Qwen3 VL 4B Thinking: API Pricing, Context Window & Benchmarks
Qwen3 VL 4B Thinking is a language model from Qwen, released in September 2025, with multimodal input, a 262K-token context window, and pricing from $0.100/M input and $1.00/M output.
Qwen3-VL is a large multimodal model that unifies vision, language, and reasoning to achieve human-level perception and cognition across text, images, and video. Built on a 235B-parameter architecture, it integrates early joint training of
Qwen3 VL 4B Thinking benchmarks
Capability tiers
Standing within each category, adjusted for leaderboard depth.
Real tasks performance
High-confidence performance for Qwen3 VL 4B Thinking across real-world prompt categories. Only 95% intervals at most 4 points wide are shown.
Performance by conversation depth
How Qwen3 VL 4B Thinking holds up as conversations get longer.
Quality Tracker
Qwen3 VL 4B Thinking Performance Across Datasets
Scores sourced from the model's scorecard, paper, or official blog posts
llm-stats.com - Fri Sep 04 2026Qwen3 VL 4B Thinking pricing
Providers
Qwen3 VL 4B Thinking starts at $0.100 per million input tokens and $1.00 per million output tokens via DeepInfra.
| Provider | Input $/M | Cached input $/M | Output $/M | Context in / out | TTFT p95 s | Output p5 c/s | Modalities in / out |
|---|---|---|---|---|---|---|---|
 DeepInfra | $0.100 | — | $1.00 | 262.1K/262.1K | 2.26 | 50 | / |
Cached input is the discounted price for prompt tokens served from a provider cache. TTFT is time to first token. Output is characters per second; p5 is the sustained floor exceeded by 95% of observed requests.
Qwen3 VL 4B Thinking model size
Qwen3 VL 4B Thinking has 4 billion parameters. See how it compares to other models in the same parameter range.
Qwen3 VL 4B Thinking context window
Input and output token limits for Qwen3 VL 4B Thinking, plus how it ranks on long-context understanding.
Qwen3 VL 4B Thinking API
Run a request to see the response
Use it in your code
Billed at $0.10 input / $1.00 output per 1M tokens through the LLM Stats gateway.
from openai import OpenAI
client = OpenAI(
api_key="YOUR_API_KEY",
base_url="https://gateway.llm-stats.com/v1"
)
response = client.chat.completions.create(
model="qwen3-vl-4b-thinking",
messages=[
{"role": "user", "content": "What is machine learning?"}
]
)
print(response.choices[0].message.content)Need an API key? Create one above in the playground, or read the API documentation.
Qwen3 VL 4B Thinking latency
Qwen3 VL 4B Thinking time to first token, sustained output throughput, and failed-request rate from live API traffic over the trailing 7 days.
Provider operational metrics
Time to first token, output throughput, and failed-request rate from live API traffic
Qwen3 VL 4B Thinking examples
Recent arena outputs from Qwen3 VL 4B Thinking, picked from the highest-ranked matchups.
Qwen3 VL 4B Thinking license
Qwen3 VL 4B Thinking is released under the Apache 2.0 license, which permits commercial use, has 4.0B parameters.
- License
- Apache 2.0
- Commercial use allowed
- Parameters
- 4.0B
Apache License 2.0 - allows commercial use
Qwen3 VL 4B Thinking resources
Official sources for Qwen3 VL 4B Thinking: api documentation, official playground, official launch post, source repository, model weights.
Qwen3 VL 4B Thinking vs other models
The most-compared alternatives to Qwen3 VL 4B Thinking are Llama 3.1 Nemotron Ultra 253B v1, Llama 3.1 405B Instruct, GPT-4o. Open any pair side-by-side for benchmarks, pricing, context, and latency.
Models like Qwen3 VL 4B Thinking
Models ranked just above and below Qwen3 VL 4B Thinking by LLM Stats score.
FAQ
Common questions about Qwen3 VL 4B Thinking.