Alibaba

Qwen3.5 0.8B

About

Qwen3.5 0.8B is a 0.8B open-weight model from Alibaba. Multimodal chat model with up to 64 images or one video per request. It accepts text, images, and video. Context window is 256K. License is Apache-2.0. Released February 28, 2026.

Parameters
0.8B
Published parameter count.
Context window
256K
Tokens of context on a request.
License
Apache-2.0
License on the weights.

Pricing

Token rates are US dollars per 1M tokens. Image rates are per 1K images. Audio rates are per 1M audio seconds.

Rate	Price
Input / 1M	$0.08
Cached input / 1M	$0.02
Output / 1M	$0.15

Compare

Qwen3.5 0.8B is the smallest of these chat models, at 0.8B, and its published input rate is the lowest, $0.08 / 1M input.

Model	What it does	Price	Parameters	Context
Qwen3.5 0.8B	Multimodal chat model with up to 64 images or one video per request.	$0.08 / 1M input	0.8B	256K
Qwen3.8 27B	Reasoning VLM (image + video + text)	$0.35 / 1M input	27B	256K
Gemini 3.5 Flash Lite	Fastest and cheapest Gemini tier. Multimodal chat; emits no reasoning tokens.	$0.30 / 1M input	—	1M
Gemini 3.7 Flash	Newest Gemini Flash tier with extended reasoning. Multimodal chat + native video.	$0.75 / 1M input	—	1M

Benchmarks

Published scores for Qwen3.5 0.8B, from the Qwen3.5-0.8B model card. Thinking-mode scores.

Evals

Reading	Example
Qualitative	Clear structure, grounded in the input
Quantitative	MMMU: 49.
Cost and performance	Lower listed rate, mid-pack latency

Examples

Counting tennis balls
Qwen3-VL 0.8B answers a counting question on a court photo.
{"answer":"4"}

Methods

Methods this model serves. Payload shapes are in the docs.

Method	Returns
chat	The model's reply

Estimate cost

Estimate. An hour of video is 15 frames a minute at 256 tokens a frame, plus the audio in that request, and 500 output tokens a minute.

Hours of video

$32.15

Quick start

Call this model on the OpenAI-compatible gateway. The model id is already filled in.

from openai import OpenAI

client = OpenAI(
    base_url="https://gateway.vlm.run/v1/openai",
    api_key="<VLMRUN_API_KEY>",
)

response = client.chat.completions.create(
    model="qwen/qwen3.5-0.8b",
    messages=[{"role": "user", "content": "What is in this image?"}],
)

print(response.choices[0].message.content)