Qwen2.5 VL 72B Instruct — Alibaba | Modeldex

Qwen2.5 VL 72B Instruct

Active

Qwen2.5-VL is proficient in recognizing common objects such as flowers, birds, fish, and insects. It is also highly capable of analyzing texts, charts, icons, graphics, and layouts within images.

Updated 4 days agoStructured data from Modeldex catalog

VisionBudget

Knowledge cutoff

Jun 30, 2024(1.8 years ago)

API release

Feb 1, 2025(1.2 years ago)

Not enough benchmark coverage yet for an Intelligence Index — needs at least 3 results across 2 categories.

Overview

Qwen2.5-VL is proficient in recognizing common objects such as flowers, birds, fish, and insects. It is also highly capable of analyzing texts, charts, icons, graphics, and layouts within images.

History

Qwen2.5 VL 72B Instruct became available via the Alibaba API on 2025-02-01.

Training & availability

Training data has a knowledge cutoff of 2024-06-30 — information about events after that date is unlikely to appear in the model's responses. Alibaba has not released the underlying model weights — access is via their hosted API only.

Capabilities

Context window: 32K tokens.
Input modalities: text, image.

Recommended for: vision, cheap.

Limitations

The knowledge cutoff is 21 months old — this model will not know about recent events, releases, or API changes.
The context window (32K tokens) is modest by 2026 standards — unsuitable for processing long documents in a single request.

Pricing

Input: $0.2500 per 1M tokens
Output: $0.7500 per 1M tokens

Use the cost calculator above to estimate monthly spend for your workload.

Quick start

Minimal example using the OpenRouter API. Copy, paste, replace the key.

from openai import OpenAI

client = OpenAI(
    base_url="https://openrouter.ai/api/v1",
    api_key="sk-or-...",
)
resp = client.chat.completions.create(
    model="alibaba/qwen2-5-vl-72b-instruct",
    messages=[{"role": "user", "content": "Explain quantum computing in one sentence."}],
)
print(resp.choices[0].message.content)

Cost calculator

Estimate your monthly bill. Presets are typical workload sizes.

Input tokens / month5.0M

@ $0.25/1M

Output tokens / month2.0M

@ $0.75/1M

Input cost

$1.25

5.0M × $0.25/1M

Output cost

$1.5

2.0M × $0.75/1M

Total / month

$2.75

$33 / year

Providers & performance

3 providers

Multi-provider inference routes for this model — sorted by throughput. Latency is time-to-first-token; throughput is output tokens per second. Data from OpenRouter, measured over the last 30 minutes.

Provider	Throughput	Latency (TTFT)	Input $ / 1M	Output $ / 1M	Context	Quant	Supports
Nebius	30tok/s	752ms	$0.25	$0.75	32K	fp8	—
Novita	22tok/s	1.92s	$0.8	$0.8	33K	bf16	—
Parasail	21tok/s	594ms

Popularity

Signals from open-source communities — not a quality measure, but useful for gauging adoption among developers.

🤗

HuggingFace downloads

103.5K/ month

HuggingFace likes

606

Integrations & tooling support

Tool calling: Not supported
Structured outputs: Not supported

Price vs quality

Budget pricing

Priced low — good for high-volume tasks. Quality tier pending more benchmark coverage.