Context Window

Compare

Llama 4 Maverick 17b 128e Instruct Fp8 vs Switchyard

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 4 Maverick 17b 128e Instruct Fp8

Image inputTool calling

Context window

1M

1,000,000 tokens · ~750K words

Model page
Nvidia

Model

Switchyard

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 4 Maverick 17b 128e Instruct Fp81M
Switchyard1M

Same context window size for both models.

Token calculator →Memory savings →

Llama 4 Maverick 17b 128e Instruct Fp8 and Switchyard have identical context windows (1000K tokens).

Full specs

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

SpecLlama 4 Maverick 17b 128e Instruct Fp8Switchyard
Context window1,000,000 tokens (1000K)1,000,000 tokens (1000K)
Max output tokens16,384 tokens (16K)N/A
Speed tierFastBalanced
VisionYesNo
Function callingYesNo
Extended thinkingNoNo
Prompt cachingNoNo
Batch APINoNo
Release dateN/ASep 2026

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 4 Maverick 17b 128e Instruct Fp8 inLlama 4 Maverick 17b 128e Instruct Fp8 outSwitchyard inSwitchyard 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$0.270/M$0.850/M——

Frequently asked questions

Switchyard has a larger context window: 1000K tokens vs 1000K. 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