Qwen3 VL 8B Instruct
Qwen · released Oct 11, 2025 · Qwen/Qwen3-VL-8B-Instruct
- Type
- Open weightsApache License 2.0
- Params
- 8.8B
- Context
- 262K
about 197K words of context
Our take
Written Sep 3, 2026Qwen3 VL is a compact vision-language model from Alibaba that accepts text and images and returns text. At 8.8 billion parameters with a permissive Apache licence, it is built for lightweight image-understanding tasks where licensing flexibility matters.
Pick this for budget-conscious image-and-text work with a permissive licence, or for long-context vision tasks needing a quarter-million tokens of history. Use it when commercial use and redistribution are required. Skip it if you need measured quality scores, fast throughput at every price point, or certainty about whether the architecture is dense or mixture-of-experts.
The case for it
- Apache 2.0 licence allows unrestricted commercial use, fine-tuning and redistribution.
- 262,144-token request limit is unusually large for a sub-10-billion-parameter model.
- Lowest tracked input price in its offer set, noticeably under the next-cheapest host.
The case against it
- No benchmark scores in our data — no measured quality, reasoning, coding or vision accuracy.
- Slowest measured throughput pairs with the highest price at one host, while a faster host charges less.
- Active parameter count is undisclosed, so efficiency and architecture claims cannot be verified.
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?
Comfortable fit
GeForce RTX 4090 · 24 GB
Room to spare. 15.6 GB spare means a 10% error in the size would not change the answer.
GeForce RTX 5090 · 32 GB
Room to spare. 23.6 GB spare means a 10% error in the size would not change the answer.
Apple M1 (8-core GPU) · 16 GB
Room to spare. 4.8 GB spare means a 10% error in the size would not 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 between 4 hours and 21 days ago — each listing carries its own date.
- per 1M tokens
- $0.12 in / $0.46 out
- Context served
- 262K
- Throughput
- Not measured
| Provider | In / out per 1M tokens | Context | Throughput | Trains on prompts | Logs prompts | Zero retention |
|---|---|---|---|---|---|---|
| OpenRouterOpenRouter's own listing | $0.12 / $0.46checked 4 hours ago | 262K | not measured | Unknown | Unknown | Unknown |
| Alibaba CloudThrough OpenRouter | $0.12 / $0.46checked 4 hours ago | 131K33K max reply | 44 tok/s | No | Yesunknown period | Unknown |
| Novita AIDirect | $0.080 / $0.50checked 21 days ago | 131K | not measured | Unknown | Unknown | Unknown |
| Parasailbf16Through OpenRouter | $0.25 / $0.75checked 10 hours ago | 262K236K max reply | 10 tok/s | No | No | Confirmed |
Novita AIAcross the 4 listings we hold: 2 say they do not train on prompts, 0 say they do and 2 do not say. 1 appears 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 | ✓ | ✓ | ✓ |
| Novita AIDirect | |||
| Parasailbf16Through OpenRouter | ✓ | ✓ | ✓ |
Tool calling: 3 of 4 listings say yes, 1 publishes no parameter list. JSON output: 3 of 4 listings say yes, 1 publishes no parameter list. Strict schema: 3 of 4 listings say yes, 1 publishes no parameter list.
Models people weigh against Qwen3 VL 8B 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.
- 1 of 4 listings publishes no parameter list, so what its API accepts is unknown to us.
- Nothing we hold says whether an endpoint streams, so we do not show it either way.
- 2 of 4 listings do not say whether they train on prompts.
- 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-8B-Instruct
- Architecture
- Dense
- Takes in, gives back
- Text and images in, text out
- Catalogue slug
- qwen-qwen3-vl-8b-instruct