---
title: "Qwen 3.8: How a 27B Open Model Rivals GPT-5.6 and Claude Opus"
url: https://daily.dev/posts/3uyn4xw5d
source_url: https://www.intelligentliving.co/qwen-3-8-27b-open-model-rivals-gpt-5-6
type: share
source: "nerdalytics"
author: "nerdalytics"
published: 2026-08-24T23:00:19.883Z
updated: 2026-08-24T23:01:32.621Z
tags: ["ai", "open-source", "qwen", "mixture-of-experts"]
upvotes: 36
comments: 8
language: en
---

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# Qwen 3.8: How a 27B Open Model Rivals GPT-5.6 and Claude Opus

**[nerdalytics](https://daily.dev/sources/jcwojcedqzpspqbvk73nc)** · [@nerdalytics](https://daily.dev/nerdalytics) · 36 upvotes · 8 comments

## Summary

Alibaba released Qwen 3.8, a new open-weight model family with two checkpoints: a 27.8B dense vision-language model that runs on a single 24GB consumer GPU, and a 2.4T-parameter (95B active) mixture-of-experts flagship, the first Qwen Max-class model with open weights. Benchmarks show the 27B model beating Claude Opus 4.6 Max on coding and computer-use tasks like SWE-bench Pro and OSWorld-Verified, though it lags on harder reasoning tests. The 2.4T flagship tops PaperBench versus GPT-5.6 Sol and Claude Opus 4.8, and QwenCloud pricing undercuts Anthropic and OpenAI significantly. Quantized GGUF builds let the 27B run on GPUs with as little as 12GB VRAM, and a 35B-A3B model is expected next though unconfirmed.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.intelligentliving.co/qwen-3-8-27b-open-model-rivals-gpt-5-6>

## Community discussion

Top comments from developers on daily.dev.

**@petercheung** · 4 upvotes

> Qwen 3.8 27b can only write very popular language such as java/rust, if you ask it to write verilog, not working

**@ra\_jeeves** · 2 upvotes

> Interesting. Haven't tried it yet.

**@bits\_and\_bytes** · 2 upvotes

> waiting to see in azure foundry.

**@namanupadhye3** · 1 upvotes

> how good is it at multi-lingual agentic systems , has anyone tried this ?

**@jliter85** · 1 upvotes

> The local AI aspect is what really caught my attention here. Seeing a 27B open model compete this closely with much larger closed models while still being practical enough to run on consumer hardware shows how quickly the gap is narrowing. I also appreciate the benchmark caveat, independent testing will be important, but the direction open-weight AI is heading is exciting.

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---

Tags: [#ai](https://daily.dev/tags/ai), [#open-source](https://daily.dev/tags/open-source), [#qwen](https://daily.dev/tags/qwen), [#mixture-of-experts](https://daily.dev/tags/mixture-of-experts)

[View this post on daily.dev](https://daily.dev/posts/3uyn4xw5d)