Qwen3.8-Max is Alibaba’s August 3, 2026 flagship and, at 2.4 trillion parameters (95B active), the biggest model the Qwen team has published with benchmarks. The pitch: “second only to Fable 5” — and on agentic and research benchmarks, the numbers back it up. On deep software engineering? Not so much.
The real story is the combination: frontier-competitive scores at $2/$6 per million tokens — roughly a third of Claude Opus 5 and a quarter of GPT-5.6 Sol — with open weights (and a 27B distillation) promised within days. If Alibaba delivers on the open-weight pledge, Qwen3.8-Max becomes the most capable downloadable model by a margin. Qwen3.7-Max is its immediate predecessor, now outpaced across the board.
Architecture
| Spec | Value |
|---|---|
| Total parameters | 2.4T |
| Active parameters | ~95B per token (~4% of network) |
| Architecture | Sparse MoE transformer (based on Qwen 3.5) |
| Context window | 1M tokens |
| Input modalities | Text, image, video |
| Output modalities | Text |
| API model ID | qwen3.8-max |
| Reasoning effort | low / medium / high |
Pricing
| Rate / 1M tokens | |
|---|---|
| Input | $2.00 |
| Output | $6.00 |
Compatible with both OpenAI and Anthropic API protocols on QwenCloud.
Benchmarks
All numbers are vendor-reported from Alibaba’s August 3 launch post. Independent validation is still limited — treat these as directional, not settled.
| Benchmark | Qwen 3.8-Max | Claude Fable 5 | GPT-5.6 Sol | Claude Opus 4.8 |
|---|---|---|---|---|
| TerminalBench 2.1 | 86.6 | 84.6 | 88.8 | 84.6 |
| SWE-bench Pro | 67.7 | 80.0 | 64.6 | 69.2 |
| PaperBench | 93.0 | 88.8 | 90.5 | 80.3 |
| GPQA Diamond | 92.6 | 92.6 | 94.1 | 92.0 |
| OSWorld-Verified | 86.1 | 85.0 | 83.2 | — |
| IFBench | 82.8 | 63.5 | 72.7 | 62.2 |
| HLE (Humanity’s Last Exam) | 43.6 | 53.3 | 47.2 | 45.7 |
The pattern: Qwen 3.8-Max dominates agentic/research rows (OSWorld, PaperBench, IFBench) but trails Fable 5 badly on core software engineering (SWE-bench Pro gap of 12.3 points, FrontierSWE gap of 15.3 points). “Second only to Fable 5” is accurate on some axes, misleading on others.
The open-weight question
Alibaba’s launch post states: “the first open-weight model at Max scale… open weights will be released next week.” Two weights are promised:
- Qwen3.8-Max (Qwen3.8-2.4T-A95B) — the full 2.4T model
- Qwen3.8-27B — a distilled variant for self-hosting on more accessible hardware
Update (August 11): A countdown on ModelScope points to the Qwen3.8-2.4T-A95B weights releasing on August 12, 2026. Qwen3.8-27B is expected to follow shortly after. The license terms are still not published. This is the first time a Max-class Qwen model has been promised as open-weight — the community is watching closely.
How it compares
- vs Claude Fable 5: Fable wins deep coding by double digits. Qwen wins agentic computer use and research tasks. Fable costs $10/$50 — 5× more on input, 8× on output.
- vs GPT-5.6 Sol: Sol leads TerminalBench (88.8 vs 86.6) and GPQA Diamond (94.1 vs 92.6). Qwen leads OSWorld and PaperBench. Sol costs $5/$30 — 2.5×/5× more.
- vs Claude Opus 5: Opus 5 sits at $5/$25 with broader agentic tooling. Qwen matches or exceeds on most benchmarks at 40% of the price.
- vs Kimi K3: Kimi K3 is 2.8T (larger) and already open-weight. Qwen 3.8-Max claims higher benchmark scores across the board — but K3’s weights are downloadable today.
- vs Qwen3.7-Max: The predecessor. Qwen 3.8-Max improves TerminalBench from 74.5 to 86.6, PaperBench from 64.8 to 93.0, SWE-bench Pro from 60.6 to 67.7. A generational leap.
Limitations
- SWE-bench gap is real. 67.7 on SWE-bench Pro means Qwen 3.8-Max is not the model to pick for deep, autonomous software engineering — Fable 5 at 80.0 remains the clear choice for that job.
- Vendor benchmarks only. The full table was published alongside the launch with no independent replication yet.
- Open weights not yet shipped. Pricing and positioning are based on API access; the self-hosting story depends on Alibaba delivering.
- Multimodal scope unclear. “Text + visual inputs confirmed” — but whether video, documents and speech are all natively supported or vary by mode is not fully specified.
For the full AI model landscape, see the 2026 LLM guide.
Best for
- Long-horizon autonomous agents and computer-use tasks
- End-to-end research reproduction and scientific workflows
- Cost-effective frontier inference at $2/$6 vs $5/$25+ proprietary alternatives
- Downstream fine-tuning once open weights ship
Pros & cons
Strengths
- Top scores on agentic benchmarks — OSWorld-Verified 86.1 (ahead of Fable 5's 85.0 and GPT-5.6 Sol's 83.2)
- PaperBench 93.0 — highest reported score for end-to-end research reproduction
- Aggressive pricing at $2/$6 — roughly 1/3 of Opus 5 and 1/4 of GPT-5.6 Sol
- Open weights (plus a 27B variant) promised imminently — first open-weight Max-class model from Alibaba
Limitations
- SWE-bench Pro 67.7 — trails Fable 5 by 12+ points and Opus 4.8 by ~1.5 points on deep coding
- Benchmarks are vendor-reported — limited independent validation so far
- Open weights not yet released as of August 5, 2026 — license terms still unwritten
- Multimodal input scope (video, documents, speech) not fully specified by Alibaba
Frequently asked questions
When was Qwen 3.8-Max released?
Alibaba previewed Qwen 3.8-Max on July 19, 2026 at the World AI Conference in Shanghai. The full launch with published specs, benchmarks and API access came on August 3, 2026.
How big is Qwen 3.8-Max?
2.4 trillion total parameters in a sparse Mixture-of-Experts configuration, with approximately 95 billion parameters active per token (~4% of the network firing per query). Built on the Qwen 3.5 architecture.
Is Qwen 3.8-Max open source?
Not yet, but imminent. A ModelScope countdown points to the Qwen3.8-2.4T-A95B weights releasing on August 12, 2026. Qwen3.8-27B is expected to follow shortly after. The license terms have not been published. Check Hugging Face and ModelScope for updates.
How does Qwen 3.8-Max compare to Fable 5?
Qwen 3.8-Max leads on OSWorld-Verified (86.1 vs 85.0), PaperBench (93.0 vs 88.8) and TerminalBench (86.6 vs 84.6). But Fable 5 crushes it on SWE-bench Pro (80.0 vs 67.7) and FrontierSWE (88.8 vs 73.5) — a 12–15 point gap on exactly the deep coding benchmarks that matter most for software engineering agents.
What is reasoning_effort?
Qwen 3.8-Max supports three reasoning effort levels: high (complex tasks demanding thorough analysis), medium (balancing accuracy and cost), and low (efficient reasoning optimized for speed). This maps to the same concept as Anthropic's effort tiers.