Context Window

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Qwen3 Coder Plus vs Sonar Reasoning 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.

Alibaba

Model

Qwen3 Coder Plus

Tool calling

Context window

998K

997,952 tokens · ~748K words

Model page
Perplexity

Model

Sonar Reasoning Pro

Image input

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 Coder Plus998K
Sonar Reasoning Pro128K

Qwen3 Coder Plus has about 7.8× the context window of the other in this pair.

Token calculator →Memory savings →

Qwen3 Coder Plus has 679% more context capacity (997K vs 128K tokens). Qwen3 Coder Plus is 50% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Qwen3 Coder Plus. Its 997K context fits entire documents without chunking (vs 128K).

  • RAG / high-volume retrieval

    Use Qwen3 Coder Plus. Input tokens are 50% 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 Coder PlusSonar Reasoning Pro
Context window997,952 tokens (997K)128,000 tokens (128K)
Max output tokens65,536 tokens (65K)N/A
Speed tierBalancedDeep
VisionNoYes
Function callingYesNo
Extended thinkingYesYes
Prompt cachingYesNo
Batch APINoNo
Release dateSep 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 Coder Plus inQwen3 Coder Plus outSonar Reasoning Pro inSonar Reasoning Pro out
Alibaba Cloud————
Openrouter$1.00/M$5.00/M——
Perplexity——$2.00/M$8.00/M

Frequently asked questions

Qwen3 Coder Plus has a larger context window: 997K 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.
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