Context Window

Compare

Dolphin vs Sonar Pro

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.

Microsoft

Model

Dolphin

Context window

16K

16,384 tokens · ~12K words

Model page
Perplexity

Model

Sonar Pro

Image input

Context window

200K

200,000 tokens · ~150K 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.

Dolphin16K
Sonar Pro200K

Sonar Pro has about 12.2× the context window of the other in this pair.

Token calculator →Memory savings →

Sonar Pro has 1120% more context capacity (200K vs 16K tokens). Dolphin is 83% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Sonar Pro. Its 200K context fits entire documents without chunking (vs 16K).

  • RAG / high-volume retrieval

    Use Dolphin. Input tokens are 83% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Dolphin. Its 16K 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.

SpecDolphinSonar Pro
Context window16,384 tokens (16K)200,000 tokens (200K)
Max output tokens16,384 tokens (16K)8,000 tokens (8K)
Speed tierBalancedBalanced
VisionNoYes
Function callingNoNo
Extended thinkingNoNo
Prompt cachingNoNo
Batch APINoNo
Release dateN/AMar 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.

ProviderDolphin inDolphin outSonar Pro inSonar Pro out
Nlp Cloud$0.500/M$0.500/M——
Perplexity——$3.00/M$15.00/M

Frequently asked questions

Sonar Pro has a larger context window: 200K tokens vs 16K. 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