Context Window

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Qwen3 30B A3B vs Sonar Deep Research

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.

Alibaba

Model

Qwen3 30B A3B

Tool calling

Context window

129K

129,024 tokens · ~97K words

Model page
Perplexity

Model

Sonar Deep Research

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.

Qwen3 30B A3B129K
Sonar Deep Research128K

Qwen3 30B A3B has about 1× the context window of the other in this pair.

Token calculator →Memory savings →

Qwen3 30B A3B has 0% more context capacity (129K vs 128K tokens). Qwen3 30B A3B is 96% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Qwen3 30B A3B. Its 129K context fits entire documents without chunking (vs 128K).

  • RAG / high-volume retrieval

    Use Qwen3 30B A3B. Input tokens are 96% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecQwen3 30B A3BSonar Deep Research
Context window129,024 tokens (129K)128,000 tokens (128K)
Max output tokens16,384 tokens (16K)N/A
Speed tierFastBalanced
VisionNoNo
Function callingYesNo
Extended thinkingYesYes
Prompt cachingNoNo
Batch APINoNo
Release dateApr 2025Mar 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.

ProviderQwen3 30B A3B inQwen3 30B A3B outSonar Deep Research inSonar Deep Research out
Alibaba Cloud————
Deepinfra$0.080/M$0.290/M——
Fireworks$0.150/M$0.600/M——
Nebius$0.100/M$0.300/M——
Perplexity——$2.00/M$8.00/M

Frequently asked questions

Qwen3 30B A3B has a larger context window: 129K tokens vs 128K. 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.
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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