Qwen3-VL
Open Vision-Language Frontier
Alibaba's vision-enabled Qwen 3 family — sizes from 2B (mobile) to 235B-A22B (frontier). Like the text Qwen 3, every variant has a "Thinking" mode that turns on multimodal chain-of-thought reasoning. Apache 2.0 throughout. Qwen2.5-VL remains widely used as the smaller-cost VLM for production deployments.
Intelligence
Below avg
Speed
Slow
46 tok/s output
Cost
High
$0.84 in / $6.18 out
Context
256K
Up to 256,000 tokens
How are Intelligence, Speed & Cost bucketed?
Intelligence and Speed buckets are percentile ranks on
Artificial Analysis. Cost buckets are fixed dollar
thresholds keyed off output-token price ($/M out).
Intelligence
- Top 1%≤ 1%
- Top 5%≤ 5%
- Top 10%≤ 10%
- Good≤ 25%
- Medium≤ 50%
- Below avg> 50%
Speed
- Top 1%≥ 345 tok/s
- Top 5%≥ 237 tok/s
- Top 10%≥ 196 tok/s
- Good≥ 146 tok/s
- Medium≥ 90 tok/s
- Slow< 90 tok/s
Cost
- Freeopen weights · self-host
- Low< $1 / M out
- Moderate$1–5 / M out
- High≥ $5 / M out
Why it matters
Open vision-language at the 235B scale, with Thinking mode for multimodal reasoning — closes the gap with closed multimodal frontier (GPT-4o, Claude 4-series, Gemini 3) for enterprises that require open weights.
Core Capabilities
Long Documents
Handles entire codebases, books, and multi-doc RAG.
Multimodal
Combines text, vision, and audio in one model.
Generative
Produces images, video, audio, or other media.
Agent Workflows
Built for tool use and autonomous tasks.
Context Window
256k tokens
≈ 197+ pages
4k Chat 聊天
32k Long docs 长文档
128k Books 整本书
400k Multi-doc 多文档
1M Codebase 整个代码库
10M
256k
Availability
API
Not available
Product / App
Not available
Open Source
Released
Enterprise
—
Pricing Model
Free / self-host
Open weights — pay only for compute.
Self-host Capability / Performance
Where this model sits relative to the middle 60% of models in the tree. All scores are 0–10 (higher is better).
Lower 20% Upper 80% This model
Context / memory
Context window size · log-scaled
6.0
9.0
7.0
Lower 20% 20th percentile — 20% of models score below this This model Where the current model lands Upper 80% 80th percentile — only 20% of models score above this
Percentile boundaries are computed across every model in the tree that reports the underlying benchmark for each capability.
What it feels like
- Vision-language variant of Qwen 3.
- Best open-source reasoning model at its release — 235B-A22B (Thinking) beats DeepSeek-R1 on 17/23 benchmarks
- Toggle-able thinking mode: same weights serve both reasoning and fast-chat modes
- Strong 119-language coverage; the most genuinely multilingual frontier-tier model
Best use cases
- Multilingual production deployments (119 languages) where most models stay English-centric
- Self-hosted reasoning workflows that need both 'fast mode' and 'thinking mode' from one weight set
- Open-weights agentic coding (Qwen3-Coder) with very large context windows
Tools to try
Not ideal for
- Multimodal tasks (Qwen3 base is text — vision lives in Qwen3-VL, audio in Qwen3-Audio)
- Edge / single-consumer-GPU deployments at the 235B scale