AI Context Window Comparison 2026: 1M to 10M Tokens
Comprehensive comparison of AI model context windows in 2026. From GPT-5.4 and Claude Opus 4.6 at 1M tokens to Llama 4 Scout at 10M. Full reference table.
Largest Context (Llama 4 Scout)
Models at 1M+ Tokens
Effective vs. Advertised Capacity
Pricing Spread at Full Context
Key Takeaways
Context windows have become one of the most consequential differentiators in the AI model landscape. In early 2024, a 128K context window was exceptional. By April 2026, five major models support 1 million tokens, one reaches 2 million, and Meta's Llama 4 Scout pushes the boundary to 10 million. This expansion changes what is architecturally possible — and what is economically viable — for every team building AI-powered products.
This reference compares every major model's context window as of April 2026, including pricing at full context, effective versus advertised capacity, and practical guidance on which window size matches which business use case. For teams building broader AI and digital transformation pipelines, understanding context window tradeoffs prevents both over-provisioning (paying for context you do not use) and under-provisioning (hitting limits that force architectural workarounds).
Why Context Windows Matter More Than Ever
A context window defines how much information a model can process in a single request. Every token of input — your prompt, system instructions, retrieved documents, conversation history, and tool call results — must fit within this window. When it does not fit, something gets dropped, summarized, or excluded entirely.
The strategic significance of context windows has shifted. In 2024, the question was whether a model could handle a single long document. In 2026, the question is whether a model can hold an entire codebase, a full legal case file, or a month of customer interactions in a single reasoning step. This shift fundamentally changes application architectures — reducing dependency on external retrieval systems and enabling reasoning patterns that were previously impossible.
A 1M-token window can hold approximately 40,000 lines of code with documentation. At 10M tokens, an entire mid-size repository fits in a single prompt, enabling cross-file reasoning without retrieval.
A standard contract set of 200-500 pages fits comfortably in 1M tokens. Multi-party litigation discovery requiring tens of thousands of pages pushes into the 2M-10M range where only a few models operate.
Agentic workflows accumulate tool call results, intermediate reasoning, and conversation history rapidly. A complex multi-step agent can consume 100K-500K tokens in a single session, making 1M+ windows essential for sustained operation.
Synthesizing quarterly earnings calls, analyst reports, and competitive intelligence across an industry sector can require 500K-2M tokens of source material. Larger windows reduce the need for pre-filtering that risks excluding relevant context.
Complete Context Window Comparison Table
The following table captures every major model's context window as of April 2026, organized from largest to smallest. Pricing reflects published API rates; models with tiered pricing are noted. For the full breakdown of all twelve models released in March 2026, see our complete guide to the twelve March 2026 model releases.
| Model | Provider | Context Window | Max Output | Input $/MTok | Output $/MTok | Architecture |
|---|---|---|---|---|---|---|
| Llama 4 Scout | Meta | 10M | 128K | ~$0.30* | ~$0.60* | 109B MoE (17B active) |
| Grok 4.20 | xAI | 2M | 128K | $2.00 | $10.00 | Dense (reasoning/non-reasoning) |
| Llama 4 Maverick | Meta | 1M | 128K | ~$0.50* | ~$0.80* | 400B MoE (17B active) |
| Gemini 3.1 Pro | 1M | 65K | $2.00 | $12.00 | Dense (thinking levels) | |
| GPT-5.4 | OpenAI | 1M | 128K | $2.50** | $15.00 | Dense (Standard/Thinking/Pro) |
| Claude Opus 4.6 | Anthropic | 1M | 32K | $5.00 | $25.00 | Dense |
| Qwen 3.6 Plus | Alibaba | 1M | 65K | Free**** | Free**** | Hybrid MoE + linear attention |
| Claude Sonnet 4.6 | Anthropic | 1M | 32K | $3.00 | $15.00 | Dense |
| Mistral Small 4 | Mistral | 256K | 32K | $0.10 | $0.30 | 119B MoE (6.5B active) |
| Grok 4 | xAI | 256K | 128K | $2.00 | $10.00 | Dense |
| GLM-5 | Zhipu AI | 200K | 32K | $1.00 | $3.20 | 744B MoE (40B active) |
| GPT-5.4 Mini | OpenAI | 128K | 128K | $0.40 | $1.60 | Dense (distilled) |
| gpt-oss-120b | OpenAI | 128K | 32K | ~$0.30* | ~$0.60* | 117B MoE (5.1B active) |
* Open-weight model — pricing reflects typical cloud provider hosting costs, not a fixed API rate.
