Top 6 LLMs by Context Window in 2026 (Advertised vs Usable)
Context window numbers are the new horsepower figures of AI marketing. A model can advertise 10 million tokens and still lose track of a fact you placed 200,000 tokens in. This ranking pairs each model's advertised maximum with its RULER effective context from independent benchmarking, because a credible list has to show both numbers.
The Full Rankings
This table ranks the six models by advertised maximum context, then pairs each with its RULER effective context. Click any model name to jump to its breakdown, or sort columns by clicking the headers. The gap between the two context columns is the whole point of this article.
| # ↕ | Model ↕ | Org ↕ | Advertised Max | RULER Effective | License / Source |
|---|---|---|---|---|---|
| 1 | Llama 4 Scout | Meta | 10M | ~5-6.5M | Llama Community License |
| 2 | Grok 4 Fast / Grok 4.20 Beta | xAI | 2M | ~1.2-1.4M | Iternal, Mar 2026 |
| 3 | Gemini 3.1 Pro | 1M (2M beta) | ~600-700K | Iternal, Mar 2026 | |
| 4 | GPT-5.4 (Codex) | OpenAI | 272K std / 1M extended | ~170K / ~600-650K | Iternal, Mar 2026 |
| 5 | Claude Opus 4.6 / Sonnet 4.6 | Anthropic | 200K std / 1M GA | 130-200K / ~600-700K | Iternal, Mar 2026 |
| 6 | Qwen 3.5 (397B) | Alibaba | 262K std / 1M extended | ~160-170K / ~600K | Apache 2.0 |
Advertised limits from vendor model cards and pricing pages. RULER effective context via the NVIDIA RULER benchmark, reported through Iternal in March 2026. DeepSeek V4 is discussed separately below because its 1M figure is vendor-stated, not independently RULER-tested in these sources.
Ranked by advertised maximum context, paired with RULER effective context (NVIDIA RULER via Iternal, March 2026: models reliably use only 50 to 65 percent of their advertised window). A credible ranking shows BOTH numbers.
Lost-in-the-middle degrades mid-prompt recall even within the window. A model that accepts a long prompt does not necessarily attend evenly across all of it, so the advertised ceiling and the usable floor can diverge sharply.
1. Llama 4 Scout (Meta)
Meta's Llama 4 Scout holds the advertised crown at 10 million tokens, the largest context window any major model claims. The figure comes from its iRoPE interleaved attention design, which Meta documented in April 2025 to let the model scale far beyond conventional positional encoding limits. On paper, that is enough to load an entire code repository or a small library of documents in one prompt.
Read more: Llama Native Multimodal Image Capabilities
2. Grok 4 Fast / Grok 4.20 Beta (xAI)
xAI's Grok 4 Fast advertises a 2 million token window, second only to Llama 4 Scout among the models tracked here. That puts it well ahead of the 1M-class frontier models from Google, OpenAI, and Anthropic on raw advertised capacity, and it pairs the long context with xAI's real-time data access on the X platform.
Read more: Top 7 LLM Benchmarks That Matter
3. Gemini 3.1 Pro (Google)
Google's Gemini 3.1 Pro advertises a 1 million token context window, with a 2 million token tier in beta. Google was an early mover on long context, and Gemini remains one of the most reliable large-window models in practice, with strong multimodal handling across text, image, audio, and video in a single prompt.
Read more: Top 10 Open-Weight LLMs
4. GPT-5.4 (Codex) (OpenAI)
OpenAI's GPT-5.4, including its Codex coding configuration, advertises 272K tokens as standard and 1 million tokens in an extended mode. The two-tier structure is honest about the trade-off: the standard window is what most API calls see, while the extended window is reserved for workloads that explicitly opt in.
Read more: Top 7 LLM Benchmarks That Matter
5. Claude Opus 4.6 / Sonnet 4.6 (Anthropic)
Anthropic's Claude Opus 4.6 and Sonnet 4.6 advertise a 200K token standard window, with a 1 million token tier now generally available. Claude has a reputation for strong recall quality within its window, and the effective context numbers reflect that, with the standard tier holding up across most of its advertised range.
Read more: Top 7 LLM Benchmarks That Matter
6. Qwen 3.5 (397B) (Alibaba)
Alibaba's Qwen 3.5, in its 397B configuration, advertises 262K tokens as standard and 1 million tokens in an extended mode. What sets it apart on this list is the license: Qwen 3.5 ships under Apache 2.0, the most permissive license of any model ranked here, which makes it genuinely open for commercial use and self-hosting without the restrictions Llama carries.
Read more: Top 10 Open-Weight LLMs
The Advertised vs Usable Gap
The single most important thing to understand about this ranking is that the advertised number and the usable number are not the same. Two well-documented effects open the gap.
