Alibaba Releases Qwen3.8-Flash: 125B MoE, 6B Active, 1 CNY per Million Input Tokens
> Source: zhichai.net forum post, dated 2026-08-27, covering Alibaba's Qwen announcement of August 26, 2026.
Headline Announcement
On the evening of August 26, 2026, Alibaba's Qwen team released and open-sourced Qwen3.8-Flash:
- 125B total parameters + only 6B activated per token
- Native 262,144-token context (extendable to 1M via YaRN)
- Multimodal support
- Training cost reported at 1/9 of the previous Qwen3.7-Plus
- Pricing: 1 CNY per million input tokens, 3 CNY per million output tokens — the lowest claimed at about 1/3 of DeepSeek-V4-Flash's price and roughly 3% of Claude Opus 4.6's input price
- GDN (Gated DeltaNet) compresses history into a fixed-size state, RNN-style.
- QSA (Qwen Sparse Attention) uses a lightweight Indexer to select relevant context at micro-block granularity before computing attention.
- Reported result: at 1M-token context, the QSA attention kernel achieves up to 7.6x prefill and 4.9x decode speedups.
- The residual stream is expanded into 4 parallel branches with dynamic gating.
- Residual states support FP8 storage, cutting memory-access overhead—critical for long-context tasks.
- Beyond the 125B main parameters, a 51B N-gram embedding can be offloaded to host memory with asynchronous prefetching, expanding embedding capacity without occupying GPU memory long-term.
- Training uses the Muon optimizer (from Keller Jordan's research), with optimization for orthogonalization precision, parameter partitioning, and fused matrix splitting.
- A scaling law was refit for the new architecture, since laws fitted on older architectures don't transfer.
- Alibaba Qwen official announcement, 2026-08-26 — Hugging Face + ModelScope + Qwen AI platform
- Qianjiang Evening News / Chao News, 2026-08-26
- IT Home / Sina Tech, 2026-08-26
- NetEase Intelligence, 2026-08-26
Alongside it, Alibaba open-sourced Qwen3.8-Flash-Next, whose new architecture is described as the prototype for the Qwen4 generation. The community is invited to validate the structural changes before Alibaba builds the full Qwen4 family on top of them.
The post frames the release around four turning points:
1. The parameter race shifts to an efficiency revolution (large total parameters, small activation). 2. The affordability line is redrawn at 1 CNY per million input tokens. 3. Qwen4's direction is set through open community co-development rather than closed-door research. 4. Qwen Office debuts, with standard mode covering ~95% of everyday office tasks.
MoE Architecture: Big Base, Small Activation
The post emphasizes that 125B total parameters represents a *growth* in knowledge capacity, while the 6B activation keeps per-inference compute comparable to a small model. This is presented as the maturation of sparse Mixture-of-Experts engineering.
The post includes a table of Qwen-series evolution:
| Time | Model | Total | Active | Input price (CNY / 1M tokens) | |---|---|---|---|---| | 2025 Q1 | Qwen 2.5 Max | 720B | 72B | 20 | | 2025 Q4 | Qwen 3 Max | 1.2T | 80B | 15 | | 2026 Q1 | Qwen 3.5 Max | 1.8T | 60B | 8 | | 2026 Q3 | Qwen 3.8 Max | 2.1T | 35B | 5 | | 2026 Q3 | Qwen 3.8-Flash | 125B | 6B | 1 |
Four Core Technical Upgrades
Attention: GDN + QSA Hybrid
Residual: Gated Residual + FP8
Embedding: 51B N-gram Embedding
Optimization: Muon + Refitted Scaling Law
Benchmarks
Per the post, Qwen3.8-Flash claims:
| Benchmark | Claim vs. competitor | |---|---| | SWE-bench Pro (agentic coding) | +9.1 over Opus 4.6 | | CoWorkBench (long-horizon professional tasks) | ahead of DeepSeek-V4-Flash | | Toolathlon Verified (real tool calls) | ahead of DeepSeek-V4-Flash | | JobBench (professional work tasks) | ~+20 over Opus 4.6 | | AndroidWorld (mobile agents) | +22.5 over Opus 4.6 | | MathVision (visual math) | +25.1 over Opus 4.6 | | ERQA (embodied AI) | +31.5 lead |
The post also notes that the pretraining-only Base model reportedly beats the Qwen3.7-Plus Base (3x its size) on general (SuperGPQA), math (GSM8K), and coding (SWE-bench Pretrain) fundamentals.
