Context Window

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Grok 4.6 vs Llama 3 1 405b

This page is context-first: how much text each model can take in one request. Full specs adds capabilities and limits; the pricing matrix below is only about $/million tokens from hosts that list both models.

Xai

Model

Grok 4.6

Image inputTool calling

Context window

500K

500,000 tokens · ~375K words

Model page
Meta

Model

Llama 3 1 405b

Context window

4K

4,096 tokens · ~3K words

Model page

Context window · side by side

Bar length is relative to the larger of the two windows (100% = max of this pair). This is not pricing.

Grok 4.6500K
Llama 3 1 405b4K

Grok 4.6 has about 122.1× the context window of the other in this pair.

Token calculator →Memory savings →

Grok 4.6 has 12107% more context capacity (500K vs 4K tokens). Grok 4.6 is 42% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Grok 4.6. Its 500K context fits entire documents without chunking (vs 4K).

  • RAG / high-volume retrieval

    Use Grok 4.6. Input tokens are 42% cheaper — critical when sending large retrieved contexts.

Full specs

Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.

SpecGrok 4.6Llama 3 1 405b
Context window500,000 tokens (500K)4,096 tokens (4K)
Max output tokens450,000 tokens (450K)N/A
Speed tierBalancedDeep
VisionYesNo
Function callingYesNo
Extended thinkingYesNo
Prompt cachingYesNo
Batch APINoNo
Release dateAug 2026N/A

Pricing matrix

Dollar rates only: hosts that list both models, per 1M tokens. For how much text fits, use the context section above — not this table.

ProviderGrok 4.6 inGrok 4.6 outLlama 3 1 405b inLlama 3 1 405b out
Azure$2.00/M$6.00/M——
Google Vertex$2.00/M$6.00/M——
Openrouter$2.00/M$6.00/M——
Together Ai——$3.50/M$3.50/M
Xai$2.00/M$6.00/M——

Frequently asked questions

Grok 4.6 has a larger context window: 500K tokens vs 4K. For long documents, large codebases, or extended agent sessions, the larger context window reduces the need to chunk inputs or summarize history.

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Mem0 gives your AI long-term memory so you stop re-sending context on every call. That means you can use a smaller, faster, cheaper model — and still get better answers.

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Example: a multi-turn chat session

Without Mem0~128K tokens sent
Full history
Repeated info
Old context
With Mem0~20K tokens sent
Key memories
Current turn

80% less to send — works with any model