The NVIDIA RULER benchmark, reported through Iternal in March 2026, found that models reliably use only 50 to 65 percent of their advertised context window. A 1 million token model behaves more like a 500 to 650K token model when you measure actual recall. Every effective figure in the table above reflects this rule, which is why an advertised-only ranking would mislead readers.
Even within a model's effective window, recall is not uniform. Information placed at the start and end of a long prompt is retrieved far more reliably than information buried in the middle. A fact dropped at the midpoint of a large document can be missed entirely, which is why position, not just total length, determines whether the model actually uses what you gave it.
The DeepSeek V4 Caveat
DeepSeek V4 deserves a mention, and a clear label. DeepSeek's API documentation advertises a 1 million token context for V4, which would place it in the same frontier tier as Gemini, GPT-5.4, and Claude's GA window. We have deliberately left it out of the ranked table for one reason.
Vendor-stated, not independently verified. The cross-model benchmark sources used here only test DeepSeek R1 and V3 at 128K tokens, where effective context lands around 80 to 90K. V4's 1 million figure comes from DeepSeek's own API docs and has not yet been independently RULER-tested in these sources. Until it is, treat the 1M number as a vendor claim rather than a verified usable limit, the same standard we hold every other model to.
This is not a knock on DeepSeek. It is the methodology working as intended: a number only enters the ranked comparison once an independent benchmark has measured what the model actually uses, not just what the vendor advertises.
How We Ranked These Models
We ranked the six models by advertised maximum context, because that is the number vendors lead with and the number readers search for. But we refused to stop there. Every entry is paired with its effective context, measured independently, so the ranking reflects what you can actually use.
- Advertised maximum: The headline token figure from each vendor's model card or pricing page. This sets the rank order. Where a model has standard and extended tiers, both are shown.
- RULER effective context: The usable window measured by the NVIDIA RULER benchmark, reported through Iternal in March 2026. Across the field, models reliably use only 50 to 65 percent of their advertised window.
- License and source: Llama 4 Scout's restrictive Community License and Qwen 3.5's permissive Apache 2.0 are flagged, because licensing changes who can actually deploy a model regardless of its context size.
- Independent verification first: A model only enters the ranked table once an independent benchmark has measured its effective context. DeepSeek V4's vendor-stated 1M is discussed separately for exactly this reason.
This list reflects our independent evaluation. Tech Jacks Solutions has no affiliate or advertising relationship with any model or vendor listed. Advertised limits were taken from vendor documentation and effective context from the NVIDIA RULER benchmark via independent testing, as of March 2026. Numbers change as vendors update models and as new benchmarks are published, so always confirm against current vendor documentation before making decisions.
Frequently Asked Questions
Which LLM has the largest context window in 2026?
By advertised maximum, Meta's Llama 4 Scout leads at 10 million tokens, enabled by its iRoPE interleaved attention design documented by Meta in April 2025. However, independent RULER benchmarking suggests the effective usable context is closer to 5 to 6.5 million tokens, since models reliably use only 50 to 65 percent of their advertised window. Even at the lower bound, it remains the largest usable context of any model ranked here.
What is the difference between advertised and effective context?
Advertised context is the maximum token count a vendor states a model accepts. Effective context is how many tokens the model can reliably reason over before recall degrades. The NVIDIA RULER benchmark, reported through Iternal in March 2026, found models reliably use only 50 to 65 percent of their advertised window. A credible ranking shows both numbers, which is why every row in our table carries an advertised figure and a RULER effective figure.
Does DeepSeek V4 really support 1 million tokens of context?
DeepSeek's API documentation advertises a 1 million token context for V4, but that figure is vendor-stated. The independent cross-model benchmark sources used here only test DeepSeek R1 and V3 at 128K, where effective context lands around 80 to 90K. Until V4 is independently RULER-tested, we treat the 1 million figure as a vendor claim rather than a verified usable limit, which is why it is discussed separately rather than placed in the ranked table.
What is lost-in-the-middle and why does it matter for long context?
Lost-in-the-middle describes how language models recall information placed at the start and end of a long prompt far better than information buried in the middle. It means that even within a model's effective window, mid-prompt facts can be missed. A large advertised context does not guarantee the model attends evenly across all of it, so where you place critical information in a long prompt matters as much as how much you include.
Is Llama 4 Scout free to use given its huge context window?
Llama 4 Scout ships under the Llama Community License, not a permissive open-source license like Apache 2.0. It is free for organizations below 700 million monthly active users, but it carries acceptable-use and naming restrictions, so it is more accurately source-available than open. Among the models ranked here, Qwen 3.5 under Apache 2.0 is the more permissively licensed long-context option if open deployment is a hard requirement.
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