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Claude Instant vs Solar Pro 3

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
Upstage

Model

Solar Pro 3

Tool calling

Context window

128K

128,000 tokens · ~96K 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
Solar Pro 3128K

Solar Pro 3 has about 1.3× the context window of the other in this pair.

Solar Pro 3 has 28% more context capacity (128K vs 100K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Solar Pro 3. Its 128K context fits entire documents without chunking (vs 100K).

Full specs

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

SpecClaude InstantSolar Pro 3
Context window100,000 tokens (100K)128,000 tokens (128K)
Max output tokens8,191 tokens (8K)N/A
Speed tierBalancedBalanced
VisionNoNo
Function callingNoYes
Extended thinkingNoYes
Prompt cachingNoYes
Batch APIYesNo
Release dateN/AJan 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.

ProviderClaude Instant inClaude Instant outSolar Pro 3 inSolar Pro 3 out
Aws Bedrock$0.800/M$2.40/M

Frequently asked questions

Solar Pro 3 has a larger context window: 128K 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.

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