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Alibaba has launched a newer model, Qwen3.5 4B, we suggest considering this model instead.

For more information, see Comparison of Qwen3.5 4B to other models and API provider benchmarks for Qwen3.5 4B.

Qwen3 VL 4B Instruct logo

Qwen3 VL 4B Instruct Intelligence, Performance & Price Analysis

Alibaba

Open weights model

Released October 2025

Model summary

Intelligence

Artificial Analysis Intelligence Index

10
3 out of 4 units for Intelligence.

Speed

Output tokens per second

N/A
Unknown out of 4 units for Speed.

Input Price

USD per 1M tokens

$0.00
1 out of 4 units for Input Price.

Output Price

USD per 1M tokens

$0.00
1 out of 4 units for Output Price.

Verbosity

Output tokens from Intelligence Index

37M
4 out of 4 units for Verbosity.

Metrics are compared against models of the same class:

  • Non-reasoning models → compared only with other non-reasoning models
  • Reasoning models → compared across both reasoning and non-reasoning
  • Open weights models → compared only with other open weights models of the same size class:
    • Tiny: ≤4B parameters
    • Small: 4B–40B parameters
    • Medium: 40B–150B parameters
    • Large: >150B parameters
  • Proprietary models → compared across proprietary and open weights models of the same price range, using a blended 3:1 input/output price ratio:
    • <$0.15 per 1M tokens
    • $0.15–$1 per 1M tokens
    • >$1 per 1M tokens
Technical specifications
ReasoningNo

This page shows the non-reasoning version of this model.

A reasoning variant may also exist.

Input modality

Supports: text, image

Output modality

Supports: text

Context window256k
~384 A4 pages of size 12 Arial font
Total parameters4.4B
LicenseApache 2.0
Model weightsHugging Face

Qwen3 VL 4B Instruct is above average in intelligence and well priced when comparing to other open weight non-reasoning models of similar size. The model supports text and image input, outputs text, and has a 256k tokens context window.

Qwen3 VL 4B Instruct scores 10 on the Artificial Analysis Intelligence Index, placing it above average among comparable models (averaging 8). When evaluating the Intelligence Index, it generated 37M tokens, which is very verbose in comparison to the average of 5.7M.

Pricing for Qwen3 VL 4B Instruct is $0.00 per 1M input tokens (competitively priced, average: $0.00) and $0.00 per 1M output tokens (competitively priced, average: $0.00). In total, it cost $0.00 to evaluate Qwen3 VL 4B Instruct on the Intelligence Index.

Intelligence
Artificial Analysis Intelligence Index; Higher is better
Speed
Output Tokens per Second; Higher is better
Price
USD per 1M Tokens; Lower is better

Navigation

IntelligenceOpennessIntelligence Index ComparisonsIntelligence Index Token Use & CostContext WindowPricingPerformance SummarySpeedLatencyEnd-to-End Response TimeModel Size (Open Weights Models Only)Comparisons

Intelligence

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v4.0 incorporates 10 evaluations: GDPval-AA, 𝜏²-Bench Telecom, Terminal-Bench Hard, SciCode, AA-LCR, AA-Omniscience, IFBench, Humanity's Last Exam, GPQA Diamond, CritPt
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Reasoning models are indicated by a lightbulb icon.

Artificial Analysis Intelligence Index v4.0 includes: GDPval-AA, 𝜏²-Bench Telecom, Terminal-Bench Hard, SciCode, AA-LCR, AA-Omniscience, IFBench, Humanity's Last Exam, GPQA Diamond, CritPt. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Artificial Analysis Intelligence Index by Open Weights / Proprietary

Artificial Analysis Intelligence Index v4.0 incorporates 10 evaluations: GDPval-AA, 𝜏²-Bench Telecom, Terminal-Bench Hard, SciCode, AA-LCR, AA-Omniscience, IFBench, Humanity's Last Exam, GPQA Diamond, CritPt
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Proprietary
Open Weights
Open Weights (Commercial Use Restricted)
Reasoning models are indicated by a lightbulb icon.

Artificial Analysis Intelligence Index v4.0 includes: GDPval-AA, 𝜏²-Bench Telecom, Terminal-Bench Hard, SciCode, AA-LCR, AA-Omniscience, IFBench, Humanity's Last Exam, GPQA Diamond, CritPt. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Indicates whether the model weights are available. Models are labelled as 'Commercial Use Restricted' if the weights are available but commercial use is limited (typically requires obtaining a paid license).

