Alibaba released Qwen3.8-Max into general availability on August 3, 2026, a 2.4 trillion-parameter Mixture-of-Experts model with roughly 95 billion active parameters per token, a 1-million-token context window, and native text, image, and video input. The flagship ships with a published benchmark table and per-token pricing on QwenCloud, but the open-weight release Alibaba promised for the week of August 10 has not appeared on Hugging Face or ModelScope as of August 11.
What changed between the Qwen3.8-Max preview and GA?
The July preview, shown at WAIC Shanghai, carried only a slide and a claim that Qwen3.8-Max trailed only Claude Fable 5. The August 3 general availability release added three things the preview lacked: a published benchmark table, a confirmed per-token price, and a production API. Active-parameter count remains a third-party-reported figure rather than an Alibaba-published technical report.
What are the headline specifications?
- Provider: Alibaba (Qwen team), within the Qwen model family
- Parameters: 2.4 trillion total, sparse MoE, roughly 95 billion active per token
- Context window: 1,000,000 tokens (991K max input, 131K max output; reasoning chains up to 262K)
- Modalities: text, image, and video input; text output
- Input price: $2.00 per million tokens (cache miss)
- Output price: $6.00 per million tokens
- Cached input: $0.25 per million tokens (implicit) to $0.17 per million tokens (explicit read)
- Release date: August 3, 2026 (general availability)
- License: proprietary API today; open weights promised for the week of August 10, not yet published
- Availability: QwenCloud API, Alibaba Cloud Model Studio, QwenWork, Vercel AI Gateway
- API protocols: OpenAI-compatible and Anthropic Messages-compatible
- Rate limits: 2 million tokens per minute, 15,000 requests per minute
How does Qwen3.8-Max score on benchmarks?
Alibaba’s published table compares Qwen3.8-Max against Claude Fable 5, GPT-5.6, and Claude Opus 4.8 across six evaluations. Every number is Alibaba’s own vendor-run result; no independent evaluator has copied the full table yet.
- OSWorld-Verified: Qwen3.8-Max 86.1, Claude Fable 5 roughly 85.0, GPT-5.6 83.2, Claude Opus 4.8 not published
- PaperBench: Qwen3.8-Max 93.0, Claude Fable 5 88.8, GPT-5.6 90.5, Claude Opus 4.8 80.3
- Terminal-Bench 2.1: Qwen3.8-Max 86.6, Claude Fable 5 84.6, GPT-5.6 88.8, Claude Opus 4.8 84.6
- SWE-bench Pro: Qwen3.8-Max 67.7, Claude Fable 5 80.0, GPT-5.6 64.6, Claude Opus 4.8 69.2
- GPQA Diamond: Qwen3.8-Max 92.6, Claude Fable 5 92.6, GPT-5.6 94.1, Claude Opus 4.8 92.0
- IFBench: Qwen3.8-Max 82.8, Claude Fable 5 63.5, GPT-5.6 72.7, Claude Opus 4.8 62.2
- HLE (Humanity’s Last Exam): Qwen3.8-Max 43.6, Claude Fable 5 53.3, GPT-5.6 47.2, Claude Opus 4.8 45.7
The pattern: Qwen3.8-Max wins on agentic computer-use and long-document tasks (OSWorld-Verified, PaperBench, IFBench) and stays competitive on terminal agentic work, but trails Claude Fable 5 by 12 points on SWE-bench Pro and finishes last of the four flagships on HLE, nearly 10 points behind Fable 5.
What can Qwen3.8-Max actually do?
Long-horizon autonomous coding
Alibaba’s headline demo is oh-my-cli, a command-line agent framework the model built and continues to maintain on its own: turning incoming requests into GitHub issues, claiming them through a state machine, writing code, running end-to-end tests, and merging its own pull requests. As of July 30 the run had produced 265 commits, 127 pull requests, and 151 issues over 16 days without human intervention. The repository was still active on August 11, with 797 commits, 61 open issues, an Apache-2.0 license, and a commit merged 33 minutes before publication.