** GPT-5.4 charges $2.50/MTok for the first 272K tokens; $5.00/MTok beyond that threshold.
*** As of March 13, 2026, Claude models charge flat standard rates at any context length up to 1M — no long-context surcharge.
**** Qwen 3.6 Plus is free during its preview period. Post-preview pricing has not been announced.
Bookmark this table. This comparison is updated as new models launch or pricing changes. For the latest on specific models, see our dedicated guides for GPT-5.4, Grok 4.20, and Qwen 3.6 Plus vs Claude Opus 4.6 vs GPT-5.4.
Frontier Tier: 1M+ Token Models
Five models now compete at the 1 million token tier, but they reach that number through different architectures and with different tradeoffs. Understanding these differences is essential for production deployment decisions.
GPT-5.4 supports up to 1M tokens of input context via the API and Codex, with a 128K token maximum output. The standard context window is 272K tokens — anything beyond that triggers a pricing surcharge where input cost doubles from $2.50 to $5.00 per MTok. The 1M capability requires explicit configuration via model_context_window and model_auto_compact_token_limit parameters. Native computer use and five-level reasoning effort control are additional differentiators.
Claude Opus 4.6 supports 1M tokens at general availability with no beta header required. As of March 13, 2026, Anthropic eliminated the long-context surcharge entirely — a 900K-token request is billed at the same $5.00/MTok input and $25.00/MTok output rate as a 9K-token request. This also includes 6x more media per request (up to 600 images or PDF pages). Opus 4.6 remains the most expensive API option at the 1M tier but delivers premium reasoning quality on complex analysis tasks.
Gemini 3.1 Pro offers 1M tokens at a flat $2.00/MTok input rate with no tiered surcharges. This makes it the most cost-predictable option for full-context workloads. The model scored 77.1% on ARC-AGI-2, 94.3% on GPQA Diamond, and 80.6% on SWE-Bench Verified. Its thinking-level parameter (low, medium, high) allows per-request cost-quality tradeoffs without model switching.
Qwen 3.6 Plus combines linear attention mechanisms with sparse mixture-of-experts to deliver a 1M-token context window with up to 65K output tokens. The model features always-on chain-of-thought reasoning and native function calling. Released on OpenRouter on March 31, 2026, it is currently free during the preview period. The hybrid architecture reduces computational load for long-context processing compared to standard dense attention models.
Llama 4 Maverick is a 400B-parameter MoE model with 17B active parameters and 128 experts. Pre-trained at 256K context, then fine-tuned to support 1M tokens via the Instruct variant. As an open-weight model under the Llama 4 Community License, it can be self-hosted, reducing per-token costs to infrastructure only. Maverick achieved the highest MMLU score (85.5%) among open models as of its release.
The practical question for most teams is not “which model has the largest context window” but “which model delivers the best recall quality and reasoning at the context size I actually need.” For workloads under 200K tokens, the pricing tiers and surcharges are irrelevant, and model selection should be based on task quality. For workloads between 200K and 1M tokens, Gemini 3.1 Pro offers the most predictable cost profile. For the intersection of quality and cost at full 1M context, the competitive landscape is genuinely tight — teams should benchmark on their specific task distribution.
