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Free AI Visibility AuditQwen3 14B is a dense open-weight language model from Qwen's Qwen3 series, built to handle both step-by-step reasoning and ordinary dialogue in one model by switching between a "thinking" mode and a faster direct mode.
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
$0.24
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 14B is a text-only causal language model in the mid-size band of the Qwen3 family, sized to run complex reasoning work without the cost of a frontier model. Its defining feature is the ability to switch between a deliberate thinking mode and a straight conversational mode, so one deployment covers both analysis and chat. It is not a multimodal model, so images, audio and video are out of scope.
Where it sits in its line, and when a sibling is the better pick.
Qwen3 14B appeared in April 2025 as part of the Qwen3 release. It sits in the middle of the series, above the small models that are cheap but limited on reasoning, and below the larger Qwen3 variants. Pick it when you want reasoning quality on a modest budget, or when you are self-hosting and want something that fits a single sensible GPU. It is the wrong pick when you need vision or audio input, or when the task genuinely needs the largest model in the family.
The API, the apps it powers, and what its limits let you do.
Qwen3 14B is reached through API providers that host the Qwen3 series, and the weights are open so it can also be run on your own infrastructure. It is text in, text out, with a long context window that comfortably holds large documents, long chat histories or sizeable codebases in a single request. The per-request output cap is more modest than the input allowance, so it suits analysing a lot and replying concisely rather than generating book-length output.
What changes because this exists, or why it does not.
The practical change is that reasoning and fast dialogue no longer need two separate models behind one product. A team can route a support conversation and a multi-step analysis task to the same endpoint and toggle the mode, which removes a routing layer and a second set of prompts to maintain. For anyone running models themselves, a dense model at this size is a straightforward deployment rather than a cluster project.
Superseded by Qwen3 235B A22B. 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 | $0.24 | per 1M tokens | 2026-09-24 | Check |
Qwen3 14B is priced in the cheap tier of hosted models, with output costing double the input, which is the usual shape. Against frontier chat models it is a fraction of the cost, and because the weights are open the hosted price is really a ceiling, self-hosting at volume takes it lower still.
| Workload | Input / month | Output / month | Cost |
|---|---|---|---|
| A small product team | 20M tokens | 5M tokens | $3.60 |
| A busy support assistant | 200M tokens | 40M tokens | $33.60 |
| A document pipeline | 1000M tokens | 100M tokens | $144.00 |
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 | 16,384 tokens |
| Modalities | text |
| Released | 28/04/2025 |
| Status | Current |
| Catalogue identifier | qwen/qwen3-14b |
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
The first we track in this line.
Came after
Qwen3 235B A22B →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
Open models like Qwen3 14B get embedded quietly into other companies' assistants and internal tools, so your brand can be cited in answers generated by a model you never see in a logo wall. What it rewards is the same thing every retrieval-backed system rewards: clear, self-contained pages that state what your product does and who it is for, because a mid-size model has less general knowledge to fall back on and leans harder on what it is given. Assume the retrieved page is doing most of the work, and write it accordingly.
Buyers rarely meet Qwen3 14B by name. They meet it through the API at aggregators and inference hosts that carry the Qwen3 series, inside products whose vendors chose an open model for cost or data-residency reasons, and on self-hosted deployments behind an internal assistant or a support widget.
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.