DeepSeek: DeepSeek V4.1 Flash 1017.
Minor update to DeepSeek-V4.1-Flash with improved reasoning, coding, agent and vision results and shorter outputs, open weights under the MIT licence.
4× AMD MI300X · $2.00/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 DeepSeek family.
DeepSeek V3
DeepSeek's flagship MoE — 671B total, 37B active, frontier-class.
DeepSeek R1
DeepSeek's reasoning model — RL-trained, frontier-class, MIT-licensed.
DeepSeek R1 Distill Llama 70B
70B Llama distilled from DeepSeek R1's reasoning traces.
DeepSeek R1 Distill Qwen 32B
32B Qwen base distilled from DeepSeek R1.
DeepSeek R1 Distill Qwen 14B
14B distilled R1 — laptop-friendly reasoning.
DeepSeek R1 Distill Qwen 7B
7B distilled R1 — runs on any modern GPU.
DeepSeek R1 Distill Qwen 1.5B
Tiny distilled R1 — phone / browser deployable.
DeepSeek: DeepSeek V4 Pro 0813
Official release of DeepSeek-V4-Pro that supersedes the preview, adding a spe...
DeepSeek: DeepSeek V4 Flash 0731
DeepSeek V4 Flash 0731 is a sparse mixture-of-experts model from DeepSeek, wi...
DeepSeek-V3.1
DeepSeek-V3.1 is post-trained on the top of DeepSeek-V3.1-Base, which is buil...
DeepSeek: DeepSeek V3 0324
DeepSeek V3, a 685B-parameter, mixture-of-experts model, is the latest iterat...
DeepSeek: DeepSeek V3.1 Terminus
DeepSeek-V3.1 Terminus is an update to [DeepSeek V3.1](/deepseek/deepseek-cha...
DeepSeek: DeepSeek V3.2
DeepSeek-V3.2 is a large language model designed to harmonize high computatio...
DeepSeek: DeepSeek V3.2 Speciale
DeepSeek-V3.2-Speciale is a high-compute variant of DeepSeek-V3.2 optimized f...
DeepSeek: DeepSeek V4 Flash
Open-weights 284B Mixture-of-Experts text model with 13B active parameters an...
DeepSeek: DeepSeek V4 Pro
Open-weights 1.6T Mixture-of-Experts text model with 49B active parameters an...
DeepSeek: R1 0528
May 28th update to the [original DeepSeek R1](/deepseek/deepseek-r1) Performa...
DeepSeek: DeepSeek V4.1 Flash
Open-weights multimodal 552B Mixture-of-Experts model that reads images and t...
DeepSeek: DeepSeek V4 Flash Vision Exp
DeepSeek-V4-Flash-Vision-Exp is an experimental MIT-licensed model adding ima...
DeepSeek: DeepSeek V3.2 Exp
DeepSeek-V3.2-Exp is an experimental large language model released by DeepSee...
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)
|
2048 GB | $25.36/hr | — | Compare → | |
|
FP8
FP8 — 8-bit float (Hopper / Blackwell)
|
1536 GB | $4.00/hr | — | Compare → | |
|
INT4
INT4 — 4-bit integer (~4× VRAM saving)
|
768 GB | $2.00/hr | — | Compare → |
What it costs per month across providers.
Estimate your monthly bill for DeepSeek: DeepSeek V4.1 Flash 1017 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 DeepSeek: DeepSeek V4.1 Flash 1017, 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 DeepSeek: DeepSeek V4.1 Flash 1017.
A minor update to DeepSeek-V4.1-Flash, the same multimodal model that reads images and text and writes text, with weights under the MIT licence and a one million token context. DeepSeek says the update improves reasoning, coding, agent tasks and visual understanding, and also shortens task execution time and output length. It keeps the base model's architecture and download format, so existing serving setups for V4.1-Flash apply.