Context Window

Compare

Claude Instant vs Gpt Oss 120b Mxfp Gguf

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.

Anthropic

Model

Claude Instant

Context window

100K

100,000 tokens · ~75K words

Model page
Openai

Model

Gpt Oss 120b Mxfp Gguf

Tool calling

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.

Claude Instant100K
Gpt Oss 120b Mxfp Gguf131K

Gpt Oss 120b Mxfp Gguf has about 1.3× the context window of the other in this pair.

Token calculator →Memory savings →

Gpt Oss 120b Mxfp Gguf has 31% more context capacity (131K vs 100K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Gpt Oss 120b Mxfp Gguf. Its 131K context fits entire documents without chunking (vs 100K).

  • Long output (reports, code files)

    Use Gpt Oss 120b Mxfp Gguf. Its 32K 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.

SpecClaude InstantGpt Oss 120b Mxfp Gguf
Context window100,000 tokens (100K)131,072 tokens (131K)
Max output tokens8,191 tokens (8K)32,768 tokens (32K)
Speed tierBalancedBalanced
VisionNoNo
Function callingNoYes
Extended thinkingNoNo
Prompt cachingNoNo
Batch APIYesNo
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.

ProviderClaude Instant inClaude Instant outGpt Oss 120b Mxfp Gguf inGpt Oss 120b Mxfp Gguf out
Aws Bedrock$0.800/M$2.40/M——
Lemonade————

Frequently asked questions

Gpt Oss 120b Mxfp Gguf has a larger context window: 131K tokens vs 100K. 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