Qwen3-VL Plus is the higher-performance tier of the Qwen3-VL vision-language series, offering a 256K-token context for image- and video-heavy workloads.
- Input
- text image video $0.2/M
- Output
- text $1.6/M
- Cache read
- $0.143/M
- Context
- 256K
- vs GPT-4o
- ~96% cheaper
Price in context
Where the price sits among 60 comparable models
The bar shows how this model’s price compares with every other model of the same kind on Synthorai. The cheapest and the most expensive are named at each end. These are base rates; batch, region and cache-write discounts are on the pricing page.
Specs & limits
Tokens
| Context window (vendor spec) | 262,144 |
|---|---|
| Max output (vendor spec) | 32,768 |
Prompt caching
| How it caches | automatic + explicit |
|---|---|
| Min prefix | 1,024 |
| Lifetime | explicit: 5m, reset on hit |
| Write cost | 1.25x |
Thinking
| Vendor control | enable_thinking + thinking_budget |
|---|---|
| Accepted values | enable_thinking true · false; thinking_budget in tokens |
| Default | off; enable_thinking defaults to false on the qwen3-vl-plus and qwen3-vl-flash series applied when the request sets nothing |
| Can be turned off | Yes |
| Thinking behaviour | Trace returns in reasoning_content; over the budget the reasoning is truncated and the model answers immediately. Not on the preserve_thinking model list. |
| Parameter | reasoning_effort |
| Values | minimal · low · medium · high the gateway's parameter surface - the vendor mapping above applies |
Model
| Modalities | text + image + video → text |
|---|
- Flagship Qwen3-VL visual-understanding model with hybrid thinking
- officially claimed visual-agent (GUI operation) capability
- video input up to 1 hour / 2GB
Use Qwen3 VL Plus in 30 seconds
OpenAI-compatible: swap the base_url, keep your SDK. POST /v1/chat/completions
from openai import OpenAI
client = OpenAI(
base_url="https://synthorai.io/v1",
api_key="sk-syn-...",
)
resp = client.chat.completions.create(
model="qwen3-vl-plus",
messages=[{"role": "user", "content": "Summarize this diff"}],
reasoning_effort="medium",
)
print(resp.choices[0].message.content)import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://synthorai.io/v1",
apiKey: "sk-syn-...",
});
const resp = await client.chat.completions.create({
model: "qwen3-vl-plus",
messages: [{ role: "user", content: "Summarize this diff" }],
reasoning_effort: "medium",
});
console.log(resp.choices[0].message.content);curl https://synthorai.io/v1/chat/completions \
-H "Authorization: Bearer sk-syn-..." \
-H "Content-Type: application/json" \
-d '{
"model": "qwen3-vl-plus",
"messages": [{"role": "user", "content": "Hello"}],
"reasoning_effort": "medium"
}'package main
import (
"context"
"fmt"
"github.com/openai/openai-go/v3"
"github.com/openai/openai-go/v3/option"
)
func main() {
client := openai.NewClient(
option.WithBaseURL("https://synthorai.io/v1"),
option.WithAPIKey("sk-syn-..."),
)
resp, _ := client.Chat.Completions.New(context.TODO(), openai.ChatCompletionNewParams{
Model: "qwen3-vl-plus",
Messages: []openai.ChatCompletionMessageParamUnion{
openai.UserMessage("Summarize this diff"),
},
ReasoningEffort: openai.ReasoningEffortMedium,
})
fmt.Println(resp.Choices[0].Message.Content)
}import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.chat.completions.*;
import com.openai.models.ReasoningEffort;
OpenAIClient client = OpenAIOkHttpClient.builder()
.baseUrl("https://synthorai.io/v1")
.apiKey("sk-syn-...")
.build();
ChatCompletion resp = client.chat().completions().create(
ChatCompletionCreateParams.builder()
.model("qwen3-vl-plus")
.addUserMessage("Summarize this diff")
.reasoningEffort(ReasoningEffort.MEDIUM)
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));About Qwen3 VL Plus
- The official documentation describes support for single and multiple image inputs, video frame understanding with adjustable sampling, visual question answering, object detection and localization in 2D and 3D, and document parsing into structured HTML or Markdown.
- Like its Flash sibling it is a hybrid thinking model that can deliberate before responding, using enable_thinking (off by default here, unlike the later Qwen3.5 and newer generations) and thinking_budget to cap the spend, with the trace returned separately in reasoning_content rather than mixed into the answer.
- What sets Plus apart in Alibaba's own listing is visual-agent capability: it is the tier documented for GUI operation and for very long video understanding, on top of the shared grounding, OCR and document-parsing features, and Alibaba flags 3D localization and Markdown document parsing as what the Qwen3-VL generation added over its predecessor.
- The served envelope allows up to 32,768 output tokens plus a large separate reasoning allowance, with video accepted up to an hour and 2 GB, and images bounded by a token ceiling rather than a fixed count.
- Frame sampling is yours to set, including supplying pre-extracted frames.
- Tool calling, structured output, batch inference and both explicit and implicit context caching are listed.
- The hosted model is a commercial service rather than an open-weights release.
- On Synthorai it is one API call away through the OpenAI-compatible interface.
FAQ
Is the Qwen3 VL Plus API free to try?
Yes: new accounts get 10 trial calls and up to $1 in free credit, no card required. At $0.2/M input tokens, that credit alone covers roughly 625 requests of ~8K tokens against Qwen3 VL Plus.
What is Qwen3 VL Plus best at?
Video understanding with adjustable sampling; object detection in 2D and 3D; 256K context for image- and video-heavy work. See the About section for the full picture from the vendor's own release notes.
How much does Qwen3 VL Plus cost?
Qwen3 VL Plus costs $0.2 per million input tokens and $1.6 per million output tokens on Synthorai. That is the provider's list price, with no platform markup. Cached input tokens bill at $0.143/M.
Does Qwen3 VL Plus support prompt caching?
Yes: automatic caching is on by default, with an explicit mode for guaranteed savings. Cached input tokens bill at $0.143/M vs $0.2/M uncached; prompts need a 1,024-token stable prefix to cache (TTL explicit: 5m, reset on hit). Prompt caching guide →
How do I get access to Qwen3 VL Plus?
Point your existing OpenAI SDK at base_url="https://synthorai.io/v1", set model="qwen3-vl-plus", and you're done. One API key covers every model on the gateway.
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Every value on this page is transcribed from the vendor's own documentation, linked above, and carries the date it was checked. Prices are compared across the catalogue; specification values that vendors define differently are shown with the difference stated rather than charted. Nothing here is measured by us, and nothing is scored.