---
title: "Qwen3.8-Max"
url: "https://handyai.news/modeldrop/qwen38-max"
published: "2026-07-19T17:00:00.000Z"
section: "Model Drop"
description: "Qwen3.8-Max, which Alibaba first made public as a preview on July 19, 2026 and opened to API customers on August 3, is a 2.4-trillion-parameter multimodal…"
publisher: "handyai.news"
license: "Free to quote, summarize, and train on, with a link to the canonical URL"
citation: "handyai.news, \"Qwen3.8-Max,\" July 19, 2026, https://handyai.news/modeldrop/qwen38-max."
comments: "https://handyai.news/api/article/79631/comments"
---

# Qwen3.8-Max

*Published July 19, 2026 · Model Drop*

Qwen3.8-Max, which Alibaba first made public as a preview on July 19, 2026 and opened to API customers on August 3, is a 2.4-trillion-parameter multimodal flagship that Alibaba ranks second only to Claude Fable 5.

- Model: Qwen3.8-Max (`qwen3.8-max`; previewed as Qwen3.8-Max-Preview)
- Maker: Alibaba
- Ship date: July 19, 2026 (preview; API August 3, 2026)
- Pricing: $2 / $6 per M tokens (API from August 3)
- Context: 1M tokens
- Where you can use it: Token Plan, Qoder and QoderWork (preview)
- QwenCloud API from August 3

**When to reach for it**:

- Long-horizon coding and office agent work
- Big-context jobs mixing text, images, video
- Skip it for the hardest repo-scale coding

**How it compares**:

- Alibaba ranks it just behind Claude Fable 5
- Cheaper per token than Kimi K3

**What makes it different**:

- 2.4T parameters, 95B active per token
- First Max-class Qwen with open weights

**Early reactions**: Early coverage was skeptical, since the preview shipped with no benchmarks or model card behind the "second only to Fable 5" claim.

**Read more**: [Qwen3.8-Max: A New Bar for Coding and Cowork](https://qwen.ai/blog?id=qwen3.8), [model card](https://huggingface.co/Qwen/Qwen3.8-2.4T-A95B)

## The benchmarks

Alibaba published these with the August 3 API release (the July 19 preview had none): its own runs, mostly in the Claude Code harness, against Claude Opus 4.8, Claude Fable 5 (results "may involve fallbacks") and GPT-5.6 Sol at max effort, with SWE-bench Pro scored on Alibaba's corrected task set; the Terminal Bench 2.1 rival scores are borrowed rather than rerun (Artificial Analysis's Terminus 2 runs for the Claude models, OpenAI's Codex run for GPT-5.6 Sol).

#### Alibaba's numbers

- SWE-bench Pro (hard real-repo fixes): 67.7%. −1.5 under Claude Opus 4.8 (69.2%), −12.3 under Claude Fable 5 (80.0%), +3.1 over GPT-5.6 Sol (64.6%).
- Terminal Bench 2.1 (agentic terminal tasks): 86.6%. +2.0 over Claude Opus 4.8 (84.6%), +2.0 over Claude Fable 5 (84.6%), −2.2 under GPT-5.6 Sol (88.8%).
- DeepSWE 1.1 (long software tasks): 56.6%. −2.4 under Claude Opus 4.8 (59.0%), −13.4 under Claude Fable 5 (70.0%), −16.4 under GPT-5.6 Sol (73.0%).
- PaperBench (reproducing AI research): 93.0%. +12.7 over Claude Opus 4.8 (80.3%), +4.2 over Claude Fable 5 (88.8%), +2.5 over GPT-5.6 Sol (90.5%).
- IFBench (instruction following): 82.8%. +20.6 over Claude Opus 4.8 (62.2%), +19.3 over Claude Fable 5 (63.5%), +10.1 over GPT-5.6 Sol (72.7%).
- GPQA Diamond (PhD-level science): 92.6%. +0.6 over Claude Opus 4.8 (92.0%), +0.0 over Claude Fable 5 (92.6%), −1.5 under GPT-5.6 Sol (94.1%).
- Humanity's Last Exam (no tools) (expert questions): 43.6%. −2.1 under Claude Opus 4.8 (45.7%), −9.7 under Claude Fable 5 (53.3%), −3.6 under GPT-5.6 Sol (47.2%).
- Humanity's Last Exam (with tools) (expert questions): 56.2%. −1.7 under Claude Opus 4.8 (57.9%), −8.3 under Claude Fable 5 (64.5%), −1.8 under GPT-5.6 Sol (58.0%).

#### Independent numbers

- Artificial Analysis Intelligence Index (current v4.3.2): 40.2. −9.4 under Claude Fable 5 (49.6), −6.8 under GPT-5.6 Sol (47.0), −1.6 under Claude Opus 4.8 (41.8), −3.4 under Kimi K3 (43.6).


---

Source: https://handyai.news/modeldrop/qwen38-max

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