Mega-Context: Llama 4 Scout at 10M Tokens
Meta's Llama 4 Scout deserves a dedicated section because its 10M context window is not just larger than competitors — it is a categorically different capability. At 10 million tokens, Scout can theoretically process approximately 15,000 pages of text, an entire mid-to-large codebase, or several years of conversational history in a single request.
Llama 4 Scout Technical Details
Architecture
109B total params, 17B active (16 experts MoE)
Training Context
Pre-trained at 256K, generalized to 10M
Training Data
~40 trillion tokens, cutoff August 2024
Hardware
Fits on a single NVIDIA H100 GPU
License
Llama 4 Community License (commercial use)
Multimodal
Native early-fusion multimodal (text + image)
The critical nuance is how Scout reaches 10M. Meta pre-trained and post-trained the model with a 256K context length, then used length generalization techniques to extrapolate to 10M. This is not the same as training directly on 10M-token sequences. Independent analyses have confirmed that the model handles retrieval-oriented tasks (finding specific facts within the context) reliably at very long contexts, but synthesis tasks (reasoning across the entire context simultaneously) degrade notably beyond approximately 1-2M tokens.
Practical guidance: Plan for Llama 4 Scout as a 1-2M effective context model for synthesis tasks and a 5-10M model for retrieval tasks. If your use case is “find this specific clause in 10,000 pages of contracts,” Scout excels. If your use case is “synthesize themes across 10,000 pages,” consider chunking into 1M windows and aggregating results.
Scout's other differentiator is hardware efficiency. At 109B total parameters with only 17B active per token, it fits on a single H100 GPU — a significant advantage for self-hosted deployments where multi-GPU setups multiply cost and operational complexity. For teams exploring both open-weight and proprietary options, our Gemma 4 vs Llama 4 vs Mistral Small 4 comparison covers the broader open-weight competitive landscape.
Pricing at Full Context: What It Actually Costs
Context window comparisons are incomplete without pricing analysis. A model offering 1M tokens at $10/MTok is a fundamentally different product than one offering 1M tokens at $2/MTok, even if their context limits are identical. The following table shows the actual cost of processing a 1M-token input document across each model.
| Model | Cost for 1M Input Tokens | Pricing Structure | Notes |
|---|---|---|---|
| Qwen 3.6 Plus | $0.00 | Free preview | Preview pricing; will change |
| Llama 4 Maverick | ~$0.50 | Infrastructure only | Self-hosted; varies by provider |
| Gemini 3.1 Pro | $2.00 | Flat rate | No surcharge at any context length |
| Grok 4.20 | $2.00 | Flat rate | Supports up to 2M at this rate |
| GPT-5.4 | ~$6.14 | Tiered (272K boundary) | $2.50 first 272K + $5.00 remaining 728K |
| Claude Sonnet 4.6 | $3.00 | Flat rate | No surcharge since March 13, 2026 |
| Claude Opus 4.6 | $5.00 | Flat rate | No surcharge since March 13, 2026 |
The cost difference remains meaningful. Processing a 1M-token document through Gemini 3.1 Pro costs $2.00. The same document through Claude Opus 4.6 costs $5.00 — a 2.5x premium. Since Anthropic eliminated long-context surcharges on March 13, 2026, this gap has narrowed considerably (it was previously 4.5x with tiered pricing). Over a pipeline processing hundreds of documents daily, even the 2.5x difference compounds into meaningful monthly costs. The pricing difference reflects Anthropic's positioning of Opus 4.6 as a premium reasoning model where superior analysis quality on complex tasks justifies the cost.
For teams managing AI budgets across multiple workloads, this pricing landscape rewards a multi-model strategy: use cost-efficient models (Gemini 3.1 Pro, Grok 4.20) for high-volume document processing, and reserve premium models (Claude Opus 4.6, GPT-5.4 Thinking) for tasks where reasoning quality justifies the cost. Our GPT-5.4 vs Opus 4.6 vs Gemini 3.1 Pro comparison provides benchmark-level detail for making these tradeoff decisions.
