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Qwen: Qwen3-VL-Plus
qwen/qwen3-vl-plus
The Qwen3 series VL models effectively integrates thinking and non-thinking modes, achieving world-leading performance in visual agent capabilities on public benchmark datasets such as OS World. This version features comprehensive upgrades in areas like visual coding, spatial perception, and multimodal reasoning, significantly enhancing visual perception and recognition abilities, and supporting the understanding of ultra-long videos.
Recent activity on Qwen3-VL-Plus
Tokens processed per day
Thoughput
(tokens/s)
| Providers | Min (tokens/s) | Max (tokens/s) | Avg (tokens/s) |
|---|---|---|---|
| Alibaba Cloud | 23.18 | 34.12 | 26.38 |
First Token Latency
(ms)
| Providers | Min (ms) | Max (ms) | Avg (ms) |
|---|---|---|---|
| Alibaba Cloud | 272 | 671 | 547.33 |
Providers for Qwen3-VL-Plus
ZenMux Provider to the best providers that are able to handle your prompt size and parameters, with fallbacks to maximize uptime.
Latency
0.55
s
Throughput
29.23
tps
Uptime
100.00
%
Recent uptime
Oct 10,2025 - 3 PM100.00%
Price
Tiered pricing
0 <= Input < 32k
Input
$ 0.2
/ M tokens
Output
$ 1.6
/ M tokens
Cache read
$ 0.04
/ M tokens
Cache write 5m
-
Cache write 1h
-
Cache write
-
Web search
-
Model limitation
Context
262.14K
Max output
32.77K
Supported Parameters
max_completion_tokens
temperature
top_p
frequency_penalty
-
presence_penalty
seed
-
logit_bias
-
logprobs
-
top_logprobs
-
response_format
stop
tools
tool_choice
-
parallel_tool_calls
-
Model Protocol Compatibility
openai
anthropic
-
Data policy
Prompt training
false
Prompt Logging
Zero retention
Moderation
Responsibility of developer
Status Page
status page
Sample code and API for Qwen3-VL-Plus
ZenMux normalizes requests and responses across providers for you.
OpenAI-PythonPythonTypeScriptOpenAI-TypeScriptcURL
pythonfrom openai import OpenAI client = OpenAI( base_url="https://zenmux.ai/api/v1", api_key="<ZenMux_API_KEY>", ) completion = client.chat.completions.create( model="qwen/qwen3-vl-plus", messages=[ { "role": "user", "content": [ { "type": "text", "text": "What is in this image?" } ] } ] ) print(completion.choices[0].message.content)