Qwen (Alibaba)

A Tier · 8.8/10

**Qwen-Image-2.1 went open-weight (weights 2026-09-14, blog 2026-09-20)** -- a unified text-to-image and editing model with a 7B visual-generation component, native transparent (RGBA) output, up to 10 reference images and mask-guided local edits -- but under a **research-only licence, not Apache**: commercial use needs a separate licence from Alibaba. Alibaba's open-weights + API family, and August 2026 was its biggest open-release month yet: **Qwen3.8-2.4T-A95B -- the Max-class flagship -- went open-weight on 2026-08-08** (custom Qwen3.8-Max licence), **Qwen3.8-27B** dense landed Apache 2.0 (8/05), and **Qwen3.8-Flash-Next** (8/24 weights, 8/26 blog) previews the Qwen4 architecture: 125B main + 51B n-gram embeddings with only 6B active, served as Qwen3.8-Flash at $0.15/$0.47 per 1M. Qwen 3.7 Max remains the GA API flagship ($2.50/$7.50)

Last updated: 2026-10-12Free tier available

Score Breakdown

7.0

Ease of Use

9.0

Output Quality

10.0

Value

9.0

Features

Benchmark Scores

Benchmarks for Qwen3.5-397B MoE

Benchmark	Description	Score
MMLU-Pro	Harder multi-subject reasoning	83.5%
GPQA Diamond	Graduate-level science questions	78.2%
AIME 2025		87%
HumanEval	Python code generation	92.5%
SWE-Bench Verified		69.4%

Last updated: 2026-04-13

Visit Qwen (Alibaba)

Personality & Tone

The multilingual Alibaba all-rounder

Tone: Helpful, verbose, and notably strong in Chinese and other non-English languages. Qwen is chattier than Mistral or DeepSeek and tends toward structured, multi-section replies.

Quirks: Best-in-class at Chinese -- occasionally switches to Mandarin mid-response for technical or cultural topics even when prompted in English. Political refusal patterns mirror other Chinese models on China-specific topics.

The Good and the Bad

What we like

+Qwen 3.6-Plus (launched Mar 30 2026) is Alibaba's answer to Claude Opus 4.5 on agentic coding -- native 1M context, always-on CoT reasoning, and tool-use baked into the base model. Matches Claude on SWE-bench per Alibaba Cloud's published benchmarks
+Qwen3.5 Small (0.8B / 2B / 4B / 9B) is the most capable sub-10B open-weight family in 2026 -- the 2B runs on iPhone in airplane mode, the 9B matches 120B-class models on reasoning benchmarks. Apache 2.0 on all of it
+Qwen3.5-Omni (Mar 30 2026) is the first native multimodal open-weight model with realtime text/audio/video inputs and outputs in one unified model -- displaces needing 3 separate models for voice + vision + text
+Full modality lineup Apache 2.0: text (Qwen3, 3.5), vision (Qwen3-VL), coder (Qwen3-Coder-Next), reasoning (Qwen3-Thinking), omni-multimodal (Qwen3.5-Omni)
+Qwen3-Coder-Next 80B-A3B runs on 8 GB VRAM and still posts top-tier coding benchmarks (sparse MoE activates only ~3B params)
+Massive ecosystem: Ollama, llama.cpp, vLLM, LM Studio, Hugging Face all ship first-class Qwen quants within days of release

What could be better

−Qwen3-Max flagship is API-only -- you can't self-host the best Alibaba model
−Censorship on politically sensitive topics (PRC regulations apply)
−English writing style occasionally stilted compared to Claude or Mistral
−Rapid release cadence means model names (Qwen3, Qwen3.5, Qwen3-Next, Qwen3-Max-Thinking) are confusing

Pricing

Self-hosted (Free)

✓Apache 2.0 license on open weights
✓Available on Hugging Face, ModelScope, Ollama
✓Fine-tuning fully permitted

API (OpenRouter / Alibaba Cloud)

$0.12/per 1M input tokens

✓Qwen3-Coder-Next 80B-A3B: $0.12 in / $0.60 out
✓Qwen3.5-397B: $0.40 in / $2.40 out
✓Qwen3-Max (API only): $0.78 in / $6.00 out