Research and competition tasks
Alibaba reports Qwen3.8-Max reproduced a published paper on data selection for LLM reasoning, writing roughly 7,600 lines of code and running 33 GPU training rounds over five days to land a +2.71 point improvement on AIME24 over the original paper’s method. In a separate 24-hour coding competition, the model’s entry reportedly beat 458 of 526 human teams, finishing in the 87th percentile.
Native multimodal agents at scale
Qwen3.8-Max processes documents past 200 pages and video past 100 hours using what Alibaba calls video memory graphs, and pairs GUI screen operation with visual feedback loops for verifying its own output, evaluated internally against Alibaba’s RecreationBench. This carries forward the multimodal push from Qwen3.6-Max-Preview, now applied to a model an order of magnitude larger.
How is Qwen3.8-Max priced and where can it be accessed?
Qwen3.8-Max is live on QwenCloud at $2.00 per million input tokens and $6.00 per million output tokens, with cached input as low as $0.17 per million on explicit reads. That undercuts Kimi K3’s $3.00/$15.00 rate card by a wide margin and roughly matches Qwen3.7-Max’s prior $2.50/$7.50 pricing despite the jump in scale.
Access paths: QwenCloud API (model ID qwen3.8-max), Alibaba Cloud Model Studio’s international scope, the Vercel AI Gateway at zero markup (alibaba/qwen3.8-max), and a day-one Anthropic Messages-compatible endpoint. That last option means Claude Code can point at Qwen3.8-Max by changing ANTHROPIC_BASE_URL and ANTHROPIC_MODEL with no other workflow changes, the cheapest path for a side-by-side comparison against Claude models inside an existing agent harness.
What about the open-weight release?
Alibaba said weights for Qwen3.8-Max and a smaller Qwen3.8-27B would land on Hugging Face and ModelScope during the week of August 10. One day past that window’s start, no repository has appeared for either model and no license has been named, which leaves open whether a Max-class weight would ship under the permissive Apache-2.0 license used for smaller Qwen releases like Qwen3.6-27B, or under something more restrictive. Until weights land, the accurate label for this model is not open-weight, regardless of what has been promised.
Where does Qwen3.8-Max win and where does it fall short?
Strengths
- Real, checkable benchmark table replaces the preview’s unverified marketing line
- Leads the four-way comparison on OSWorld-Verified, PaperBench, and IFBench
- $2.00/$6.00 pricing undercuts Kimi K3 by a wide margin while adding native multimodal input
- Day-one Anthropic-compatible endpoint makes it a drop-in swap for Claude Code and similar agent harnesses
- The oh-my-cli autonomous coding project is a live, publicly auditable demonstration rather than a one-time benchmark run
Weaknesses
- Trails Claude Fable 5 by 12 points on SWE-bench Pro, the harder of the two coding benchmarks in Alibaba’s own table
- Finishes last of four flagships on HLE, nearly 10 points behind Fable 5
- Every benchmark number is vendor-run by Alibaba; no independent evaluator has copied the full table yet
- Promised open weights for the week of August 10 haven’t shipped as of August 11, with no license confirmed
- Active-parameter figure (95B) comes from third-party reporting, not an Alibaba-published technical report or model card
FAQ
Is Qwen3.8-Max open source?
Not yet. Alibaba promised open weights for Qwen3.8-Max and a smaller Qwen3.8-27B during the week of August 10, 2026, but as of August 11 neither model has appeared on Hugging Face or ModelScope, and no license has been confirmed.
How much does Qwen3.8-Max cost?
$2.00 per million input tokens and $6.00 per million output tokens on QwenCloud, with cached reads as low as $0.17 per million tokens. That works out to roughly one-third of Kimi K3’s per-token cost.
Does Qwen3.8-Max actually beat GPT-5.6 and Claude Fable 5?
It depends on the task. Alibaba’s own table shows Qwen3.8-Max ahead on OSWorld-Verified, PaperBench, and IFBench, but behind Claude Fable 5 by 12 points on SWE-bench Pro and behind all three rivals on HLE. The result is not a clean sweep in either direction.
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This article summarizes reporting from awesomeagents.ai.