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Open weights model

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Released March 2026

Qwen3.5 2B: Intelligence, Performance & Price Analysis

Name: Intelligence
Creator: Artificial Analysis
License: https://artificialanalysis.ai/docs/legal/Terms-of-Use.pdf

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Intelligence

N/A

Artificial Analysis Intelligence Index

Performance

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Time per Intelligence Index task (minutes)

Cost

In -Out -

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Cost per Intelligence Index task

Verbosity

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Output tokens from Intelligence Index

Qwen3.5 2B (Reasoning) supports text, image, and video input, outputs text, and has a 262k tokens context window.

Input modality	Supports: text, image, and video
Output modality	Supports: text
Context window	262k ~393 A4 pages of size 12 Arial font
Total parameters	2.3B
License	Apache 2.0
Model weights	Hugging Face

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

Intelligence

Artificial Analysis Intelligence Index · Higher is better

No publicly available API

Time per Task

Inference time (minutes) per Intelligence Index task · Lower is better

No publicly available API

Cost per Task

Weighted average cost (USD) per Intelligence Index task · Lower is better

No publicly available API

Intelligence

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v5.0.1 incorporates 9 evaluations: AA-Briefcase v1.1, AutomationBench-AA, Terminal-Bench 4.0, FrontierCode-AA 2.0, AA-Omniscience v1.1, GDP.pdf, HLE-Diamond, CritPt v2 (Hard Subset), and Terminal-Bench-Science 0.1

Artificial Analysis Intelligence Index v5.0.1 includes: AA-Briefcase v1.1, AutomationBench-AA, Terminal-Bench 4.0, FrontierCode-AA 2.0, AA-Omniscience v1.1, GDP.pdf, HLE-Diamond, CritPt v2 (Hard Subset), Terminal-Bench-Science 0.1. 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

Indicates whether the model weights are available. Models are labelled as 'Commercial Use Restricted' if commercial use is limited by conditions, and as 'Non-commercial' if the license prohibits commercial use.

Intelligence Index vs. Cost per Task

Artificial Analysis Intelligence Index · Weighted average cost (USD) per Artificial Analysis Intelligence Index task

Most attractive quadrant

No data available

Weighted average cost per Intelligence Index task. Each evaluation’s cost is calculated from input, cache hit, cache write, reasoning, and answer token prices, divided by task count, and weighted by its Intelligence Index weight.

Benchmarks

Evaluations

Intelligence evaluations measured independently by Artificial Analysis · Higher is better

GDPval-AA v2.1

Agentic real-world work tasks, Elo

MMMU-Pro

Visual reasoning

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

Openness Index

Artificial Analysis Openness Index: Score

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

Cost

Cost per Intelligence Index Task

Weighted average cost (USD) per Intelligence Index task · Segmented by token type · Lower is better

No data available

Cost to Run Artificial Analysis Intelligence Index

Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index

No data available

The cost to run the evaluations in the Artificial Analysis Intelligence Index, calculated using the model's input, cache hit, cache write, reasoning, and answer token prices and the number of tokens used across evaluations (excluding repeats).

Pricing: Cache Hit, Input, and Output

Price (USD per M Tokens)

Price per token for cached prompts (previously processed), typically offering a significant discount compared to regular input price, represented as USD per million tokens. The values shown here are the cache hit price; cache write and cache storage are billed separately and vary by provider — see "Cache pricing by provider" for detail.

Context Window

Context window: tokens limit · Higher is better

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).

Model Size (Open Weights Models Only)

Model Size: Total and Active Parameters

Comparison between total model parameters and parameters active during inference (billions)

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.

Further details

								Weights
Qwen3.5 2B (Reasoning)	Alibaba	-	2.3B	262k	-	-	-	🤗	-
Qwen3.5 2B (Non-reasoning)	Alibaba	-	2.3B	262k	-	-	-	🤗	-

Frequently Asked Questions

Common questions about Qwen3.5 2B

Qwen3.5 2B was released on March 2, 2026.

Qwen3.5 2B was created by Alibaba.

Yes, Qwen3.5 2B (Reasoning) is a reasoning model. It uses extended thinking or chain-of-thought reasoning to work through complex problems before providing an answer. No, Qwen3.5 2B (Non-reasoning) is not a reasoning model. It provides direct responses without extended chain-of-thought reasoning.

Qwen3.5 2B supports text, image, and video input.

Qwen3.5 2B supports text output.

Yes, Qwen3.5 2B supports image input and can analyze, describe, and answer questions about images.

Yes, Qwen3.5 2B is multimodal. It can process text, image, and video input and generate text output.

Qwen3.5 2B has a context window of 262k tokens. This determines how much text and conversation history the model can process in a single request.

Yes, Qwen3.5 2B is open weights. The model weights are publicly available and can be downloaded for self-hosting.

Qwen3.5 2B has 2.3B parameters.

Qwen3.5 2B is released under the Apache 2.0 license. This license allows commercial use. View license

Qwen3.5 2B is an open weights model that can be self-hosted. View providers

Qwen3.5 2B is an open weights model that can be downloaded and self-hosted. Compare providers

Qwen3.5 2B: Intelligence, Performance & Price Analysis

Model summary

Intelligence

Performance

Cost

Verbosity

Technical specifications

49 models in this class

Intelligence

Time per Task

Cost per Task

Intelligence

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index by Open Weights / Proprietary

Artificial Analysis Intelligence Index

Open Weights

Intelligence Index vs. Cost per Task

Cost per Intelligence Index Task

Artificial Analysis Intelligence Index

Benchmarks

Evaluations

Intelligence Evaluation Relevance

Artificial Analysis Intelligence Index

Openness Index

Artificial Analysis Openness Index: Score

Cost

Cost per Intelligence Index Task

Cost per Intelligence Index Task

Cost to Run Artificial Analysis Intelligence Index

Cost to Run Artificial Analysis Intelligence Index

Pricing: Cache Hit, Input, and Output

Cache Hit

Context Window

Context Window

Context Window for RAG

Context Window

Model Size (Open Weights Models Only)

Model Size: Total and Active Parameters

Total Parameters

Active Parameters at Inference Time

Further details

More from Alibaba

Frequently Asked Questions

When was Qwen3.5 2B released?

Who created Qwen3.5 2B?

Is Qwen3.5 2B a reasoning model?

What input modalities does Qwen3.5 2B support?

What output modalities does Qwen3.5 2B support?

Can Qwen3.5 2B process images?

Is Qwen3.5 2B multimodal?

What is the context window of Qwen3.5 2B?

Is Qwen3.5 2B open source?

How many parameters does Qwen3.5 2B have?

What is the license for Qwen3.5 2B?

Is Qwen3.5 2B available via API?

Where can I use Qwen3.5 2B?