API (Qwen 3.6-Plus flagship, Mar 30 2026)

$0.90/per 1M input tokens

✓Native 1M context window
✓Always-on chain-of-thought reasoning
✓Agentic tool-use baked in (matches Claude Opus 4.5 on SWE-bench per Alibaba Cloud)
✓API-only (weights not released)

API (Qwen 3.7 Max flagship, May 20 2026 -- CLOSED WEIGHTS)

$2.50 / $7.50/per 1M tokens input/output (50% launch promo $1.25/$3.75 until 2026-06-22)

✓Vendor benchmarks: SWE-Bench Pro 60.6%, Terminal-Bench 2.0-Terminus 69.7%, GPQA 92.4%
✓PROPRIETARY / API-only on DashScope + Model Studio
✓Qwen3.7-Plus sibling (Jun 2): multimodal image/video input at $0.40/$1.60 per 1M

API (Qwen 3.6-Max-Preview, Apr 20 2026 -- CLOSED WEIGHTS)

TBD/per 1M tokens

✓#1 on SWE-bench Pro, Terminal-Bench 2.0, SciCode, QwenClawBench, QwenWebBench
✓256K context window (text-only at launch, no vision)
✓OpenAI- and Anthropic-API-compatible endpoints
✓Alibaba's FIRST flagship shipped without open weights -- proprietary only
✓Available via Qwen Studio + Alibaba Cloud Model Studio

Self-hosted (Qwen3.6-27B dense, Apr 22 2026)

✓Apache 2.0 open weights -- no commercial restrictions
✓DENSE 27B (not MoE) -- all params active, simpler to deploy than sparse-MoE flagships
✓Multimodal: text + image + video, 262K native context (extensible to ~1M)
✓Runs on a single consumer-tier GPU (RTX 4090 or equivalent at FP16; smaller VRAM at quant)
✓BEATS Qwen3.5-397B-A17B MoE flagship on coding while being ~15x smaller in active params

System Requirements

Hardware needed to self-host. Min = smallest viable setup (usually heavy quantization). Max = full-precision / production-grade.

Model variant	Min	Max
Qwen3-Coder-Next 80B-A3B (sparse MoE)	8 GB VRAM Q4 (RTX 3060)	1× A100 80 GB FP16
Qwen3.5 (397B MoE flagship)	128 GB RAM + 24 GB GPU (Q3)	4× H100 FP8
Qwen3-VL (vision flagship)	24 GB VRAM (Q4)	1× H100 FP16
Qwen3-Max	API-only -- weights not released	API-only -- weights not released
Qwen3.6-35B-A3B (sparse MoE, vision-language, Apr 16 2026)Apache 2.0 open weights. SWE-bench 73.4% -- current best for open-weights coding.	8 GB VRAM Q4 (RTX 3060 tier; ~3B active params keep inference cheap)	1x A100 80 GB FP16 or 2x RTX 4090 for 262K+ context

