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    Long Context Models

    Models with context windows of 200K tokens or more — up to 2M — for whole-codebase and multi-document workloads

    Compare
    Use Case
    Capabilities
    Provider
    Status
    Input Price ($/M tokens)
    Output Price ($/M tokens)
    Context Size (tokens)
    186/351
    Models
    43/51
    Providers
    123
    Vision Models (filtered)
    168
    Tool-enabled (filtered)
    2
    Free Models (filtered)
    Features
    Cerebras
    glm-4.6
    		$2.25$2.75—
    Z AI
    glm-4.6
    		$0.60$2.20$0.11
    NovitaAI
    glm-4.6
    		$0.55$2.20$0.11
    NovitaAI
    glm-4.6
    		$0.55$2.20$0.11
    Z AI
    glm-4.6
    		$0.60$2.20$0.11
    Alibaba Cloud
    glm-4.6
    		$0.43$2.01—
    EmberCloud
    glm-4.7-flash
    		$0.06$0.40$0.01
    EmberCloud
    glm-4.7-flash
    		$0.06$0.40$0.01
    Z AI
    glm-4.7-flash-free
    		$0.00$0.00$0.00
    Z AI
    glm-4.7-flashx
    		$0.07$0.40$0.01
    Z AI
    glm-4.7-flashx
    		$0.07$0.40$0.01
    Cerebras
    glm-4.7
    		$2.25$2.75—
    NovitaAI
    glm-4.7
    		$0.60$2.20$0.11
    Together AI
    glm-4.7
    		$0.45$2.00—
    EmberCloud
    glm-4.7
    		$0.38$1.98$0.19
    EmberCloud
    glm-4.7
    		$0.38$1.98$0.19
    Z AI
    glm-4.7
    		$0.60$2.20$0.11
    NovitaAI
    glm-4.7
    		$0.60$2.20$0.11
    Z AI
    glm-4.7
    		$0.60$2.20$0.11
    Cerebras
    glm-4.7
    		$2.25$2.75—
    Together AI
    glm-4.7
    		$0.45$2.00—
    ByteDance
    glm-4.7
    		$0.60$2.20$0.11
    Alibaba Cloud
    glm-4.7
    		$0.43$2.01—
    ByteDance
    glm-4.7
    		$0.60$2.20$0.11
    Vertex AI (OpenAI-compatible)
    glm-4.7
    		$0.60$2.20—
    Vertex AI (OpenAI-compatible)
    glm-4.7
    		$0.60$2.20—
    Vertex AI (OpenAI-compatible)
    glm-5
    		$1.00$3.20$0.10
    Alibaba Cloud(cn-beijing)
    glm-5
    		$0.57$2.58—
    EmberCloud
    glm-5
    		$0.72$2.30$0.14
    EmberCloud
    glm-5
    		$0.72$2.30$0.14
    Z AI
    glm-5
    		$1.00$3.20$0.20
    Z AI
    glm-5
    		$1.00$3.20$0.20
    Nebius AI
    glm-5
    		$1.00$3.20—
    Together AI
    glm-5
    		$1.00$3.20—
    NovitaAI
    glm-5
    		$1.00$3.20$0.20
    Baidu
    glm-5
    		$1.00$3.20$0.20
    Vertex AI (OpenAI-compatible)
    glm-5
    		$1.00$3.20$0.10
    Alibaba Cloud
    glm-5
    		$0.57$2.58—
    Baidu
    glm-5
    		$1.00$3.20$0.20
    Alibaba Cloud
    glm-5
    		$0.57$2.58—
    NovitaAI
    glm-5
    		$1.00$3.20$0.20
    Nebius AI
    glm-5.1
    		$1.40$4.40—
    NovitaAI
    glm-5.1
    		$1.38$4.40$0.26
    EmberCloud
    glm-5.1
    		$0.93$2.93$0.17
    Nebius AI
    glm-5.1
    		$1.40$4.40—
    EmberCloud
    glm-5.1
    		$0.93$2.93$0.17
    Baidu
    glm-5.1
    		$1.40$4.40$0.26
    Z AI
    glm-5.1
    		$1.40$4.40$0.26
    Baidu
    glm-5.1
    		$1.40$4.40$0.26
    Z AI
    glm-5.1
    		$1.40$4.40$0.26
    Page 1 of 16

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    • ByteDance
    • MiniMax
    • EmberCloud
    • Meta
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    © 2026 OffRail. All rights reserved.

    Every model on this page accepts at least 200,000 tokens of context — roughly 150,000 words — and the largest stretch much further: Grok 4.1 Fast at 2 million tokens, with Gemini, Claude Sonnet 5, GPT-5.4, DeepSeek V4, and GLM-5.2 at or above the million-token mark. That's enough to fit an entire codebase, a legal document set, or months of chat history into a single prompt.

    Advertised size isn't everything: retrieval quality can degrade well before the window is full, and long prompts get expensive fast. Cached input pricing — shown in the list — matters more than the headline price when you re-send large contexts on every request.

    Frequently asked questions

    Which LLM has the largest context window?

    Grok 4.1 Fast currently leads with a 2 million token window. Gemini models run just over 1 million, and Claude Sonnet 5, GPT-5.4, DeepSeek V4, GLM-5.2, and Qwen3.7 also offer million-token windows.

    How many words fit in a 200K context window?

    Roughly 150,000 English words — about 600 pages. A million-token window fits around 750,000 words: several full-length books, or a mid-sized codebase.

    Do models actually use the full window well?

    Not uniformly. Most models recall the start and end of a prompt better than the middle, and effective context is often smaller than the advertised maximum. For critical retrieval over huge inputs, test with your own data and consider chunking plus retrieval instead of one giant prompt.

    How do I keep long-context costs down?

    Use cached input pricing: providers charge a fraction of the normal rate for re-sent, unchanged prefixes, which is exactly the shape of chatting over a large document or codebase. Structure prompts so the big static context comes first and only the question changes.

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