Context Window

Compare

Anthropic Claude vs Starcoder 16b

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
Bigcode

Model

Starcoder 16b

Context window

8K

8,192 tokens · ~6K 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
Starcoder 16b8K

Anthropic Claude has about 12.2× the context window of the other in this pair.

Token calculator →Memory savings →

Anthropic Claude has 1120% more context capacity (100K vs 8K tokens). Starcoder 16b is 97% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Anthropic Claude. Its 100K context fits entire documents without chunking (vs 8K).

  • RAG / high-volume retrieval

    Use Starcoder 16b. Input tokens are 97% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Starcoder 16b. Its 8K 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 ClaudeStarcoder 16b
Context window100,000 tokens (100K)8,192 tokens (8K)
Max output tokens8,191 tokens (8K)8,192 tokens (8K)
Speed tierBalancedBalanced
VisionNoNo
Function callingNoNo
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.

ProviderAnthropic Claude inAnthropic Claude outStarcoder 16b inStarcoder 16b out
Aws Bedrock$8.00/M$24.00/M——
Fireworks——$0.200/M$0.200/M

Frequently asked questions

Anthropic Claude has a larger context window: 100K 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