Context Window

Compare

Llama 3 8b Chat vs Llama 4 Maverick 17b 128e Instruct Fp8

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.

Meta

Model

Llama 3 8b Chat

Context window

8K

8,192 tokens · ~6K words

Model page
Meta

Model

Llama 4 Maverick 17b 128e Instruct Fp8

Image inputTool calling

Context window

1M

1,000,000 tokens · ~750K 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.

Llama 3 8b Chat8K
Llama 4 Maverick 17b 128e Instruct Fp81M

Llama 4 Maverick 17b 128e Instruct Fp8 has about 122.1× the context window of the other in this pair.

Token calculator →Memory savings →

Llama 4 Maverick 17b 128e Instruct Fp8 has 12107% more context capacity (1000K vs 8K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Llama 4 Maverick 17b 128e Instruct Fp8. Its 1000K context fits entire documents without chunking (vs 8K).

Full specs

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

SpecLlama 3 8b ChatLlama 4 Maverick 17b 128e Instruct Fp8
Context window8,192 tokens (8K)1,000,000 tokens (1000K)
Max output tokensN/A16,384 tokens (16K)
Speed tierFastFast
VisionNoYes
Function callingNoYes
Extended thinkingNoNo
Prompt cachingNoNo
Batch APINoNo
Release dateN/AN/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.

ProviderLlama 3 8b Chat inLlama 3 8b Chat outLlama 4 Maverick 17b 128e Instruct Fp8 inLlama 4 Maverick 17b 128e Instruct Fp8 out
Azure——$0.250/M$1.00/M
Deepinfra——$0.200/M$0.800/M
Ibm Watsonx——$0.371/M$1.48/M
Lambda——$0.050/M$0.100/M
Meta————
Novita——$0.270/M$0.850/M
Together Ai$0.200/M$0.200/M$0.270/M$0.850/M

Frequently asked questions

Llama 4 Maverick 17b 128e Instruct Fp8 has a larger context window: 1000K tokens vs 8K. For long documents, large codebases, or extended agent sessions, the larger context window reduces the need to chunk inputs or summarize history.

Powered by Mem0

Use a smaller model.
Get better results.

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.

Calculate your savingsLearn about Mem0

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