Known Issues

QWEN-IMAGE-2.1-TURBO -- AN 8-STEP CHECKPOINT, SAME RESEARCH-ONLY LICENCE (Hugging Face repo created 2026-10-09, vendor-primary model card): 'Qwen-Image-2.1-Turbo is an accelerated checkpoint of Qwen-Image-2.1 for text-to-image generation and image editing with 8 denoising steps. It uses the same 7B visual generation architecture and loads directly with QwenImage21Pipeline in Diffusers.' It ships its own sampling schedule, uses CFG=1 by default and reuses text and reference-image context across steps with prefix KV caching. **Licence front matter: license_name qwen-research** -- the same non-commercial research licence as Qwen-Image-2.1, so the speed-up does not change what you may build on it. Requires a Diffusers build from source (PR #14950). No speed or quality numbers versus the full-step model are stated in the card's opening sections.Source: Hugging Face model card Qwen/Qwen-Image-2.1-Turbo (huggingface.co/Qwen/Qwen-Image-2.1-Turbo/raw/main/README.md; createdAt 2026-10-09 from the HF API) -- fetched 2026-10-12 · 2026-10-09
QWEN-IMAGE-2.1 IS OPEN-WEIGHT -- BUT UNDER A RESEARCH-ONLY LICENCE, WHICH IS THE FIRST THING TO CHECK BEFORE YOU BUILD ON IT (weights 2026-09-14, blog and licence 2026-09-20; vendor-primary via Hugging Face and Qwen's blog): Alibaba published **Qwen-Image-2.1**, 'a unified text-to-image generation and image editing model' whose visual generation component is **7B parameters across 32 Single-Stream DiT layers**, with a mixed-granularity attention scheme (token-level causal mask for text, chunk-level for images) and **prefix KV-cache reuse** so reference images and editing instructions are computed once and cached. **What is new in capability terms:** (1) **native transparency** -- it generates regular or RGBA images from a prompt, edits transparent layers, and extracts a subject from a photograph as an RGBA layer, folding the December 2025 Qwen-Image-Layered model into the main line; (2) **up to 10 reference images** in one edit (group portraits from six headshots, a full outfit from five product shots, a room from ten furnishings); (3) **local editing by circles, painted annotations, or a separate mask** passed alongside the untouched original; (4) identity and product-fidelity preservation, panoramas, infographics, storyboards, and improved typography and portrait lighting. Output up to 2048x2048, with preset ratios to 2752x1536. Two companion **prompt-rewriting models (Qwen-Image-2.1-PE-T2I and PE-I2I, 2026-09-20)**, fine-tuned from Qwen3.5-VL 9B, turn short requests in any language into detailed English prompts with a recommended aspect ratio. **BENCHMARKS: chart only.** The blog shows a 'Qwen-Image-Bench' comparison against open and closed models as an image with no table, so no scores are printed here. **THE LICENCE, VERBATIM FROM THE REPO:** the model ships under the **Qwen RESEARCH LICENSE AGREEMENT (release date September 20, 2026)**, which grants rights 'FOR NON-COMMERCIAL PURPOSES ONLY', defines Non-Commercial as 'for research or evaluation purposes only', and says 'You shall not use the Materials for any commercial purpose without obtaining a separate commercial license from us' (model-business@notice.qwencloud.com). **That is a departure from the Apache-2.0 image releases this page praised in 2025 and a different posture from Qwen3.8-27B (Apache 2.0)** -- so 'open-source', the word Qwen uses in its own headline, means open for research here, not open for products. **VERSIONING TRAP:** this page recorded a **Qwen-Image-3.0 (2026-07-21, Qwen Chat only, no weights)**; the open release carries the lower number 2.1. Treat 2.1 as the open-weight line and 3.0 as the hosted flagship rather than reading 2.1 as a downgrade