Pricing: The "1 CNY Era"
| Item | Price | |---|---| | Input | 1 CNY / 1M tokens | | Output | 3 CNY / 1M tokens |
The post's price-war timeline:
| Time | Model | Input price | Change | |---|---|---|---| | 2024 Q3 | Qwen 2 Max | 40 CNY | baseline | | 2025 Q1 | Qwen 2.5 Max | 20 CNY | −50% | | 2025 Q4 | DeepSeek V3 | 2 CNY | −90% | | 2026 Q1 | Qwen 3 Flash | 1.5 CNY | −93% | | 2026 Q3 | Qwen 3.8-Flash | 1 CNY | −97.5% over 18 months |
At ~2,000 tokens per chat input, 1 CNY of input budget covers roughly 500 conversations.
Qwen Office
Qwen3.8-Flash powers the new Qwen Office feature in the Qwen app. Standard mode reportedly covers ~95% of daily office tasks: document summarization and translation, data analysis and reports, PPT outlines, email drafting, meeting minutes, and Excel formulas—scenes characterized as high-frequency, repetitive, and low-risk. Harder tasks can route to Qwen3.8-Max or Pro mode.
Qwen3.8-Flash-Next: The Qwen4 Preview
The open-weight Next architecture inverts the usual closed development cycle: open-source first, community validation, then the full Qwen4 family (targeted for 2026 H2–2027 H1 per the post). Weights are on Hugging Face and ModelScope; the production version runs on the Qwen AI platform with 1M default context and built-in official tools.
As of August 2026, the Qwen3.8 series includes Qwen3.8-Max, Qwen3.8-27B, and Qwen3.8-Flash (2.4T total parameters across the series); Qwen models have surpassed 3 billion downloads with over 300,000 derivative models.
The Chinese Open-Source Matrix (2026 H2)
| Vendor | Series | Notes | |---|---|---| | Alibaba Qwen | Qwen3.8 (Max / 27B / Flash) | Flash at 1 CNY/1M input | | Zhipu | GLM-5.3 (Max / Flash) | Flash open (320B-A18B), priced at 1/40 of Opus | | DeepSeek | V4 (Pro / Flash / Lite) | V4-Pro launched |
Compared internationally, the post contrasts ~30 CNY/1M input for closed models (Claude Opus 4.8/4.6, GPT-5 at ~25) against 1–3 CNY for Chinese open models—described as a separate affordability tier rather than a point on the same curve.
A Flagged Risk: Price Wars vs. R&D
The post cautions that continuous price cuts could compress R&D if revenue growth lags model iteration. Qwen3.8-Flash's answer: architectural upgrades (sparse activation, attention optimization, Muon, refitted scaling law) that expand total parameters while lowering per-inference cost—a sustainable path and an asymmetric strategy against closed-source incumbents.
Outlook (Next 12 Months)
1. Full Qwen4 family on the Next architecture, possibly in Max / Pro / Flash / Lite tiers. 2. Native multimodal expansion toward video understanding/generation and 3D. 3. Deeper agent capabilities, already a differentiator (SWE-bench Pro lead).
Key Facts at a Glance
| Dimension | Value | |---|---| | Release date | 2026-08-26 | | Model | Qwen3.8-Flash (125B / 6B MoE) | | Training cost | 1/9 of Qwen3.7-Plus | | Price | 1 CNY in / 3 CNY out per 1M tokens | | Context | 262K native / 1M via YaRN | | Architecture | QSA + GDN + Gated Residual + N-gram Embedding + Muon | | Benchmarks | SWE-bench Pro +9.1 / JobBench +20 / AndroidWorld +22.5 / MathVision +25.1 / ERQA +31.5 | | Qwen4 preview | Qwen3.8-Flash-Next open weights | | Qwen ecosystem | 3B downloads, 300K+ derivative models |