DeepSeek-R1

Language DeepSeek Partly verified Released Jan 20, 2025
  • In Input modalities: Text Out Output modalities: Text
  • Reasoning
  • Tool use
  • Structured output
  • Open weights

Prices

4 offerings · prices as listed
DeepSeek API (opens in a new tab) Official China · CNY deepseek-r1
Conditions/ 1M tokensInputCache readOutput
Standard ¥4.00¥1.00¥16.00

Prices from DeepSeek | DeepSeek-R1 发布,性能对标 OpenAI o1 正式版 (opens in a new tab) (checked Nov 5, 2026)

Alibaba Cloud Model Studio (opens in a new tab) Official Available on this channel since Jun 18, 2025 Global · USD Context: 131.07KReasoning: YesTool use: YesStructured output: —Max output: 16.38KInput modalities: TextOutput modalities: TextMax thinking budget: 32.77K deepseek-r1
Conditions/ 1M tokensInputOutput
Standard $0.574$2.294

Prices from Alibaba Cloud Model Studio model pricing - Alibaba Cloud Model Studio - Alibaba Cloud Documentation Center (opens in a new tab) (checked Sep 29, 2026)

Alibaba Cloud Model Studio (opens in a new tab) Official Available on this channel since Jun 18, 2025 China · CNY Context: 131.07KReasoning: YesTool use: YesStructured output: —Max output: 16.38KInput modalities: TextOutput modalities: TextMax thinking budget: 32.77K deepseek-r1
Conditions/ 1M tokensInputOutput
Standard no longer listed since Oct 11, 2026 ¥4.00¥16.00

Prices from 阿里云百炼:模型价格 (opens in a new tab) (checked Nov 2, 2026)

Alibaba Cloud Model Studio (opens in a new tab) Official Available on this channel since Jun 18, 2025 China · CNY vanchin/deepseek-r1
Conditions/ 1M tokensInputOutput
Standard ¥4.00¥16.00

Prices from 阿里云百炼:模型价格 (opens in a new tab) (checked Nov 2, 2026)

Standard: the price a request with no conditions pays (the headline price elsewhere on the site).

Details

Max thinking budget
32.77K
Knowledge cutoff
Jul 2024
Licence
MIT License
Family
deepseek-r
Model ID

Not stated on DeepSeek’s pages: max input, reasoning efforts, reasoning can be off, built-in tools, aliases.

Run locally

Weights: deepseek-ai/DeepSeek-R1
  • Parameters 684.5B
  • Active 51B (estimated) · 256 experts, 8 per token
  • Native precision FP8
  • Layers 61
  • KV cache per token 69 KB
  • Config positions 160K (config.json max_position_embeddings)
  • API context 63K (maker’s API)
My hardware GeForce RTX 4090 24 GB · 1008 GB/s · 64 GB DDR5 dual channel · 89.6 GB/s
On your hardware · GeForce RTX 4090
Doesn’t fit IQ4_XS (automatic) 8K context
WeightsKV cacheOverheadUsable
Needs
≈ 356.1 GB (weights 338.7 + KV cache 0.5 + overhead 16.9)
Usable
21.6 GB (90% of its memory)
Try
it’s too large for this hardware; see the smallest that runs it below
PrecisionWeightsNeeds at 8KOn your hardwareLongest context
FP8 (native)maker’s file641.3 GB≈ 673.9Doesn’t fit—
Q6_K523.5 GB≈ 550.2Doesn’t fit—
Q5_K_M453.4 GB≈ 476.6Doesn’t fit—
Q5_0438.3 GB≈ 460.7Doesn’t fit—
Q4_K_M387.3 GB≈ 407.2Doesn’t fit—
Q4_0358.6 GB≈ 377.0Doesn’t fit—
IQ4_XS338.7 GB≈ 356.1Doesn’t fit—
Q3_K_Mquality drops311.6 GB≈ 327.7Doesn’t fit—
IQ3_XXSquality drops243.8 GB≈ 256.6Doesn’t fit—
Q2_Kquality drops239.1 GB≈ 251.5Doesn’t fit—
IQ2_XXSquality drops164.2 GB≈ 172.9Doesn’t fit—

Click a row to choose a precision. “Maker’s file” sizes are the maker’s own files; others are parameters × bits per weight.

Compared with other hardware

Each at the highest precision it runs. Click one for details.

Smallest that runs it (Q4_K_M, 8K): no single listed machine; about 6 × 80 GB GPUs.

Run commands

The maker publishes no files at this precision. Choose its native precision or one of its own builds, or quantise it yourself with llama.cpp.

Memory to fine-tune
Full≈ 10203.3 GBDoesn’t fit your hardware
LoRA≈ 1279.4 GBDoesn’t fit your hardware
QLoRA≈ 363.0 GBDoesn’t fit your hardware

Batch 1, 2,048-token sequences, gradient checkpointing; LoRA and QLoRA train rank-16 adapters on the attention projections.

Estimates; facts from the maker’s Hugging Face repositories, as of 2026-11-06. How it’s estimated

Cited scores

From third-party leaderboards, not measured by us
  • Epoch Capabilities Index 138.6

    #100 of 118 listed models · CI 136.8–140.4

  • LMArena Text 1,398

    #100 of 163 listed models · CI 1,393–1,403 · 18,524 votes

  • Epoch AI · GPQA Diamond 69.2%

    #98 of 168 listed models · ±3.07

  • Epoch AI · MATH Level 5 93.1%

    #12 of 64 listed models · ±0.7

  • Epoch AI, 'Capabilities & benchmarking'. Published online at epoch.ai. Retrieved from 'https://epoch.ai/benchmarks'. Licensed CC BY 4.0. · License · as of Oct 18 – Nov 5, 2026
  • LMArena (Arena), leaderboard-dataset on Hugging Face, licensed CC BY 4.0 · License · as of Oct 28, 2026
  • EQ-Bench by Samuel J. Paech (eqbench.com; EQ-bench/EQ-bench-site on GitHub), MIT License. · License · as of Oct 28, 2026
  • changes we made
  1. Now priced on DeepSeek API Input ¥4.00 · Output ¥16.00 · Cache read ¥1.00
  2. Now priced on Alibaba Cloud Model Studio, China Input ¥4.00 · Output ¥16.00
  3. Context window 128K → 131.07K

Sources

DeepSeek’s official policies: data use, privacy, terms →

Some values on this page are still waiting to be checked against official pages: a filled dot is verified, a hollow one is not yet (with or without a source on file). How we verify data

DeepSeek · China · deepseek.com

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