or a successor. Early traction as of 2026-09-21: about 6,500 downloads and 1,400 likes on the Hugging Face repo, one day after the blog post.Source: Hugging Face model card Qwen/Qwen-Image-2.1 (repo created 2026-09-14, license qwen-research; README with architecture and quick-start), LICENSE file in the same repo ('Qwen RESEARCH LICENSE AGREEMENT Release Date: September 20, 2026'), Qwen/Qwen-Image-2.1-PE-T2I and PE-I2I model cards (created 2026-09-20), Qwen blog 'Qwen-Image-2.1: Compact, Efficient, and Unified Image Creation' (qwen.ai/blog?id=qwen-image-2.1, dated 2026/09/20, rendered via Firecrawl), HF API downloads/likes -- all fetched 2026-09-21 · 2026-09-20
STALENESS CATCH-UP -- THREE QWEN3.8 OPEN-WEIGHT RELEASES IN AUGUST, INCLUDING THE FIRST OPEN MAX-CLASS MODEL, AND THE QWEN4 ARCHITECTURE PREVIEW (2026-08-05, 2026-08-08 and 2026-08-24/26; vendor-primary via Qwen's blog and Qwen's own Hugging Face model cards; this page was last reviewed 2026-07-22 and had only the WAIC preview): **(1) Qwen3.8-27B (weights on HF 2026-08-05, Apache 2.0)** -- the dense successor to the Qwen3.6-27B this page already recommends for single-GPU self-hosting; FP8 build followed 8/13. **(2) Qwen3.8-2.4T-A95B (weights on HF 2026-08-08, custom 'qwen3.8-max' licence, NOT Apache)** -- 'For the first time, Qwen3.8 brings a **Qwen-Max-class model to open release**': a 2.4T-parameter MoE with 95B active, the open counterpart of the hosted **Qwen3.8-Max** (which adds vision input, non-thinking mode, 1M context by default and built-in tools on QwenCloud). Reasoning depth via `reasoning_effort`; `preserve_thinking` keeps prior reasoning context. **The licence is the catch:** it is a bespoke Qwen3.8-Max licence rather than Apache 2.0, so read it before commercial deployment -- this is the same pattern as Kimi's K3 licence. **(3) Qwen3.8-Flash-Next (weights 2026-08-24, blog 2026-08-26, 'Qwen Community 1.0' licence)** -- 'a multimodal MoE model that also serves as an **early preview of the architecture used in Qwen4**', playing the role Qwen3-Next played for Qwen3.5. Four architectural changes: **Gated DeltaNet + Qwen Sparse Attention (QSA)** hybrid, **Gated Residual** (4-branch residual stream with dynamic gates), **N-gram Embedding** (a 51B lookup table that can be offloaded to host memory), and the **Muon optimizer**. Shape: **125B main model + 51B n-gram embeddings, 6B active per token**; 262,144 native context, 1M with YaRN. Qwen says training cost about **1/9 of Qwen3.7-Plus** while beating it on coding and office tasks. **Vendor benchmarks vs Qwen3.8-27B / Qwen3.7-Plus / DeepSeek-V4-Flash-0731:** DeepSWE 1.1 **58.7** / 42.2 / 16.5 / 54.4; SWE-bench Pro **62.5** / 61.7 / 55.8 / 56.0; SWE-bench Multilingual **81.0**; NL2Repo 48.1 (DeepSeek 54.2 leads); CoWorkBench **73.9**; JobBench **55.7**; Toolathlon Verified **73.5**; IFBench 81.3. All first-party. **Hosted pricing:** the production version, **Qwen3.8-Flash** on QwenCloud (1M context default, built-in tools), is **$0.15 per 1M input / $0.47 per 1M output** -- undercutting DeepSeek's new V4.1-Flash on output ($0.60 off-peak) and matching it on input. **WHY THIS PAGE MISSED IT:** qwen.ai/blog is client-rendered and returns nothing to plain fetches; the fix is to enumerate the Qwen org on Hugging Face by createdAt, which is how all three were found. **ALSO ON HF:** Qwen-Drive-1.0-4B (8/27), an autonomous-driving model outside this page's scope.Source: Qwen blog (qwen.ai/blog?id=qwen3.8-flash-next, dated '2026/08/26', rendered via Firecrawl); Hugging Face model cards Qwen/Qwen3.8-Flash-Next (created 2026-08-24, license qwen-community-1.0), Qwen/Qwen3.8-2.4T-A95B (created 2026-08-08, license qwen3.8-max), Qwen/Qwen3.8-27B (created 2026-08-05, apache-2.0) -- all fetched 2026-09-14 · 2026-08-26
