DeepSeek, proprietary

# DeepSeek-R1

> DeepSeek-R1 by DeepSeek, released January 2025. Ranked #115 of 354 with a Noometry Index of 42.3. API: $0.50 in / $2.15 out per M tokens. 164K context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/deepseek-r1
- Last updated: 2026-10-10
- Title: DeepSeek-R1 Benchmarks, Price & Rank (October 2026)

DeepSeek-R1 by DeepSeek ranks 115th of 354 ranked models on the Noometry Index as of October 2026, with a score of 42.3. Its strongest category is long context, where it ranks 36th. API pricing starts at $0.50 per million input tokens and $2.15 per million output tokens, with a 164K-token context window.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #115 of 354
- **Index score:** 42.3
- **Evidence:** Confirmed 52 results
- **Provider:** [![](/logos/deepseek.svg) DeepSeek](https://noometry.com/providers/deepseek)
- **Released:** January 20, 2025
- **Weights:** Proprietary
- **Reasoning:** Yes
- **Context window:** 164K
- **Max output:** 64K
- **Input price:** $0.50 / M
- **Output price:** $2.15 / M
- **Blended price:** $0.91 / M
- **Output speed:** 10 tokens/s [Kagi](https://help.kagi.com/kagi/ai/llm-benchmark.html)
- **Value:** #101 of 219
- **Knowledge cutoff:** July 2024
- **Input:** text
- **Hugging Face:** [deepseek-ai/DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1)

## Category scores

Each category score combines every public result we have in that category.

DeepSeek-R1 category scores

1.  Coding 46.3
2.  Agentic & Tool Use 30.7
3.  Reasoning 18.6
4.  Math 43.8
5.  Knowledge 44.5
6.  Multilingual 52.4
7.  Instruction Following 72.0
8.  Long Context 45.4
9.  Writing & Preference 61.4
10.  020406080

DeepSeek-R1 category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 46.3 | #68 | 5 |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 30.7 | #75 | 2 |
| [Reasoning](https://noometry.com/best/reasoning) | 18.6 | #278 | 8 |
| [Math](https://noometry.com/best/math) | 43.8 | #79 | 5 |
| [Knowledge](https://noometry.com/best/knowledge) | 44.5 | #87 | 6 |
| [Multilingual](https://noometry.com/best/multilingual) | 52.4 | #85 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 72.0 | #143 | 3 |
| [Long Context](https://noometry.com/best/long-context) | 45.4 | #36 | 2 |
| [Writing & Preference](https://noometry.com/best/writing) | 61.4 | #88 | 7 |

## Strengths and weaknesses

Categories where DeepSeek-R1 places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

DeepSeek-R1: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Long Context](https://noometry.com/best/long-context) | 45.4 | +4.5 | #36 of 296, top 13% |
| [Coding](https://noometry.com/best/coding) | 46.3 | +7.6 | #68 of 340, top 20% |
| [Math](https://noometry.com/best/math) | 43.8 | +7.2 | #79 of 327, top 25% |

### Weakest categories

DeepSeek-R1: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Reasoning](https://noometry.com/best/reasoning) | 18.6 | −5.0 | #278 of 350, top 80% |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 30.7 | +0.3 | #75 of 154, top 49% |
| [Instruction Following](https://noometry.com/best/instruction-following) | 72.0 | +0.7 | #143 of 305, top 47% |

## Closest competitors

The models ranked just above and below DeepSeek-R1. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to DeepSeek-R1
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [GPT-5.2 Codex](https://noometry.com/models/gpt-5-2-codex) | #111 | 42.6 | $4.81 | — | [Compare](https://noometry.com/compare/deepseek-r1-vs-gpt-5-2-codex) |
| [Qwen3.5-Flash](https://noometry.com/models/qwen3-5-flash) | #112 | 42.5 | $0.18 | — | [Compare](https://noometry.com/compare/deepseek-r1-vs-qwen3-5-flash) |
| [Nemotron 3 Ultra](https://noometry.com/models/nemotron-3-ultra) | #113 | 42.5 | $0.93 | — | [Compare](https://noometry.com/compare/deepseek-r1-vs-nemotron-3-ultra) |
| [Hunyuan T1 20250711](https://noometry.com/models/hunyuan-t1) | #114 | 42.5 | — | — | [Compare](https://noometry.com/compare/deepseek-r1-vs-hunyuan-t1) |
| [Step 3.5 Flash](https://noometry.com/models/step-3-5-flash) | #116 | 42.3 | $0.15 | — | [Compare](https://noometry.com/compare/deepseek-r1-vs-step-3-5-flash) |
| [Qwen3.6 27B](https://noometry.com/models/qwen3-6-27b) | #117 | 42.2 | $1.35 | — | [Compare](https://noometry.com/compare/deepseek-r1-vs-qwen3-6-27b) |
| [Amazon Nova Experimental Chat 10 20](https://noometry.com/models/amazon-nova-experimental-chat-10-20) | #118 | 42.1 | — | — | [Compare](https://noometry.com/compare/amazon-nova-experimental-chat-10-20-vs-deepseek-r1) |
| [Qwen3.5 122B-A10B](https://noometry.com/models/qwen3-5-122b-a10b) | #119 | 42.1 | $1.10 | — | [Compare](https://noometry.com/compare/deepseek-r1-vs-qwen3-5-122b-a10b) |

Sponsored placements are available on pages like this one. [Advertise on Noometry](https://noometry.com/advertise)

## Benchmark results

Every published result we track, with its source. Bold rows are the ones used for ranking; where several exist we prefer independent runs over self-reported numbers.