Intelligence Evaluations

Intelligence evaluations measured independently by Artificial Analysis; Higher is better
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Results claimed by AI Lab (not yet independently verified)
GDPval-AA (Agentic Real-World Work Tasks, (ELO-500)/2000)
Terminal-Bench Hard (Agentic Coding & Terminal Use)
𝜏²-Bench Telecom (Agentic Tool Use)
AA-LCR (Long Context Reasoning)
AA-Omniscience Accuracy (Knowledge)
AA-Omniscience Non-Hallucination Rate (1 - Hallucination Rate)
Humanity's Last Exam (Reasoning & Knowledge)
GPQA Diamond (Scientific Reasoning)
SciCode (Coding)
IFBench (Instruction Following)
CritPt (Physics Reasoning)
MMMU Pro (Visual Reasoning)
Reasoning models are indicated by a lightbulb icon.

While model intelligence generally translates across use cases, specific evaluations may be more relevant for certain use cases.

Artificial Analysis Intelligence Index v4.0 includes: GDPval-AA, 𝜏²-Bench Telecom, Terminal-Bench Hard, SciCode, AA-LCR, AA-Omniscience, IFBench, Humanity's Last Exam, GPQA Diamond, CritPt. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Openness

Artificial Analysis Openness Index: Results

Openness Index assesses model openness on a 0 to 100 normalized scale (higher is more open)
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Intelligence Index Comparisons

Intelligence vs. Price

Artificial Analysis Intelligence Index; Price: USD per 1M Tokens
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Most attractive quadrant
Alibaba
Anthropic
DeepSeek
Google
Meta
Mistral
NVIDIA
OpenAI
xAI
Z AI
Reasoning models are indicated by a lightbulb icon.

While higher intelligence models are typically more expensive, they do not all follow the same price-quality curve.

Artificial Analysis Intelligence Index v4.0 includes: GDPval-AA, 𝜏²-Bench Telecom, Terminal-Bench Hard, SciCode, AA-LCR, AA-Omniscience, IFBench, Humanity's Last Exam, GPQA Diamond, CritPt. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Price per token, represented as USD per million Tokens. Price is a blend of Input & Output token prices (3:1 ratio).

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

Intelligence Index Token Use & Cost

Output Tokens Used to Run Artificial Analysis Intelligence Index

Tokens used to run all evaluations in the Artificial Analysis Intelligence Index
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Reasoning Tokens
Answer Tokens
Reasoning models are indicated by a lightbulb icon.

The number of tokens required to run all evaluations in the Artificial Analysis Intelligence Index (excluding repeats).

Cost to Run Artificial Analysis Intelligence Index

Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index
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Input Cost
Reasoning Cost
Output Cost
Reasoning models are indicated by a lightbulb icon.

The cost to run the evaluations in the Artificial Analysis Intelligence Index, calculated using the model's input and output token pricing and the number of tokens used across evaluations (excluding repeats).

Context Window

Context Window

Context Window: Tokens Limit; Higher is better
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Reasoning models are indicated by a lightbulb icon.

Larger context windows are relevant to RAG (Retrieval Augmented Generation) LLM workflows which typically involve reasoning and information retrieval of large amounts of data.

Maximum number of combined input & output tokens. Output tokens commonly have a significantly lower limit (varied by model).

Pricing

Pricing: Input and Output Prices

Price: USD per 1M Tokens
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Input price
Output price
Reasoning models are indicated by a lightbulb icon.

Price per token included in the request/message sent to the API, represented as USD per million Tokens.

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

Intelligence vs. Price (Log Scale)

Artificial Analysis Intelligence Index; Price: USD per 1M Tokens; Inspired by prior analysis by Swyx
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Most attractive quadrant
Alibaba
Anthropic
DeepSeek
Google
Meta
Mistral
NVIDIA
OpenAI
xAI
Z AI
Reasoning models are indicated by a lightbulb icon.

While higher intelligence models are typically more expensive, they do not all follow the same price-quality curve.

Artificial Analysis Intelligence Index v4.0 includes: GDPval-AA, 𝜏²-Bench Telecom, Terminal-Bench Hard, SciCode, AA-LCR, AA-Omniscience, IFBench, Humanity's Last Exam, GPQA Diamond, CritPt. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Price per token, represented as USD per million Tokens. Price is a blend of Input & Output token prices (3:1 ratio).

