Qwen3-VL-30B-A3B Instruct
An efficient 30B MoE vision-language model with 3B active, offering strong multimodal capability at low inference cost.
- Input
- $0.20 /1M tokens
- Output
- $0.80 /1M tokens
Cheapest: $0.20 / $0.70 per 1M tokens via Novita
Key Specifications
- Context window
- 262K tokens
- Max output
- 66K tokens
- Parameters
- 31.1B, 3B active
- Inputs
- Text, image, video
- Capabilities Show details
- Function calling Connect to external tools, APIs, and systems. Structured output Return responses in structured formats like JSON.
Hosted API pricing
| Provider | Input /1M tokens | Output /1M tokens | Cost at 10M in + 2M out | |
|---|---|---|---|---|
|
|
$0.20 | $0.70 | $3.40 | View |
|
|
$0.20 | $0.80 | $3.60 | View |
Heads up: Base-tier, on-demand rates per 1M tokens; cached, batch and long-context tiers excluded. A provider may serve a shorter context or a quantized build than the creator's release. Verify before provisioning. More on how we price.
Estimated cost to self-host
Qwen3-VL-30B-A3B Instruct needs about 21 GB of GPU memory at 4-bit with 32K context. The cheapest rental that fits is 2× RTX 4070 at about $144 a month.
That costs the same as roughly 480M tokens a month on Alibaba Cloud's API. Self-hosting is more expensive below that volume.
| Precision | Memory | Cheapest fit (32K context) | Cost /mo | Break-even vs API |
|---|---|---|---|---|
| 4-bitINT4 / FP4 |
21 GB
|
$144
|
480M tokens /mo
|
|
| 8-bitFP8 / INT8 |
36 GB
|
$173
|
576M tokens /mo
|
|
| 16-bitFP16 / BF16 |
67 GB
|
$346
|
1B tokens /mo
|
Estimates based on median on-demand rates for Nvidia GPUs. Memory is weights plus KV cache for one request, using FP8 KV cache where supported and FP16/BF16 otherwise. Break-even assumes a 5:1 input-to-output ratio. No guarantee of runtime support, usable performance, or that a matching quantized build exists. How we estimate costs.
Similarly priced models
The models nearest Qwen3-VL-30B-A3B Instruct by blended rate, each at its own cheapest provider.
| Model | Blended /1M | Input /1M | Output /1M | Context | Cutoff | vs Qwen3-VL-30B-A3B Instruct |
|---|---|---|---|---|---|---|
|
|
$0.225 | $0.17 | $0.50 | 262K | −21% | |
|
|
$0.23 | $0.08 | $0.98 | 262K | −19% | |
|
|
$0.24 | $0.16 | $0.64 | 10M | Aug 2024 | −15% |
|
|
$0.2533 | $0.12 | $0.92 | 262K | −11% | |
|
|
$0.2833 | $0.20 | $0.70 | 1M | Aug 2024 | 0% |
|
|
$0.2833 | $0.20 | $0.70 | 262K | ||
|
|
$0.30 | $0.20 | $0.80 | 131K | Jan 2026 | +6% |
|
|
$0.30 | $0.30 | $0.30 | 33K | +6% | |
|
|
$0.30 | $0.20 | $0.80 | 33K | +6% | |
|
|
$0.3133 | $0.17 | $1.03 | 262K | +11% | |
|
|
$0.3167 | $0.20 | $0.90 | 1M | +12% |
Prices are USD per 1M tokens at each model's cheapest listed provider. Blended is the cost of 10M input plus 2M output tokens, spread over the 12M.
Frequently Asked Questions
What is Qwen3-VL-30B-A3B Instruct good for?
Image and video understanding on a single consumer GPU. Apache 2.0 weights, a 30B mixture of experts with 3B active.
When is Qwen3-VL-30B-A3B Instruct not a good fit?
Audio input, image output and built-in web search, none of which it supports. Long chain-of-thought reasoning is the job of the Thinking sibling.
What is the cheapest way to run Qwen3-VL-30B-A3B Instruct?
Hosted, unless you push serious volume. Alibaba Cloud charges $0.20 in / $0.80 out per 1M tokens. The cheapest rental that fits is 2x RTX 4070 at $144 a month, which costs the same as about 480M tokens a month on that API.
Can I self-host Qwen3-VL-30B-A3B Instruct?
Yes. The weights are Apache 2.0 licensed. At 4-bit it needs about 21 GB of GPU memory, which starts at roughly $144 a month on the cheapest rental that fits.
More from Alibaba Cloud
| Model | Context | Input /1M | Output /1M |
|---|---|---|---|
|
|
33K | $0.30 | $0.30 |
|
|
33K | $0.20 | $0.80 |
|
|
262K | $0.17 | $1.03 |
|
|
262K | $0.12 | $0.92 |
|
|
262K | $0.15 | $1.20 |
|
|
262K | $0.15 | $1.20 |
|
|
33K | $0.25 | $0.75 |
|
|
16K | $0.25 | $0.75 |