Effective vs. Advertised Context
Every model discussed in this guide advertises a maximum context window. None of them maintain peak performance at that maximum. This is not a deficiency of specific models — it is a fundamental property of how transformer-based architectures handle very long sequences. Understanding the gap between advertised and effective context is critical for production system design.
Context Degradation Pattern
Research from multiple independent labs and benchmark suites has established a consistent pattern:
High fidelity zone. Recall accuracy and reasoning quality remain near peak levels. A 1M-token model performs excellently within the first 600K tokens.
Degradation zone. Recall of facts placed in the middle of the context begins to drop. The “lost in the middle” effect becomes measurable. Information at the start and end of the context remains accessible, but central content is increasingly missed.
Unreliable zone. Performance drops are no longer gradual — they become sudden and unpredictable. A model claiming 200K tokens may fail to retrieve facts beyond approximately 160K tokens, with sudden cliffs rather than smooth degradation.
The practical implication is straightforward: design systems to target 60-70% of advertised context as the working maximum. For a 1M-token model, plan for 600K-700K tokens of reliable content plus room for system prompts, instructions, and output space. For Llama 4 Scout's 10M window, plan for 1-2M tokens of reliable synthesis context and use the remaining capacity for retrieval-oriented queries where missing a few mid-context facts is acceptable.
Use Case Matching: Which Window for Which Task
Context window selection should be driven by workload requirements, not by maximizing window size. Larger windows cost more per request, increase latency, and — as discussed — do not maintain peak quality at their limits. The optimal strategy is matching window size to actual need.
| Use Case | Typical Context Need | Recommended Model(s) | Why |
|---|---|---|---|
| Chat / customer support | 8K-32K | GPT-5.4 Mini, Mistral Small 4 | Cost-efficient; context rarely exceeded |
| Single document analysis | 50K-200K | Claude Sonnet 4.6, GPT-5.4 | Within standard pricing tiers |
| Multi-document synthesis | 200K-600K | Gemini 3.1 Pro, Qwen 3.6 Plus | Flat pricing; no surcharge penalty |
| Full codebase reasoning | 500K-1M | Claude Opus 4.6, GPT-5.4 | Superior code reasoning quality |
| Agentic multi-step workflows | 100K-500K | Grok 4.20, Claude Opus 4.6 | Strong tool use + large working memory |
| Enterprise document search | 1M-10M | Llama 4 Scout | Only model for 10M retrieval tasks |
| Long conversation memory | 200K-2M | Grok 4.20, Llama 4 Maverick | Days of conversation without summarization |
The key insight is that most production workloads operate well within the 200K-token range, where all frontier models perform similarly and pricing differences are minimal. The 1M+ tier becomes relevant for specific high-value use cases — codebase analysis, legal discovery, research synthesis — where the cost premium is justified by the elimination of complex retrieval pipelines and the ability to reason across entire document sets. For teams evaluating where AI fits in their operations, our AI and digital transformation services help match model capabilities to actual business requirements.
Context Window Optimization Strategies
Having access to a 1M-token window does not mean every request should use 1M tokens. Effective context management directly impacts cost, latency, and output quality. The following strategies are proven in production deployments.
Start with minimal context and expand only when the initial response indicates the model needs more information. A two-pass approach — first pass with a summary, second pass with full documents if needed — can reduce average context usage by 60-80% while maintaining quality for the requests that genuinely need full context.
Use RAG to retrieve the most relevant documents from a large corpus, then load those documents into a long-context window for cross-document reasoning. This combines the search efficiency of RAG with the synthesis capabilities of long context. The RAG stage handles scale (millions of documents); the long-context stage handles reasoning depth (across hundreds of pages).
