Roboflow
Qwen

Qwen: Qwen3.7 Plus

Qwen3.7 Plus Overview

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Qwen3.7 Plus Interactive Demo

Qwen3.7 Plus Details & Performance

Details

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Vision Tasks

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Features

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Usage

Past 30 Days

Performance

Avg. Latency

Arena Rankings

Qwen3.7 Plus Vision Evals

Vision Evals is Roboflow's ground-truth benchmark: every model runs the same real-world samples across six vision tasks, and answers are scored against ground truth.

Evals updated August 20, 2026Pricing updated August 22, 2026

Overall score#18 of 31
67.4%
Avg cost / sample#4 of 31
$0.0008
Avg speed / sample#12 of 31
7.01s
Avg tokens / sample
1.5K

Strengths and weaknesses

Qwen3.7 Plus averages 67.4% across the six Vision Evals tasks, ranking #18 of 31 models overall.

Its weakest relative showing is OCR, ranking #27 of 31 at 86.5%.

At $0.0008 per sample it is the 4th cheapest of the 31 benchmarked models, and its average inference time of 7.0s per sample makes it the 12th fastest.

Performance profile

Field medianQwen3.7 Plus

Field medians: Object Detection 54.5%, Counting 63.5%, Identification 84.4%, OCR 89.3%, Data Extraction 86.6%, Reasoning 57.6%.

Results by task

TaskScoreField (0 to 100)RankCost / sampleSpeed
Object Detection
60.1%
#8 of 31$0.001313.21s
Counting
50.0%
#24 of 31$0.00042.34s
Identification
84.4%
#13 of 31$0.00032.21s
OCR
86.5%
#27 of 31$0.000910.39s
Data Extraction
83.5%
#21 of 31$0.00042.42s
Reasoning (low)
39.7%
#24 of 31$0.00032.19s
Reasoning (high)
68.2%
#14 of 31$0.004358.41s
  • Thinking longer helps: 28.5 points higher on reasoning at high effort for 12.4x the cost and 26.7x the latency.

Price vs. performance

Score vs. cost

Overall benchmark score against estimated cost per sample. Upper-left is the sweet spot: high quality at low cost.

31 models on the current benchmark · scores and efficiency pooled across all six tasks at low effort · Qwen 3.7 Plus highlighted

Qwen3.7 Plus scores from a single evaluation run · Methodology

View all Vision Evals →

Qwen3.7 Plus Pricing

Qwen3.7 Plus costs $0.320 per 1M input tokens and $1.28 per 1M output tokens.

Input$0.320 / 1M tokens
Output$1.28 / 1M tokens
Cached input$0.064 / 1M tokens

Pricing updated Aug 22, 2026

Deploy Qwen3.7 Plus with an API

Qwen3.7 Plus runs as a hosted REST endpoint through Roboflow Workflows. Pick a task, then hand the prompt to your coding agent or copy the code. Deploying the workflow into a free Roboflow workspace replaces the your-workspace and YOUR_API_KEY placeholders with your own.

Connect your agent to Roboflow (once)

Add the Roboflow MCP server

claude mcp add --transport http roboflow https://mcp.roboflow.com/mcp

Run /mcp and authorize Roboflow in your browser when the OAuth flow opens.

Start a new Claude Code session so the MCP loads, then paste the prompt below (it works the same in any agent).

Deploy this workflow to your Roboflow workspace to use it.

Integrate the Roboflow "Qwen3.7 Plus" workflow into my app.

- Endpoint: POST https://serverless.roboflow.com/<your-workspace>/workflows/qwen3-7-plus-captioning
- Auth: send my Roboflow API key as `api_key` in the request body, read from the ROBOFLOW_API_KEY env var (never hardcode).
- Body: { "api_key": ..., "inputs": { `image`: { type: "url" | "base64", value } } }.
- Billing: this workflow needs no provider API key — inference runs on my Roboflow credits. A BYO provider key can be added to the model step in the Roboflow workflow editor later.