FLAGSHIP PREVIEW -- QWEN3.8-MAX (2026-07-19 at WAIC Shanghai, press-reported): Alibaba previewed **Qwen3.8-Max**, a **2.4-trillion-parameter sparse-MoE multimodal model** (text/image/video/docs), days after Moonshot's Kimi K3 open-weights launch. Alibaba positions it as 'second only to [Claude] Fable 5.' Access at preview is CLOSED: via Alibaba's Token Plan subscription plus the Qoder / QoderWork agentic platforms, at roughly 10% of standard pricing; **open weights 'promised soon' with no date or license, and no model card, benchmark table, or third-party eval yet** -- treat the ranking claim as unverified vendor positioning. SEPARATELY (2026-07-21) the Qwen team released **Qwen-Image-3.0**, a 3rd-gen image model (up to ~4,500-token prompts, legible text down to ~10px, 12 languages, UI/web + live-data rendering) -- but a transparency regression: **Qwen Chat access only, no open weights, no benchmarks, no technical report** (a departure from Qwen's prior Apache-2.0 same-day-weights image releases)Source: MarkTechPost (2026-07-19, Qwen3.8-Max preview), Unite.ai (2026-07-21, Qwen-Image-3.0) · 2026-07-19
USER-CREATED AI AGENTS SHUT DOWN -- TOOK EFFECT 2026-07-15 (confirmed): Alibaba's Qwen app (and ByteDance's Doubao, simultaneously) **shut down user-created/custom AI agent features on July 15, 2026** as China's rules on 'humanlike AI interaction services' (the anthropomorphic-AI/companion rules) took effect that day; Qwen had already disabled humanlike interactive agents ~7/10 ahead of the broader cutoff. IMPORTANT DATA DETAIL: the platforms diverge -- **Doubao** offers read-only access to old agent data until **October 15, 2026** (unrecoverable after), but **Qwen offers no migration path at all**: agent configurations and conversation histories are permanently deleted. This is a consumer-app change -- the Qwen models/API are unaffected. Separately (SCMP, early July, press-reported): Alibaba cut Qwen3.7-Max by ~80% and Qwen3.7-Plus by ~60% for international users on the Qoder platform during off-peak hours (10pm-8am Beijing = US working hours) -- an explicit play for US developer demand amid a broader Chinese token price warSource: SCMP (scmp.com/tech/big-tech/article/3359482), TechTimes (2026-07-15, effective-date confirmation), Bloomberg (2026-07-06), TechNode · 2026-07-15
FLAGSHIP GA + VENDOR NUMBERS CONFIRMED (verified 2026-06-09): **Qwen 3.7 Max** shipped 2026-05-20 ('Qwen3.7: The Agent Frontier' on the official Qwen blog) -- the benchmark figures that circulated in aggregators are now vendor-published: **SWE-Bench Pro 60.6%, Terminal-Bench 2.0-Terminus 69.7%, GPQA 92.4%**, plus SWE-Multilingual 78.3%, SciCode 53.5%, QwenSVG 1608. PROPRIETARY / API-only on DashScope/Model Studio. Official pricing: **$2.50/M input, $7.50/M output list -- with a 50% launch promo ($1.25/$3.75) running until 2026-06-22**. Separately, **Qwen3.7-Plus** launched ~2026-06-02 on Bailian/Model Studio: multimodal (image + video input, text output) with GUI-agent, deep reasoning, and tool invocation at $0.40/$1.60 per 1M -- press-reported agent scores (ScreenSpot Pro 79.0, AndroidWorld 81.0) await vendor tables. Both models confirmed on Alibaba's official Model Studio model listSource: Qwen blog (qwen.ai/blog?id=qwen3.7), Alibaba Cloud pricing campaign (alibabacloud.com/campaign/qwen-discount), Alibaba Model Studio model list (help.aliyun.com/zh/model-studio/models), VentureBeat (Qwen3.7-Plus) · 2026-06-09
