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Alibaba / Qwen Advanced

Qwen2.5-72B-Instruct

Qwen2.5-72B-Instruct is a large multilingual instruction model with 128K context, up to 8K output and Qwen-licensed weights.

General Purpose Language ModelText Freemium
In plain English

What is this model and why does it matter?

Qwen2.5-72B-Instruct is Qwen's large instruction checkpoint with 128K context, up to 8K output and multilingual support.

Large self-hosted assistantsMultilingual analysisCodingStructured outputResearch
Model overview

Qwen2.5-72B-Instruct: features, use cases and important details

Qwen2.5-72B-Instruct is the large 72B instruction-tuned checkpoint in Qwen’s September 2024 Qwen2.5 release.

Verified model facts

Qwen documents a 128K context window, up to 8K generated output, broad multilingual support and the Qwen License for this 72B release.

Best fit

It fits large self-hosted assistants, multilingual analysis, coding, structured generation and research.

Limitations

Serving a 72B model is infrastructure intensive, outputs can still be inaccurate and newer Qwen generations should be evaluated for new projects.

Get started

How to use this model

  1. Review the Qwen License.
  2. Download the exact 72B Instruct checkpoint.
  3. Provision multi-GPU inference infrastructure.
  4. Use the official chat template.
  5. Benchmark against newer Qwen generations before a new production deployment.
Copy and try

Example prompts

  • Analyze this long technical specification and identify risks.
  • Return a structured JSON comparison of these options.
  • Review this code architecture and propose improvements.
Capabilities

What it can do

  • 128K context
  • Up to 8K output
  • Multilingual instruction following
  • Coding
  • Structured output
Best for

Practical use cases

  • Large self-hosted chat
  • Enterprise analysis
  • Coding assistants
  • Research
Pricing

What does it cost?

Downloadable 72B checkpoint under the Qwen License; no provider per-token price applies to this exact weight release.

Simple summaryThe 72B checkpoint requires substantial GPU infrastructure; hosting costs depend on precision and serving architecture.

What stands out

  • Strong large-model capability
  • 128K context
  • Structured output improvements
  • Official Qwen weights

Things to consider

  • High hardware cost
  • Custom Qwen License
  • Older than newer Qwen generations
Limitations

Important restrictions and trade-offs

  • Can hallucinate
  • Long-context processing is expensive
  • No direct current token price for the checkpoint
SimplifyAITools verdict

Our editorial take

A powerful historical Qwen2.5 open-weight release for large self-hosted deployments, though new projects should compare newer Qwen generations.

References

Primary sources

  1. Open source 1 ↗
  2. Open source 2 ↗
  3. Open source 3 ↗
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