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Free AI Visibility AuditQwen3 8B is a dense, text-only open-weight language model from Qwen's Qwen3 series, built to handle both reasoning-heavy work and ordinary dialogue in one small model.
QwenVerified 24/09/2026Released 28/04/202501 / decision
Each figure sits next to the middle of the field, so it can be read as dear or cheap, wide or narrow, rather than floating on its own. Compared against the 126 models we track, not against an absolute standard.
Capability
Not measured
No published benchmark scores yet
Input / 1M tokens
$0.12
field median $0.30
Output / 1M tokens
$0.46
field median $1.25
Context
131K
field median 524K
02 / overview
Four questions, answered separately, because somebody arrives at one of them rather than at the top of the page.
The job it was built for, and the job it is not for.
Qwen3 8B is a causal language model of roughly eight billion parameters that can switch between a "thinking" mode for maths and step-by-step problems and a faster mode for normal conversation. The job it was built for is running reasoning workloads at small-model cost, on your own hardware if you want it there. It handles text only, so no image, audio or document-vision work.
Where it sits in its line, and when a sibling is the better pick.
Qwen3 8B first appeared in April 2025 as one of the smaller dense members of the Qwen3 family. It is the right pick when you want Qwen3 behaviour at a size you can self-host or run cheaply at volume, and when the task benefits from optional step-by-step reasoning. It is the wrong pick for anything needing images or long generated outputs, and for the hardest reasoning tasks, where the larger Qwen3 models earn their extra cost.
The API, the apps it powers, and what its limits let you do.
Qwen3 8B is reached through the Qwen API and through the hosting providers that serve open Qwen weights, and the weights themselves can be run on your own infrastructure. Its context window is large enough to hold long documents, transcripts or a full retrieval payload in a single call, while the output ceiling keeps it pointed at answers, summaries and structured responses rather than long-form drafting. Mode switching is handled per request, so the same deployment serves both a chat assistant and a reasoning step in a pipeline.
What changes because this exists, or why it does not.
The practical change is that reasoning-style generation stops being something you reserve for a frontier model. A team can put a thinking-mode step inside a high-volume pipeline, classification, extraction, query rewriting, answer checking, and absorb the cost, or run the whole thing in their own environment where data cannot leave. For anyone already using small open models, it is an incremental upgrade rather than a new category.
Follows Qwen3 32B. Superseded by Qwen3.7 Flash. See the whole line.
03 / evidence
Split by category, so a strong number never hides a thin evidence base. Verified means we read it on the benchmark's own published results; a provider's claim about its own model is shown and labelled rather than dropped.
No published benchmark scores for this model yet.
We publish a score only where we can link the result to where it was published. Until a benchmark result for this model exists in a source we read, this section stays empty rather than being filled with a provider's marketing figure.
How we decide what counts as evidence04 / ledger
Every published row, grouped by category, each compared with the best published score on the same benchmark. 'Is 64% good' is a question nobody can answer; '26 points behind the leader' is one anybody can.
Nothing in the ledger yet.
Each row here carries a score, the benchmark it came from, what the leading model scored on the same test, and a link to the published result. Rows appear as results are published and read.
How we decide what counts as evidence05 / capability
Where this model is strong, and against how many peers. Ranks are against models with evidence in that category, not against everything we track: ranking against models nobody tested would rank who published, not who is better.
No category scores to shape yet.
A category score is the weighted mean of the benchmarks published for it. With no published rows there is nothing to average, and an empty chart drawn at zero would say something false.
How we decide what counts as evidence06 / cost
List API rates as last read from the provider, with the source on every row, plus every change we have recorded since we started tracking it.
| Charge | Price | Unit | Read on | Source |
|---|---|---|---|---|
| Input | $0.12 | per 1M tokens | 2026-09-24 | Check |
| Output | $0.46 | per 1M tokens | 2026-09-24 | Check |
Qwen3 8B sits at the cheap end of the hosted market, with output priced at a few multiples of input, as is standard. Running it at volume costs a fraction of a frontier model, and because the weights are open, the hosted price is effectively a ceiling rather than the only option.
| Workload | Input / month | Output / month | Cost |
|---|---|---|---|
| A small product team | 20M tokens | 5M tokens | $4.62 |
| A busy support assistant | 200M tokens | 40M tokens | $41.60 |
| A document pipeline | 1000M tokens | 100M tokens | $162.50 |
List API rates, no caching and no batch discount, which both providers offer and which change the answer a great deal. Treat these as the ceiling, not the bill.
07 / specs
As listed by the provider's own catalogue and re-read every few hours. Anything absent is absent there too.
| Context window | 131,072 tokens |
|---|---|
| Maximum output | 8,192 tokens |
| Modalities | text |
| Released | 28/04/2025 |
| Status | Current |
| Catalogue identifier | qwen/qwen3-8b |
08 / lineage
What this model replaced, what replaced it, and what else its provider has in the field.
Also from Qwen
09 / line
Every model in this family in release order, so a page from eight months ago says in one glance that two newer ones exist.
Came before
← Qwen3 32BCame after
Qwen3.7 Flash →Ordered by release date and worked out from the naming, so a new member slots in as soon as its page exists. A retired model keeps its page and its place in the line.
10 / notes
What this model changes for a brand trying to be cited in AI answers, and every change we have logged since it launched.
What it changes for you
Models in this class are where a lot of production retrieval and summarisation actually runs, so the way your content is written matters as much as which frontier model is in the headlines. A smaller model has less spare capacity to reconcile vague or scattered claims, so it leans harder on clean, self-contained statements of what you do, who you serve and what you cost. If your positioning only makes sense after reading three pages, it will not survive the summarisation step.
Buyers rarely meet Qwen3 8B by name. They meet it inside products built on it: support assistants, in-app search, summarisation features and internal tools where a vendor chose a cheap self-hostable model, plus the Qwen API and third-party inference platforms that serve open weights.
Nothing published here yet. Changes appear within hours of a provider announcing them.
11 / questions
The things people ask about this model, answered from what is on this page rather than from anywhere else.
Models change what gets cited. We measure whether AI answers name your brand or your competitors across every assistant, and show you what to change.