Compare

Anthropic Claude vs Olmo 3 32B Think

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

Anthropic Claude

Context window

100K

100,000 tokens · ~75K words

Model page
Allenai

Model

Olmo 3 32B Think

Context window

66K

65,536 tokens · ~49K 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.

Anthropic Claude100K
Olmo 3 32B Think66K

Anthropic Claude has about 1.5× the context window of the other in this pair.

Anthropic Claude has 52% more context capacity (100K vs 65K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Anthropic Claude. Its 100K context fits entire documents without chunking (vs 65K).

  • Long output (reports, code files)

    Use Olmo 3 32B Think. Its 65K 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.

SpecAnthropic ClaudeOlmo 3 32B Think
Context window100,000 tokens (100K)65,536 tokens (65K)
Max output tokens8,191 tokens (8K)65,536 tokens (65K)
Speed tierBalancedDeep
VisionNoNo
Function callingNoNo
Extended thinkingNoYes
Prompt cachingNoNo
Batch APIYesNo
Release dateN/ANov 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.

ProviderAnthropic Claude inAnthropic Claude outOlmo 3 32B Think inOlmo 3 32B Think out
Aws Bedrock$8.00/M$24.00/M

Frequently asked questions

Anthropic Claude has a larger context window: 100K tokens vs 65K. 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.

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