Qwen3 14B — Specifications
| Developer | Alibaba |
|---|---|
| Type | LLM (dense) |
| Modality | Text → Text |
| Parameters | 14B |
| Context window | 128K |
| Max output | — |
| License | Apache 2.0 (open) |
| Open weights | Yes |
| Released | 2025 |
| Input price | $0.12 /1M |
| Output price | $0.24 /1M |
| API providers | Alibaba, OpenRouter, Ollama |
Run it locally
| VRAM (4-bit) | ~9 GB |
|---|---|
| Minimum GPU | RTX 4070 12GB (Q4) |
What is Qwen3 14B?
Qwen3 14B is a mid-size dense model from Alibaba’s Qwen3 family — Apache 2.0, a 128K
context, and roughly 9 GB of VRAM at 4-bit, which puts it on an RTX 4070 12GB. Hosted pricing
is $0.12 in / $0.24 out per million tokens.
It sits at the point where a local model stops feeling like a demo. The 8B tier is fine
for classification and short generation but starts to show its limits on multi-step
instructions; the 32B tier needs a 24 GB card that most developer machines do not have.
Qwen3 14B is the largest of the family that still fits a mainstream 12 GB GPU, and being
dense rather than mixture-of-experts, its memory requirement is predictable — 9 GB is 9 GB,
with no gap between active and resident parameters to catch you out when sizing hardware.
Apache 2.0 licensing means no conditions to review, and a 128K context is enough for
realistic document work. For a team building its first genuinely useful on-premises
deployment, this is a sensible default.
Qwen3 14B pricing: API cost per 1M tokens
| Input (per 1M tokens) | $0.120 |
|---|---|
| Output (per 1M tokens) | $0.240 |
| Output/input ratio | 2× |
| Blended (4:1 in:out) | $0.144 per 1M tokens |
What Qwen3 14B costs per month
Real monthly spend at a 4:1 input-to-output mix — the ratio a typical chat or RAG workload actually produces.
| Workload | Tokens / month | Cost / month |
|---|---|---|
| Side project | 1M in / 0.25M out | $0.18 |
| Small team | 20M in / 5M out | $3.60 |
| Production | 200M in / 50M out | $36 |
Run your own numbers in the AI API cost calculator.
Cheaper alternatives to Qwen3 14B
| Model | Blended $/1M | You save |
|---|---|---|
| Mistral 7B open | $0.0220 | 85% cheaper |
| Llama 3.1 8B open | $0.0220 | 85% cheaper |
| Mistral NeMo 12B open | $0.0240 | 83% cheaper |
Self-host or pay the API?
Qwen3 14B is open-weight, so you can run it yourself. It needs ~9 GB of VRAM at 4-bit (RTX 4070 12GB (Q4)). Self-hosting only beats the API once your volume is high enough to keep that hardware busy — the self-hosting vs API calculator works out the break-even point for your token volume.
Frequently asked questions
How much does Qwen3 14B cost per 1M tokens?
Qwen3 14B costs $0.120 per 1M input tokens and $0.240 per 1M output tokens. At a typical 4:1 input-to-output mix that blends to about $0.144 per 1M tokens.
How much does Qwen3 14B cost per month?
A small-team workload of 20M input and 5M output tokens a month costs about $3.60 on Qwen3 14B. A side project (1M in / 0.25M out) costs roughly $0.18.
What is a cheaper alternative to Qwen3 14B?
Mistral 7B is the strongest cheaper option in our database at $0.0220 per 1M blended — about 85% less than Qwen3 14B. It is also open-weight, so self-hosting is an option.
Can I run Qwen3 14B locally?
Yes. Qwen3 14B is open-weight and needs about ~9 GB of VRAM at 4-bit quantisation (RTX 4070 12GB (Q4)).
Why does Qwen3 14B charge more for output than input?
Output tokens are generated one at a time and cannot be batched the way a prompt can, so they cost the provider more to serve. Qwen3 14B charges 2× more for output, which is why prompt-heavy workloads are far cheaper to run than generation-heavy ones.
Prices are the published list rates for the model's primary API and are reviewed as providers change them. Volume, batch and cached-input discounts are not included. Compare every model side by side in the AI models database or the LLM leaderboard.