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

 Pricing Comparison of Qwen3 VL 4B Instruct API Providers

Speed

Measured by Output Speed (tokens per second)

Output Speed

Output Tokens per Second; Higher is better
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Reasoning models are indicated by a lightbulb icon.

Tokens per second received while the model is generating tokens (ie. after first chunk has been received from the API for models which support streaming).

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

Output Speed vs. Price

Output Speed: Output Tokens per Second; Price: USD per 1M Tokens
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Most attractive quadrant
Alibaba
Anthropic
DeepSeek
Google
Meta
Mistral
NVIDIA
OpenAI
xAI
Z AI
Reasoning models are indicated by a lightbulb icon.

Tokens per second received while the model is generating tokens (ie. after first chunk has been received from the API for models which support streaming).

Price per token, represented as USD per million Tokens. Price is a blend of Input & Output token prices (3:1 ratio).

Latency

Measured by Time (seconds) to First Token

Latency: Time To First Answer Token

Seconds to First Answer Token Received; Accounts for Reasoning Model 'Thinking' time
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Input processing
Thinking (reasoning models, when applicable)
Reasoning models are indicated by a lightbulb icon.

Time to first answer token received, in seconds, after API request sent. For reasoning models, this includes the 'thinking' time of the model before providing an answer. For models which do not support streaming, this represents time to receive the completion.

End-to-End Response Time

Seconds to output 500 Tokens, calculated based on time to first token, 'thinking' time for reasoning models, and output speed

End-to-End Response Time

Seconds to Output 500 Tokens, including reasoning model 'thinking' time; Lower is better
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Input processing time
'Thinking' time (reasoning models)
Outputting time
Reasoning models are indicated by a lightbulb icon.

Seconds to receive a 500 token response. Key components:

  • Input time: Time to receive the first response token
  • Thinking time (only for reasoning models): Time reasoning models spend outputting tokens to reason prior to providing an answer. Amount of tokens based on the average reasoning tokens across a diverse set of 60 prompts (methodology details).
  • Answer time: Time to generate 500 output tokens, based on output speed

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

Model Size (Open Weights Models Only)

Model Size: Total and Active Parameters

Comparison between total model parameters and parameters active during inference
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Active Parameters
Passive Parameters
Reasoning models are indicated by a lightbulb icon.

The total number of trainable weights and biases in the model, expressed in billions. These parameters are learned during training and determine the model's ability to process and generate responses.

The number of parameters actually executed during each inference forward pass, expressed in billions. For Mixture of Experts (MoE) models, a routing mechanism selects a subset of experts per token, resulting in fewer active than total parameters. Dense models use all parameters, so active equals total.

Frequently Asked Questions

Common questions about Qwen3 VL 4B Instruct

Qwen3 VL 4B Instruct was released on October 14, 2025.

Qwen3 VL 4B Instruct was created by Alibaba.

Qwen3 VL 4B Instruct scores 10 on the Artificial Analysis Intelligence Index, placing it above average among other open weight non-reasoning models of similar size (median: 8).

When evaluated on the Intelligence Index, Qwen3 VL 4B Instruct generated 37M output tokens, which is at the higher end compared to other open weight non-reasoning models of similar size (median: 5.7M).

No, Qwen3 VL 4B Instruct is not a reasoning model. It provides direct responses without extended chain-of-thought reasoning.

Qwen3 VL 4B Instruct supports text and image input.

Qwen3 VL 4B Instruct supports text output.

Yes, Qwen3 VL 4B Instruct supports image input and can analyze, describe, and answer questions about images.

Yes, Qwen3 VL 4B Instruct is multimodal. It can process text and image input and generate text output.

Qwen3 VL 4B Instruct has a context window of 260k tokens. This determines how much text and conversation history the model can process in a single request.

Yes, Qwen3 VL 4B Instruct is open weights. The model weights are publicly available and can be downloaded for self-hosting.

Qwen3 VL 4B Instruct has 4.44 billion parameters.

Qwen3 VL 4B Instruct is released under the Apache 2.0 license. This license allows commercial use. View license

Qwen3 VL 4B Instruct achieves a score of 10 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across reasoning, knowledge, mathematics, and coding.

Qwen3 VL 4B Instruct is an open weights model that can be self-hosted. View providers

Qwen3 VL 4B Instruct is an open weights model that can be downloaded and self-hosted. Compare providers