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 Aug 3, 2026Qwen3 VL is a vision-language model from Alibaba that accepts text and images and returns text. It carries a permissive Apache licence and a 131,072-token request limit, but no benchmark scores have been measured yet.
Pick this for Apache-licensed multimodal deployment where open weights matter, or for long-context vision tasks needing a large working memory. Use it if you want identical pricing across every provider and no arbitrage complexity. Skip it if you need measured quality data to validate performance, or if consistent throughput is critical — the two measured endpoints differ by nearly threefold.
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 for open vision-language models of this size.
- All three tracked offers are priced identically, so provider choice does not hinge on cost.
The case against it
- No benchmark scores in the catalogue, so quality is entirely unverified.
- Throughput is inconsistent and partly undisclosed: two Alibaba endpoints report 10 and 27 tokens per second, and the third offer reports nothing.
- 33.4 billion total parameters with no disclosed active count — not a sparsity-based efficiency play.
How good is it?
We hold no score for this model.
We look for every model we track on every board we watch, and none of them has turned up Qwen3 VL 32B Instruct — so there is no intelligence, coding, agentic or writing score to show you, not a low one, none.
Can you run it yourself?
- Fits in memory
- weights load entirely on the card
- Spills to system RAM
- some weights offload; much slower
- Too large
- will not load even with offload
- est
- size is calculated; the verdict could change by 10%
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.
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.
All 3 sizes here are calculated, not measured. We hold no measured file for this model, so each size comes from the parameter count and every verdict above inherits that uncertainty.
Check against your own machine → · All 71 devices, with every size →
Or rent it from someone else
Cheapest of 3 live listings. Picked at the widest standard context we hold, within one quantisation slice, so the numbers beside it are a price one host actually charges.
- per 1M tokens
- $0.10 in / $0.42 out
- Context served
- 131K
- Throughput
- Not measured
| Provider | In / out per 1M tokens | Context | Throughput | Trains on prompts | Logs prompts | Zero retention |
|---|---|---|---|---|---|---|
| OpenRouter | $0.10 / $0.42 | 131K | not measured | Unknown | Unknown | Unknown |
| Alibaba Cloudfp8 | $0.10 / $0.42 | 131K | 49 tok/s | No | Yesunknown period | Unknown |
| Alibaba Cloud | $0.10 / $0.42 | 131K | 27 tok/s | No | Yesunknown period | Unknown |
Across the 3 listings we hold: 2 say they do not train on prompts, 0 say they do and 1 do not say. 0 appear in the zero-retention registry we check; the rest are unknown to us rather than confirmed either way.
What each host's API supports
From the parameter list each endpoint publishes. Streaming is omitted: nothing we hold reports it, for any model.
- ✓
- Supported
- ✗
- Not supported
- Not published
- host gave no parameter list
| Provider | Tool calling | JSON output | Strict schema |
|---|---|---|---|
| OpenRouter | ✓ | ✓ | ✓ |
| Alibaba Cloudfp8 | ✓ | ✓ | ✓ |
| Alibaba Cloud | ✓ | ✓ | ✓ |
Tool calling: 3 of 3 listings say yes. JSON output: 3 of 3 listings say yes. Strict schema: 3 of 3 listings say yes.
Models people weigh against Qwen3 VL 32B Instruct
When we formed this view
Dates behind this page
Prices last checked 4d ago
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 board we watch has turned up a score, 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 3 listings do not say whether they train on prompts.
- We hold no cached-input rate for any of its listings.
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
- Modality record
- text+image->text
- Catalogue slug
- qwen-qwen3-vl-32b-instruct