Context Window

Compare

Dbrx vs Hermes 4 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.

Databricks

Model

Dbrx

Context window

33K

32,768 tokens · ~25K words

Model page
Nous Research

Model

Hermes 4 405B

Context window

131K

131,072 tokens · ~98K 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.

Dbrx33K
Hermes 4 405B131K

Hermes 4 405B has about 4× the context window of the other in this pair.

Token calculator →Memory savings →

Hermes 4 405B has 300% more context capacity (131K vs 32K tokens). Hermes 4 405B is 16% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Hermes 4 405B. Its 131K context fits entire documents without chunking (vs 32K).

  • RAG / high-volume retrieval

    Use Hermes 4 405B. Input tokens are 16% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Hermes 4 405B. Its 117K max output lets you generate complete artifacts in one request.

Full specs

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

SpecDbrxHermes 4 405B
Context window32,768 tokens (32K)131,072 tokens (131K)
Max output tokens32,768 tokens (32K)117,964 tokens (117K)
Speed tierBalancedDeep
VisionNoNo
Function callingNoNo
Extended thinkingNoYes
Prompt cachingNoYes
Batch APINoNo
Release dateN/AAug 2025

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.

ProviderDbrx inDbrx outHermes 4 405B inHermes 4 405B out
Fireworks$1.20/M$1.20/M——
Nebius——$1.00/M$3.00/M
Openrouter——$1.00/M$3.00/M

Frequently asked questions

Hermes 4 405B has a larger context window: 131K tokens vs 32K. 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