Qwen3 VL 32B Instruct
Qwen · released Oct 19, 2025 · Qwen/Qwen3-VL-32B-Instruct
- Type
- Open weightsApache License 2.0
- Params
- 33.4B
- Context
- 131K
about 98K words of context
Our take
Written Sep 2, 2026Qwen3 VL is a 33.4-billion-parameter vision-language model from Alibaba that accepts text and images under a permissive Apache licence. It handles up to 131,072 tokens in a single request, though no benchmark scores are available to verify its quality claims.
Pick this for Apache-licensed vision-language work where you need open weights and image understanding, or for long-document plus image workflows at a single known price point. Skip it if you need measured quality data, consistent throughput guarantees, or verified efficiency claims.
The case for it
- Apache 2.0 licence allows commercial use, fine-tuning and redistribution.
- 131,072-token request limit is among the longer contexts we hold for open vision-language models of this size.
- Identical pricing across all three tracked offers simplifies provider comparison.
The case against it
- No benchmark scores in our data — chat, coding, reasoning and vision accuracy all unverified.
- Throughput varies 35% between the two measured Alibaba endpoints, with OpenRouter speed undisclosed.
- No active parameter count disclosed, so efficiency claims are unsupported.
How good is it?
We hold no score for this model.
So there is no figure here for everyday use, coding, agent work or writing. That is a gap in our data, not a low score.
Can you run it yourself?
GeForce RTX 4090 · 24 GB
Loads, but do not expect an assistant. Some of the weights sit in ordinary system memory, which is far slower than the card.21.1 GB of weights, plus 2.4 GB for the software that runs it and the smallest conversation it can hold, comes to 23.5 GB against the 22.8 GB this 24 GB device leaves free.
Comfortable fit
GeForce RTX 5090 · 32 GB
Room to spare. 7.4 GB spare means a 10% error in the size would not change the answer.
Apple M1 Pro (16-core GPU) · 32 GB
Borderline fit on an estimated size. It leaves 0.6 GB spare on a size we calculated rather than measured, and a 10% error either way would change the answer.
Memory use by level
Against a 24 GB card.
What is quantisation? →This model on every device we track71 devicesThe Q4 build most people download, on each device: what the weights come to, how much context the memory leaves, and whether it runs. Smallest device that runs it first.
Check against your own machine → · Where to rent it hosted →
Or rent it from someone else
Prices checked 4 hours ago — each listing carries its own date.
- per 1M tokens
- $0.10 in / $0.42 out
- Context served
- 131K
- Throughput
- ~37 tok/s
| Provider | In / out per 1M tokens | Context | Throughput | Trains on prompts | Logs prompts | Zero retention |
|---|---|---|---|---|---|---|
| OpenRouterOpenRouter's own listing | $0.10 / $0.42checked 4 hours ago | 131K | not measured | Unknown | Unknown | Unknown |
| Alibaba CloudThrough OpenRouter | $0.10 / $0.42checked 4 hours ago | 131K33K max reply | 37 tok/s | No | Yesunknown period | Unknown |
OpenRouterAcross the 2 listings we hold: 1 says it does not train on prompts, 0 say they do and 1 does not say. 0 appear in the zero-retention registry we check; the rest are unknown to us.
What each host's API supports
From the parameter list each endpoint publishes. Streaming is omitted: nothing we hold reports it, for any model.
| Provider | Tool calling | JSON output | Strict schema |
|---|---|---|---|
| OpenRouterOpenRouter's own listing | ✓ | ✓ | ✓ |
| Alibaba CloudThrough OpenRouter | ✓ | ✓ | ✓ |
Tool calling: 2 of 2 listings say yes. JSON output: 2 of 2 listings say yes. Strict schema: 2 of 2 listings say yes.
Models people weigh against Qwen3 VL 32B Instruct
When we formed this view
Recent changes
What moved
first indexed by our pipelineEach date is the day we first saw the change, or the day the maker announced it.
What we do not know about this model yet
- We hold no measured file for it, so all 3 sizes on this page are calculated from the parameter count.
- No independent board has scored it, so we hold no quality figures at all.
- Nothing we hold says whether an endpoint streams, so we do not show it either way.
- 1 of 2 listings does not say whether it trains on prompts.
- We hold no cached-input rate for any of its listings.
- We hold no batch or off-peak rate for any of its listings.
- We hold a decode speed for it, but no prompt-processing (prefill) figure, so how long the input side of a job takes is unknown to us.
Licence and identifiers
What the licence allowsApache License 2.0, what it allows commercially, and the identifiers you need to pull this model — its Hugging Face repo, our slug and a machine-readable card.
Licence
Apache License 2.0
Fully permissive: commercial use, redistribution, and derivatives allowed. Requires attribution and a copy of the license. Includes an express patent grant.
Identifiers
- Hugging Face
- Qwen/Qwen3-VL-32B-Instruct
- Architecture
- Dense
- Takes in, gives back
- Text and images in, text out
- Catalogue slug
- qwen-qwen3-vl-32b-instruct