Compare

Qwen2 5 Vl 72b vs Qwen3 1p7b Fp8 Draft 131072

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

Qwen2 5 Vl 72b

Image inputTool calling

Context window

131K

131,072 tokens · ~98K words

Model page
Alibaba

Model

Qwen3 1p7b Fp8 Draft 131072

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.

Qwen2 5 Vl 72b131K
Qwen3 1p7b Fp8 Draft 131072131K

Same context window size for both models.

Qwen2 5 Vl 72b and Qwen3 1p7b Fp8 Draft 131072 have identical context windows (131K tokens). Qwen3 1p7b Fp8 Draft 131072 is 23% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • RAG / high-volume retrieval

    Use Qwen3 1p7b Fp8 Draft 131072. Input tokens are 23% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecQwen2 5 Vl 72bQwen3 1p7b Fp8 Draft 131072
Context window131,072 tokens (131K)131,072 tokens (131K)
Max output tokens131,072 tokens (131K)131,072 tokens (131K)
Speed tierDeepFast
VisionYesNo
Function callingYesNo
Extended thinkingNoNo
Prompt cachingYesNo
Batch APINoNo
Release dateN/AN/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.

ProviderQwen2 5 Vl 72b inQwen2 5 Vl 72b outQwen3 1p7b Fp8 Draft 131072 inQwen3 1p7b Fp8 Draft 131072 out
Fireworks$0.100/M$0.100/M
Nebius$0.130/M$0.400/M
Novita$0.800/M$0.800/M
Ovhcloud$0.910/M$0.910/M

Frequently asked questions

Qwen3 1p7b Fp8 Draft 131072 has a larger context window: 131K 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.

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