Context Window

Compare

Command A+ vs Deepseek R1 Distill Qwen 1 5b

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.

Cohere

Model

Command A+

Image inputTool calling

Context window

192K

192,000 tokens · ~144K words

Model page
Alibaba

Model

Deepseek R1 Distill Qwen 1 5b

Context window

131K

131,072 tokens · ~98K 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.

Command A+192K
Deepseek R1 Distill Qwen 1 5b131K

Command A+ has about 1.5× the context window of the other in this pair.

Token calculator →Memory savings →

Command A+ has 46% more context capacity (192K vs 131K tokens). Deepseek R1 Distill Qwen 1 5b is 70% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Command A+. Its 192K context fits entire documents without chunking (vs 131K).

  • RAG / high-volume retrieval

    Use Deepseek R1 Distill Qwen 1 5b. Input tokens are 70% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecCommand A+Deepseek R1 Distill Qwen 1 5b
Context window192,000 tokens (192K)131,072 tokens (131K)
Max output tokens64,000 tokens (64K)N/A
Speed tierBalancedDeep
VisionYesNo
Function callingYesNo
Extended thinkingYesNo
Prompt cachingYesNo
Batch APINoNo
Release dateSep 2026N/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.

ProviderCommand A+ inCommand A+ outDeepseek R1 Distill Qwen 1 5b inDeepseek R1 Distill Qwen 1 5b out
Nscale——$0.090/M$0.090/M
Openrouter$0.300/M$1.50/M——
Together Ai——$0.180/M$0.180/M

Frequently asked questions

Command A+ has a larger context window: 192K tokens vs 131K. For long documents, large codebases, or extended agent sessions, the larger context window reduces the need to chunk inputs or summarize history.

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Example: a multi-turn chat session

Without Mem0~128K tokens sent
Full history
Repeated info
Old context
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80% less to send — works with any model