China-friendly vision models for OpenRouter

China-friendly vision models for OpenRouter | Generated by AI

Home 2026.07

Question: If ChatGPT/Gemini violate terms for Chinese mainland users, what vision models can I use from OpenRouter?

Answer:

Yes, this is the practical problem. For users in mainland China, official ChatGPT/Gemini consumer services may have availability restrictions, and OpenAI explicitly says accessing ChatGPT/API from unsupported countries can result in account blocking or suspension. ([OpenAI Help Center][1]) OpenAI’s terms also require users to comply with applicable trade laws and restrictions. ([OpenAI][2])

For a ww vision-model CLI, I would avoid depending only on:

google/gemini-2.5-flash
openai/gpt-5
anthropic/claude-sonnet-4

even though technically they are excellent VLMs.

Better options:

1. Qwen-VL (best fit for China users)

OpenRouter:

qwen/qwen2.5-vl-72b-instruct

or:

qwen/qwen2.5-vl-32b-instruct

Architecture:

image
  |
  v
vision encoder
  |
  v
LLM
  |
  v
text answer

Example:

ww vision-model test \
  --model qwen/qwen2.5-vl-72b-instruct \
  --max-tokens 1000

Good for:

screenshot understanding
OCR
UI agents
Chinese documents
charts

2. Open-source VLMs

Search OpenRouter:

vl
vision-language
multimodal

Look for:

input:
  text
  image

output:
  text

Examples:

qwen/qwen2.5-vl-72b-instruct
qwen/qwen2.5-vl-32b-instruct
qwen/qwen2.5-vl-7b-instruct
mistralai/pixtral
google/gemma-3

3. Self-host (probably your style)

Since you already run GPUs/training, local VLM is interesting.

RTX 4070 12GB:

Possible:

Qwen2.5-VL-3B
Qwen2.5-VL-7B
MiniCPM-V
InternVL

Example:

ollama pull qwen2.5vl:7b

ollama run qwen2.5vl:7b

No ToS dependency.

Architecture:

image
 ↓
CLIP/SigLIP vision tower
 ↓
projector
 ↓
Qwen LLM
 ↓
answer

4. For your `ww` agent use case

I would make the default priority:

qwen2.5-vl-72b  (cloud)
qwen2.5-vl-7b   (local)
gemini-2.5-flash (if available)
claude-sonnet   (if available)

Because screenshot agents mostly need:

coordinate understanding
OCR
UI element detection
short reasoning

not pure benchmark IQ.

A useful OpenRouter filter idea:

for model in models:
    if "image" in model.input_modalities:
        if "text" in model.output_modalities:
            print(model.id)

You want:

image -> text

not:

text -> image

FLUX was:

text -> image

which is why your test returned:

finish_reason=stop
content=""

For a China-friendly AI agent stack, Qwen-VL + local Ollama/vLLM is probably the cleanest path.

[1]: https://help.openai.com/en/articles/9131992-chatgpt-and-api-services-in-unsupported-countries-and-territories?utm_source=chatgpt.com “ChatGPT and API services in unsupported countries and territories	OpenAI Help Center”
[2]: https://openai.com/policies/terms-of-use/?utm_source=chatgpt.com “Terms of Use	OpenAI”

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