- Type
- Open weightsApache License 2.0
- Params
- 8.2B
- Context
- 131K
about 98K words of context
Our take
Written Sep 4, 2026Qwen3 is a compact downloadable text model with 8.2 billion parameters and a permissive Apache licence. It handles up to 131,072 tokens in a single request and offers very cheap hosted access on some providers, though no benchmark scores are available to confirm its quality.
Pick this for budget text tasks where licence permissiveness matters — the cheapest tracked host is several times less expensive than the next tier. Use it for commercial or derivative work without restriction, or for moderate-length documents up to 131,072 tokens. Skip it if you need measured quality data, consistent throughput guarantees, or anything beyond plain text input and output.
The case for it
- Cheapest tracked host is several times less expensive than the next tier for this model.
- Apache 2.0 licence allows unrestricted commercial use, modification and redistribution.
- 131,072-token request limit is substantial for an 8.2-billion-parameter model.
The case against it
- No benchmark scores in our data, so quality is unmeasured.
- Throughput data is thin and inconsistent across hosts, with no basis to claim speed leadership or deficit.
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. 16 GB spare means a 10% error in the size would not change the answer.
GeForce RTX 5090 · 32 GB
Room to spare. 24 GB spare means a 10% error in the size would not change the answer.
Apple M1 (8-core GPU) · 16 GB
Room to spare. 5.2 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 2 hours and 21 days ago — each listing carries its own date.
Alibaba Cloud, through OpenRouter
Cheapest of the 2 listings we can compare like for like — at 131K of context, out of 3 in the table below. One cheaper row there is outside that comparison: a different context length.
- per 1M tokens
- $0.12 in / $0.46 out
- Context served
- 131K
- Throughput
- ~13 tok/s
| Provider | In / out per 1M tokens | Context | Throughput | Trains on prompts | Logs prompts | Zero retention |
|---|---|---|---|---|---|---|
| Novita AIDirect | $0.035 / $0.14checked 21 days ago | 128K | not measured | Unknown | Unknown | Unknown |
| OpenRouterOpenRouter's own listing | $0.12 / $0.46checked 2 hours ago | 131K | not measured | Unknown | Unknown | Unknown |
| Alibaba CloudThrough OpenRouter | $0.12 / $0.46checked 2 hours ago | 131K8K max reply | 13 tok/s | No | Yesunknown period | Unknown |
Novita AI
OpenRouterAcross the 3 listings we hold: 1 says it does not train on prompts, 0 say they do and 2 do 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 |
|---|---|---|---|
| Novita AIDirect | |||
| OpenRouterOpenRouter's own listing | ✓ | ✓ | ✗ |
| Alibaba CloudThrough OpenRouter | ✓ | ✓ | ✗ |
Tool calling: 2 of 3 listings say yes, 1 publishes no parameter list. JSON output: 2 of 3 listings say yes, 1 publishes no parameter list. Strict schema: 0 of 3 listings say yes, 2 say no, 1 publishes no parameter list.
Models people weigh against Qwen3 8B
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 3 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 3 listings do not say whether they train 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/Qwen3-8B
- Architecture
- Dense
- Takes in, gives back
- Text in, text out
- Catalogue slug
- qwen-qwen3-8b