INTERPRETABILITY RELEASE (2026-05-01): Alibaba released **Qwen-Scope** -- an open-source suite of **sparse autoencoder (SAE) weights** for the Qwen3 and Qwen3.5 model families, plus inference + visualization tooling. SAEs decompose internal model activations into interpretable feature directions (Anthropic / Google DeepMind have been the highest-profile labs publishing this style of interpretability work; Alibaba is the first major Chinese lab to ship SAE weights openly). Practical implication: researchers and red-teamers can now probe Qwen3.5 internals (feature attribution, concept steering, refusal-circuit analysis) without training their own SAEs -- a multi-month / multi-GPU effort. Adds to Qwen's open-weights story even as the Qwen 3.6 flagship line goes closed-weights. Materially raises the floor for safety / alignment research on Chinese open-weight families.Source: Qwen blog (qwen.ai/blog?id=qwen-scope) · 2026-05-01
Qwen3.6-27B dropped 2026-04-22 (~48h after Max-Preview) as the new open-weights coding champion. DENSE 27B, NOT MoE -- all params active, simpler to deploy. Apache 2.0 open weights with no commercial restrictions. Multimodal: text + image + video. 262K native context (extensible to ~1M). Verified benchmarks: SWE-bench Verified 77.2%, SWE-bench Pro 53.5%, Terminal-Bench 2.0 59.3% (matches Claude Opus 4.5 exactly), MMLU-Pro 86.2%, GPQA Diamond 87.8%, AIME 2026 94.1%, MMMU-Pro 75.8%. Notably BEATS the Qwen3.5-397B-A17B MoE flagship on coding from a single consumer GPU -- this displaces the 35B-A3B as the open-weights centerpiece for most users. Quants from unsloth + mlx-community shipped same weekSource: qwen.ai/blog?id=qwen3.6-27b, HuggingFace Qwen/Qwen3.6-27B, implicator.ai analysis · 2026-04-22
Qwen 3.6-Max-Preview launched 2026-04-20 as Alibaba's first flagship shipped WITHOUT open weights -- #1 across SWE-bench Pro, Terminal-Bench 2.0, SciCode, QwenClawBench, and QwenWebBench. 256K context, text-only (no vision at launch), APIs compatible with OpenAI + Anthropic SDKs for easy swap-in. Signals Alibaba's pivot toward closed flagships while keeping mid-size models open. If you picked Qwen for the 'best open weights' story, the best model is no longer open -- but the 3.6-27B Apache 2.0 dense released 48 hours later mostly closes that complaint for non-frontier coding workloadsSource: Decrypt, CNTechPost · 2026-04
CRITICAL (2026-04-15): Qwen Code free OAuth tier discontinued. Alibaba throttled from 1,000 -> 100 free requests/day before fully cutting it. To keep using Qwen Code you now need an Alibaba Cloud Coding Plan (~$50/mo), BYOK via OpenRouter / Fireworks / Cerebras, or local inference on your own hardware via Ollama/vLLM. The 'free open-source AI' framing no longer applies to hosted access -- only to self-hosted deploymentSource: Decrypt, GitHub QwenLM/qwen-code issues #3316 · 2026-04
Qwen3.6-35B-A3B open-weights release on 2026-04-16 is the open-model story of April. Sparse MoE with 35B total / 3B active, vision-language multimodal, Apache 2.0, 262K native context (extensible to ~1M). Benchmarks are best-in-class for open weights: SWE-bench Verified 73.4% (vs Gemma 4-31B 52%), AIME 2026 92.7, GPQA Diamond 86.0, MMMU 81.7 (beats Claude Sonnet 4.5 at 79.6), Terminal-Bench 2.0 51.5%. Simon Willison's pelican-drawing test favored Qwen3.6-35B-A3B over Claude Opus 4.7 -- a high-signal community endorsementSource: Hugging Face Qwen3.6-35B-A3B, Alibaba Cloud announcement, Simon Willison, MarkTechPost, buildfastwithai review · 2026-04
Qwen3-Max and now Qwen 3.6-Max-Preview are both API-only -- the pattern suggests Alibaba has quietly adopted closed-weights for flagships while keeping sub-flagship sizes Apache 2.0. Confusing for users expecting Alibaba's best model to be self-hostableSource: Reddit r/LocalLLaMA, Decrypt · 2026-04
Refuses discussion of Tiananmen, Taiwan sovereignty, Xi Jinping -- same PRC content filters as DeepSeekSource: Hugging Face discussions · 2026-01

