Qwen: Qwen3.8 27B.
Qwen3.8-27B is a 27B dense Apache 2.0 model with image and video input and a 262K-token context, aimed at coding, research and long-running agents.
1× Nvidia GeForce RTX 3090 · $0.15/hr.
Most-aggressive quantisation we have a working recommendation for. Lower precision = less VRAM = cheaper hardware, at a small accuracy cost.
Cheapest hosted endpoints.
Variants in the Qwen family.
Qwen3.8-Flash is Qwen's official API model built on Qwen3.8-Flash-Next, with ...
Alibaba's flagship open-weight LLM — 72B dense.
Qwen2.5-7B-Instruct is a 7.61B instruction-tuned open-weights text model unde...
14B Qwen 2.5 — sweet spot for single-GPU local hosting.
32B Qwen 2.5 — laptop-class workhorse.
3B Qwen 2.5 — laptop / edge target.
Alibaba's frontier MoE — 235B total / 22B active.
Qwen3.8-2.4T-A95B is a text mixture-of-experts model, 2.4T total and 95B acti...
Qwen3-32B is a dense 32.8B open-weights text model under Apache 2.0 that swit...
Qwen3-14B is a dense 14.8B open-weights text model under Apache 2.0 that swit...
Qwen3-8B is a dense 8.2B parameter causal language model from the Qwen3 serie...
Qwen3.8 Max is the flagship model in Alibaba's Qwen3.8 series, the general-av...
Qwen3.7 Flash is a vision-language reasoning model from Alibaba. It is suited...
Qwen3.7-Plus is an Alibaba model with image, video and text input and a 1M-to...
Qwen3.8 Max 0902 is an updated snapshot of Qwen3.8 Max from Alibaba's Qwen te...
Qwen3.5-0.8B is Alibaba's smallest model in the Qwen3.5 series, featuring a h...
Qwen3.5-2B is a compact yet capable model from Alibaba's Qwen3.5 series. It f...
Qwen3.5-4B is a mid-size model from Alibaba's Qwen3.5 series that delivers a ...
Qwen Plus 0728, based on the Qwen3 foundation model, is a 1 million context h...
Qwen-Plus is the Plus model of Alibaba's Qwen3 series, text only with a 1M-to...
Qwen3-235B-A22B-Instruct-2507 is a multilingual, instruction-tuned mixture-of...
Qwen3-235B-A22B-Thinking-2507 is a high-performance, open-weight Mixture-of-E...
Qwen3-30B-A3B is an Apache 2.0 text model with 30.5B total and 3.3B active pa...
Qwen3-30B-A3B-Instruct-2507 is a 30.5B-parameter mixture-of-experts language ...
Qwen3-30B-A3B-Thinking-2507 is a 30B parameter Mixture-of-Experts reasoning m...
Qwen3-Max is an updated release built on the Qwen3 series, offering major imp...
Qwen3-Next-80B-A3B-Instruct is an instruction-tuned chat model in the Qwen3-N...
Qwen3-Next-80B-A3B-Thinking is a reasoning-first chat model in the Qwen3-Next...
The Qwen3.5 series 397B-A17B native vision-language model is built on a hybri...
The Qwen3.5 native vision-language series Plus models are built on a hybrid a...
Qwen3.5-Plus 2026-04-20 is a dated snapshot with image, video and text input ...
The Qwen3.5 122B-A10B native vision-language model is built on a hybrid archi...
The Qwen3.5 27B native vision-language Dense model incorporates a linear atte...
The Qwen3.5 Series 35B-A3B is a native vision-language model designed with a ...
Qwen3.5-9B is a multimodal foundation model from the Qwen3.5 family, designed...
The Qwen3.5 native vision-language Flash models are built on a hybrid archite...
Qwen3.6-27B is a 27B dense open-weights model, Apache 2.0, with image and tex...
Qwen3.6-35B-A3B is an Apache 2.0 mixture-of-experts model, 35B total and 3B a...
Qwen3.6-Flash is a vision-language model on Qwen's API that takes text, image...
Qwen 3.6 Plus builds on a hybrid architecture that combines efficient linear ...
Qwen3.7-Max is Alibaba's largest Qwen3.7 model, a text-only model with no dow...
Qwen3.6-Max-Preview is a proprietary frontier model from Alibaba Cloud built ...
Cheapest hardware per quantisation.
Each row is one quantisation tier (the same weights compressed differently). Lower precision → lower VRAM → cheaper hardware, at the cost of small accuracy loss. $/hr refreshed hourly from each provider's API.
| Quantisation | Cheapest GPU config | Total VRAM | Live $/hr | tokens/sec | |
|---|---|---|---|---|---|
|
FP16
FP16 — half precision (default)
|
192 GB | $0.50/hr | 68.7 | Compare → | |
|
FP8
FP8 — 8-bit float (Hopper / Blackwell)
|
48 GB | $0.27/hr | 17.4 | Compare → | |
|
INT4
INT4 — 4-bit integer (~4× VRAM saving)
|
24 GB | $0.15/hr | 48.5 | Compare → |
What it costs per month across providers.
Estimate your monthly bill for Qwen: Qwen3.8 27B across every host that publishes per-token pricing. Slide your token volumes; the chart + table re-rank cheapest-first.
Cheapest provider on the left.
Total monthly cost — input + output tokens combined.
Bill breakdown.
Rent the GPU instead of paying per token.
For an open-weights model like Qwen: Qwen3.8 27B, you can rent a GPU and serve inference yourself. The math: cheapest GPU rental × 730 hours/month + your electricity rate × power draw.
Assumes the GPU runs 24/7 at ~85% utilisation. If your traffic is bursty, you'll pay less for the API and probably more for the GPU (idle hours still cost rental). The breakeven analysis lives on the Self-host vs API breakeven tool.
About Qwen: Qwen3.8 27B.
Qwen3.8-27B is an Apache 2.0 open-weights dense model with 27 billion parameters and a built-in vision encoder for images and video, including hour-scale video. Its context is 262,144 tokens natively and can be extended to about 1M with RoPE scaling. Qwen aims it at coding, professional work, research and long-running agents. Thinking is on by default, can be turned off per request, and its depth can be tuned. A hosted version with a 1M default context was listed as coming soon.