With the Roboflow MCP connected, call `workflows_get` on "qwen3-7-plus-captioning" to read the exact input schema (the source of truth), then `workflows_run` on a sample image to confirm the output shape before writing code (the MCP is authenticated, so this needs no key). Without the MCP, use the contract above.

Before running the app, set up these keys so it does not error at runtime:
- `ROBOFLOW_API_KEY` (sent as `api_key`) from https://app.roboflow.com/settings/api
Create a .gitignore'd .env with these variables, using placeholder values for any I haven't given you. Then pause and tell me directly, in your reply: the full path to the .env file, exactly which keys I need to paste in, and the link to get each one. Wait for me to confirm I've added them before you run anything. Do not run the app until I confirm.

Then add the integration to my codebase: match my project's language, framework, and conventions; read every key from environment variables (never hardcode); add basic error handling; and include a small runnable example. If you can't tell what language my project uses, ask me.
Installpip install inference-sdk

Deploy this workflow to your Roboflow workspace to use it.

# Inference runs on your Roboflow credits — no provider API key needed. To bill your own provider account instead, add an api_key to the model step in the Roboflow workflow editor.
# 1. Import the library
from inference_sdk import InferenceHTTPClient

# 2. Connect to your workflow
client = InferenceHTTPClient(
  api_url="https://serverless.roboflow.com",
  api_key="YOUR_API_KEY"
)

# 3. Run your workflow on an image
result = client.run_workflow(
  workspace_name="your-workspace",
  workflow_id="qwen3-7-plus-captioning",
  images={
    "image": "YOUR_IMAGE.jpg"  # Path to your image file
  },
  use_cache=True  # cache workflow definition for 15 minutes
)

# 4. Get your results
print(result)

Deploy this workflow to your Roboflow workspace to use it.

// Inference runs on your Roboflow credits — no provider API key needed. To bill your own provider account instead, add an api_key to the model step in the Roboflow workflow editor.
const response = await fetch('https://serverless.roboflow.com/your-workspace/workflows/qwen3-7-plus-captioning', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    api_key: 'YOUR_API_KEY',
    inputs: {
      "image": {"type": "url", "value": "IMAGE_URL"}
    }
  })
});

const result = await response.json();
console.log(result);

Deploy this workflow to your Roboflow workspace to use it.

# Inference runs on your Roboflow credits — no provider API key needed. To bill your own provider account instead, add an api_key to the model step in the Roboflow workflow editor.
curl --location 'https://serverless.roboflow.com/your-workspace/workflows/qwen3-7-plus-captioning' \
--header 'Content-Type: application/json' \
--data '{
  "api_key": "YOUR_API_KEY",
  "inputs": {
    "image": {"type": "url", "value": "IMAGE_URL"}
  }
}'

Frequently Asked Questions About Qwen3.7 Plus Vision

Yes. Qwen3.7 Plus accepts image input and handles OCR, data extraction, object counting, identification, visual reasoning, and object detection. On Roboflow's Vision Evals its strongest task is Object Detection at 60.1% (#8 of 31). You can test it on your own image in the demo above.

Yes. its transcriptions match the ground truth 86.5% on average (#27 of 31) on Vision Evals OCR. Pulling specific fields out of documents (data extraction) scores 83.5%.

Not its strength. On Vision Evals, Qwen3.7 Plus scores 60.1% mAP@50 on object detection (#8 of 31) and 50% exact-match accuracy on object counting. For production counting or precise localization, pairing it with a specialized detector like RF-DETR or your own trained model in a Roboflow Workflow is usually more reliable: detect the objects, then count the detections.

On our benchmark's task mix, Qwen3.7 Plus averages $0.0008 per sample at $0.32 per 1M input and $1.28 per 1M output tokens (#4 of 31 on cost), with an average speed of 7.0s per sample across the benchmark. Actual cost depends on your images and prompts.

On the overall Vision Evals ranking, Qwen3.7 Plus sits #18 of 31 at 67.4%, just behind Grok 4.6 (67.8%) and just ahead of Claude Opus 4.8 (66.8%). See the full side-by-side: Qwen3.7 Plus vs Grok 4.6.