Route requests to different models based on required context size. Under 128K: use efficient models like GPT-5.4 Mini or Mistral Small 4. 128K-1M: route to Gemini 3.1 Pro for flat-rate pricing. Over 1M: route to Grok 4.20 or Llama 4 Scout. This routing reduces costs by 40-70% compared to sending all requests to a single premium model, while maintaining quality where it matters.
Both Google (Gemini) and Anthropic (Claude) offer context caching APIs that allow repeated requests against the same base context at reduced cost. If your workflow involves asking multiple questions about the same large document, caching reduces the per-query cost by 75-90% after the initial context load. This is particularly effective for legal review, code auditing, and research workflows.
Strategic Implications for Business Leaders
Context window expansion has moved from a technical curiosity to a strategic business consideration. The ability to process larger documents in a single AI request directly impacts which business processes can be automated, which products can be built, and which competitive advantages are available. Three strategic implications stand out.
1. RAG Complexity Is No Longer Required for Many Workloads
Retrieval-augmented generation (RAG) pipelines were built to compensate for small context windows. A 32K window required vector databases, embedding models, retrieval logic, re-ranking, and chunk management to work with large document sets. At 1M tokens, many document sets fit in a single request, eliminating the entire retrieval pipeline for those workloads. This simplifies architecture, reduces maintenance burden, and eliminates the retrieval errors that RAG systems introduce. Not all RAG becomes obsolete — corpus-scale search still requires it — but the threshold for needing RAG has risen dramatically.
2. AI Agent Capabilities Have Expanded
AI agents accumulate context rapidly: every tool call, web search result, file read, and intermediate reasoning step adds tokens. A complex agentic session can consume 100K-500K tokens before completing its task. The expansion to 1M-2M context windows means agents can now execute longer, more complex multi-step workflows without hitting context limits that previously forced session truncation or context summarization. This enables agentic workflows that were architecturally impossible 18 months ago — entire project planning cycles, multi-day research tasks, and complex debugging sessions within a single agent context.
3. Cost Management Becomes a First-Order Architecture Decision
When context was limited to 128K tokens, cost per request was bounded by that limit. At 1M tokens, a single request to Claude Opus 4.6 can cost $9.00 in input tokens alone (before output). A pipeline processing 100 documents per day at that rate generates $27,000 in monthly API costs. This makes context-window cost optimization — tiered routing, caching, progressive loading, and provider selection — as strategically important as the AI capability itself. Teams that treat context management as an afterthought will face budget overruns that undermine the ROI case for their AI investments.
The bottom line: Context window size is no longer a limiting constraint for most applications — cost and effective recall quality are. The strategic question has shifted from “can we fit our data in the context?” to “what is the most cost-effective way to fit our data in the context while maintaining the quality our use case requires?”
Conclusion
The context window landscape in April 2026 is defined by abundance. Five models at 1M tokens, one at 2M, and one at 10M provide more than enough capacity for nearly every business use case. The competition has shifted from raw context size to the tradeoffs that matter in production: effective recall accuracy, pricing structure, architecture compatibility, and the quality of reasoning within those large contexts.
For most teams, the optimal strategy is a multi-model approach: Gemini 3.1 Pro or Qwen 3.6 Plus for cost-efficient large-context workloads, Claude Opus 4.6 or GPT-5.4 Thinking for premium reasoning tasks, and Llama 4 Scout for the rare use cases that genuinely require 10M-token processing. The open-weight ecosystem — covered in depth in our Open-Source AI Landscape April 2026 guide — adds self-hosted options that reduce per-token costs to infrastructure only, which is particularly compelling at the 1M+ tier where API costs become significant.
Build provider abstraction into your architecture now. The model that offers the best context-to-cost ratio today will not hold that position in six months. The teams that will sustain their AI advantage are those that can swap models by changing a configuration value rather than rewriting application code.
Optimize Your AI Architecture
Choosing the right context window for each workload can reduce AI costs by 40-70% while maintaining quality. Our team helps businesses design multi-model pipelines that match context requirements to the most cost-effective providers.
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