Best for

Developers who want frontier-tier open weights with Apache 2.0 licensing. Qwen3-Coder-Next is arguably the best local coding model; Qwen3.5-397B is a top-3 open generalist.

Not for

Teams that need the Qwen3-Max flagship self-hostable (it's API-only), or use cases that touch Chinese-government-sensitive topics.

Our Verdict

Qwen is the most complete open-weights family in 2026. Alibaba ships Apache-2.0 weights across text, coding, vision, and reasoning -- every modality has a top-tier entry. Qwen3-Coder-Next is a standout: 3B active params but competitive with Claude Sonnet on coding. The catch is that Qwen3-Max, the absolute flagship, stays closed. If you can live with the PRC content filters and want the best open-weights ecosystem, Qwen is the S-tier pick.

Sources

Hugging Face: Qwen/Qwen-Image-2.1-Turbo model card -- 8-step checkpoint, qwen-research licence (created 2026-10-09) (accessed 2026-10-12)
Hugging Face: Qwen/Qwen-Image-2.1 model card -- 7B visual generation component, RGBA, 10 reference images (created 2026-09-14) (accessed 2026-09-21)
Hugging Face: Qwen/Qwen-Image-2.1 LICENSE -- Qwen Research License Agreement, non-commercial only (release date 2026-09-20) (accessed 2026-09-21)
Qwen blog: Qwen-Image-2.1: Compact, Efficient, and Unified Image Creation (2026-09-20) (accessed 2026-09-21)
Hugging Face: Qwen/Qwen-Image-2.1-PE-T2I prompt-rewriting model (created 2026-09-20) (accessed 2026-09-21)
Qwen blog: Qwen3.8-Flash-Next -- A New Architecture, Towards Ultimate Cost-Efficiency (2026-08-26): Qwen4 architecture preview, benchmarks, Qwen3.8-Flash at $0.15/$0.47 (accessed 2026-09-14)
Hugging Face: Qwen/Qwen3.8-Flash-Next model card (created 2026-08-24, Qwen Community 1.0 licence) (accessed 2026-09-14)
Hugging Face: Qwen/Qwen3.8-2.4T-A95B -- open Max-class weights (created 2026-08-08, custom Qwen3.8-Max licence) (accessed 2026-09-14)
Hugging Face: Qwen/Qwen3.8-27B (created 2026-08-05, Apache 2.0) (accessed 2026-09-14)
MarkTechPost: Alibaba previews Qwen3.8-Max (2.4T multimodal, 2026-07-19) (accessed 2026-07-22)
Unite.ai: Alibaba launches Qwen-Image-3.0 without benchmarks or weights (2026-07-21) (accessed 2026-07-22)
Qwen blog: Qwen3.7 -- The Agent Frontier (vendor benchmarks) (accessed 2026-06-09)
Alibaba Cloud: Qwen 3.7 Max pricing + launch discount (accessed 2026-06-09)
Alibaba Model Studio model list (qwen3.7-max + qwen3.7-plus) (accessed 2026-06-09)
Decrypt: Alibaba Qwen 3.7 Max preview review (2026-05) (accessed 2026-05-26)
Qwen blog: Qwen3.6-27B dense Apache 2.0 (2026-04-22) (accessed 2026-04-27)
HuggingFace Qwen/Qwen3.6-27B (accessed 2026-04-27)
Implicator.ai: Qwen3.6-27B beats 397B MoE on coding (accessed 2026-04-27)
Decrypt: Qwen 3.6-Max-Preview launch (accessed 2026-04-21)
CNTechPost: Qwen3.6-Max-Preview release notes (accessed 2026-04-21)
Decrypt: Qwen Code free tier shutdown (accessed 2026-04-21)
Qwen blog: Qwen 3.6-Plus towards real-world agents (accessed 2026-04-17)
Alibaba Cloud: Qwen 3.6-Plus for agentic AI (accessed 2026-04-17)
Hugging Face Qwen collection (accessed 2026-04-17)
OpenRouter pricing (accessed 2026-04-17)

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MMLU's harder successor: 10 answer choices and more reasoning.

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Graduate-level physics, biology, and chemistry written to defeat Google-search.

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Alternatives to Qwen (Alibaba)

Llama 4 (Meta)

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Qwen (Alibaba)

Score Breakdown

Benchmark Scores

Personality & Tone

The Good and the Bad

What we like

What could be better

Pricing

Self-hosted (Free)

API (OpenRouter / Alibaba Cloud)

API (Qwen 3.6-Plus flagship, Mar 30 2026)

API (Qwen 3.7 Max flagship, May 20 2026 -- CLOSED WEIGHTS)

API (Qwen 3.6-Max-Preview, Apr 20 2026 -- CLOSED WEIGHTS)

Self-hosted (Qwen3.6-27B dense, Apr 22 2026)

System Requirements

Known Issues

Best for

Not for

Our Verdict

Sources

Explore more Qwen (Alibaba) rankings

The Tier List Tuesday

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