code files, then compact the retrieved content to remove noise within those files. The combination gives you both relevance filtering (only the right files) and noise removal (only the important parts of those files).

Morph Compact: Verbatim Compaction

Morph Compact takes a deletion-based approach to context management. Instead of rewriting or summarizing content, it identifies which tokens carry signal and removes the rest. Every surviving sentence is verbatim from the original input. No paraphrasing, no summarization, no hallucination risk from the compression step itself.

This distinction matters for coding agents and technical applications. When a summarization engine compresses a context containing src/lib/auth.ts:47, it might paraphrase it as "the auth module." The agent then loses the exact file path and line number needed for its next edit. Verbatim compaction keeps the original reference intact or removes it entirely. Nothing is "close enough."

The practical impact: instead of needing a 2M token window to hold your entire codebase context, compact the context down to the 400-600K tokens that actually matter. The model performs better on the compacted input because it spends attention on signal instead of noise. You pay less in both cost and latency. And because the compaction is verbatim, every file path, error message, and code snippet survives exactly as written.

When to Compact vs. When to Use a Large Window

Large context windows still have valid use cases. Single-pass document analysis (ingesting a full legal contract or annual report for one round of questions) benefits from having the entire document available. The problems emerge when the same context is sent repeatedly (agentic loops), when most of the context is irrelevant to the current step, or when the context exceeds the model's effective utilization range.

from openai import OpenAI

client = OpenAI(
    api_key="your-morph-api-key",
    base_url="https://api.morphllm.com/v1"
)

# Compact a long context before sending to your main model
response = client.chat.completions.create(
    model="morph-compact",
    messages=[{
        "role": "user",
        "content": long_agent_context  # 500K tokens of accumulated context
    }]
)

compacted = response.choices[0].message.content
# Result: ~175K tokens, every surviving line verbatim from original
# Send compacted context to your main model (GPT-4.1, Claude, etc.)
# Better accuracy (less noise) + lower cost + faster TTFT

For a deeper comparison of compression methods including benchmarks and code examples, see our full context compression guide.

How to Choose: Window Size vs. Context Strategy

The right context strategy depends on your use case, not the biggest number available. Most applications fall into one of four categories, each with different requirements.

A practical decision framework: if your total context fits comfortably within 50% of the model's effective window (not advertised window) and you only send it once, a large window is fine. If your context approaches the effective limit, gets re-sent multiple times, or contains substantial irrelevant content, compression will improve both quality and cost.

Matching Models to Context Needs

Not every task needs the biggest window. A customer support chatbot with 10-20 message histories needs 8-16K tokens, well within every modern model's range. A document analysis pipeline processing 50-page contracts needs ~75K tokens. A coding agent working on a large monorepo with accumulated context from dozens of tool calls may need 200K-500K.

The models in the comparison table vary by 500x in window size (200K to 100M) and 50x in cost ($0.10 to $5.00 per 1M tokens). Choosing the right model means matching window size and cost to your actual context requirements, not reaching for the biggest number available.

Frequently Asked Questions

Which LLM has the largest context window in 2026?

Magic.dev's LTM-2-Mini holds the record at 100 million tokens, though it is not publicly available. Among models you can actually use, Meta's Llama 4 Scout supports 10 million tokens (practical limit ~1.4M), and Google's Gemini 3 Pro and xAI's Grok 4 each support 2 million tokens. MiniMax-Text-01 supports 4M with a lightning attention architecture. See the full comparison table for all models with pricing.

What is the effective context window vs. advertised context window?

Effective context is how much of the advertised window a model can use with maintained accuracy. NVIDIA's RULER benchmark shows effective context is typically 50-65% of advertised size. Llama 3.1-70B drops from 96.5 accuracy at 4K tokens to 66.6 at 128K. The model "supports" 128K but performs poorly above ~80K. This gap exists across every model tested, not just open-source ones. For details on token limits and their practical implications, see our dedicated guide.

How much does it cost to fill a 1M token context window?

Claude Opus 4.6 costs $5.00 per 1M input tokens. GPT-4.1 costs $2.00. Gemini 2.5 Pro costs $1.25. DeepSeek V3 costs $0.14. Gemini 2.0 Flash costs $0.10. These costs multiply in agentic applications where context is re-sent with each model call, potentially 10-50 times per session. A 20-call session at GPT-4.1 prices with 500K avg context costs $20 in input tokens alone. See the cost table for the full breakdown.

Does a bigger context window mean better LLM performance?

No. Multiple independent studies show the opposite. The "Context Length Alone Hurts" paper found Llama-3.1-8B HumanEval scores dropping from 57.3% to 9.7% at 30K tokens. The Chroma study found all 18 frontier models degrade with length. Retrieve-then-solve improved Mistral from 35.5% to 66.7% by being selective about context. Focused, relevant context consistently outperforms large context filled with everything.

What is the lost-in-the-middle problem?

LLMs attend disproportionately to content at the beginning and end of their input. Information positioned in the middle of long contexts gets progressively less attention, with 30%+ accuracy drops documented in research. This means increasing context window size doesn't proportionally increase the model's access to that information. Content placement matters as much as content presence. The problem is architectural: positional encodings lose discriminative power at large distances, and softmax attention flattens across many tokens.

What are alternatives to using larger context windows?

Three approaches outperform raw large context in benchmarks. Context compaction (like Morph Compact) deletes noise tokens for 50-70% reduction at 3,300+ tok/s with 98% verbatim accuracy. Retrieve-then-solve selects relevant context before including it, improving Mistral from 35.5% to 66.7%. CompLLM achieves 2x compression that surpasses uncompressed performance on very long sequences by removing tokens that dilute attention. See our context compression guide for implementation details and code examples.

The Bottom Line

Context window size is important. You need a window large enough to hold the content your application requires. But after a threshold (roughly 100-200K for most production use cases), bigger windows deliver diminishing returns and increasing costs. The research is clear: effective context is 50-65% of advertised, performance degrades with length across all models, and the attention mechanism struggles with mid-positioned content.

The practical strategy for 2026 is not "find the model with the biggest window." It is: choose a model with a window that fits your effective context needs, then use compaction or retrieval to keep the content you send high-signal and focused. A 200K model with clean, compacted context will outperform a 2M model drowning in noise tokens on virtually every benchmark that matters for production applications.

For teams building agentic applications, the math is straightforward. Compaction reduces cost per call, improves latency per call, and improves model accuracy per call. The only tradeoff is adding a compaction step to your pipeline. At 3,300+ tokens per second, that step adds negligible latency relative to the savings in the main model call.