### Coding

DeepSeek-R1 Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Aider Polyglot](https://noometry.com/benchmarks/aider-polyglot) | 56.9% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Aider Polyglot](https://noometry.com/benchmarks/aider-polyglot) | 71.4% | #9 of 44, top 21% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 35.7% | #97 of 121, top 81% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 36.5% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 41.6% | #72 of 119, top 61% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LiveBench Coding](https://noometry.com/benchmarks/livebench-coding) | 66.7% | #10 of 39, top 26% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1427 | #112 of 294, top 39% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1371 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [ALE-Bench](https://noometry.com/benchmarks/ale-bench) | 804.12 | #56 of 105, top 54% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [AlgoTune](https://noometry.com/benchmarks/algotune) | 1.7 | #7 of 18, top 39% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Agentic & Tool Use

DeepSeek-R1 Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [DeepResearch Bench](https://noometry.com/benchmarks/deepresearch-bench) | 35.1% | #23 of 24, top 96% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [BALROG](https://noometry.com/benchmarks/balrog) | 34.9% | #12 of 35, top 35% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [METR Time Horizons](https://noometry.com/benchmarks/metr-time-horizons) | 51.9% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [METR Time Horizons](https://noometry.com/benchmarks/metr-time-horizons) | 53.8% | #22 of 32, top 69% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Reasoning

DeepSeek-R1 Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 1.3% | #66 of 83, top 80% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 1.1% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SimpleBench](https://noometry.com/benchmarks/simplebench) | 40.8% | #53 of 77, top 69% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SimpleBench](https://noometry.com/benchmarks/simplebench) | 30.9% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Kagi LLM Benchmark](https://noometry.com/benchmarks/kagi-reasoning) | 69.4% | #25 of 99, top 26% |  | [Kagi LLM Benchmark](https://help.kagi.com/kagi/ai/llm-benchmark.html) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 15.8% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 21.2% | #68 of 83, top 82% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 1.1% | #82 of 134, top 62% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LiveBench Reasoning](https://noometry.com/benchmarks/livebench-reasoning) | 83.2% | #7 of 39, top 18% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1416 | #111 of 297, top 38% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1361 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LiveBench Data Analysis](https://noometry.com/benchmarks/livebench-data-analysis) | 69.8% | #5 of 39, top 13% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 141.29 | #104 of 213, top 49% |  | [Epoch AI](https://epoch.ai/eci) | 2025-05-28 |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 138.97 |  |  | [Epoch AI](https://epoch.ai/eci) | 2025-01-20 |
| [ForecastBench](https://noometry.com/benchmarks/forecastbench) | 60 | #33 of 72, top 46% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LiveBench](https://noometry.com/benchmarks/livebench) | 71.6% | #7 of 39, top 18% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Math

DeepSeek-R1 Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 66.4% | #98 of 173, top 57% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-05-29 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 53.3% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-02-26 |
| [Omni-MATH](https://noometry.com/benchmarks/omni-math) | 42.4% | #23 of 57, top 41% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LiveBench Math](https://noometry.com/benchmarks/livebench-math) | 80.7% | #3 of 39, top 8% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1393 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1400 | #125 of 285, top 44% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [MATH Level 5](https://noometry.com/benchmarks/math-level-5) | 96.6% | #7 of 79, top 9% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-05-29 |
| [MATH Level 5](https://noometry.com/benchmarks/math-level-5) | 93.1% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-01-31 |

### Knowledge

DeepSeek-R1 Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 76.3% | #88 of 186, top 48% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-05-29 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 71.7% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-05-26 |
| [MMLU-Pro](https://noometry.com/benchmarks/mmlu-pro) | 79.3% | #18 of 58, top 32% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [Confabulations](https://noometry.com/benchmarks/confabulations) (lower is better) | 12.7% | #9 of 51, top 18% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/confabulations) |  |
| [Confabulations](https://noometry.com/benchmarks/confabulations) (lower is better) | 14.6% |  |  | [Lech Mazur benchmarks](https://github.com/lechmazur/confabulations) |  |
| [Vectara Hallucination Rate](https://noometry.com/benchmarks/vectara-hallucination) (lower is better) | 11.3% | #68 of 96, top 71% |  | [Vectara Hallucination Leaderboard](https://github.com/vectara/hallucination-leaderboard) |  |
| [GPQA (HELM)](https://noometry.com/benchmarks/helm-gpqa) | 66.6% | #15 of 57, top 27% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1394 | #131 of 273, top 48% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1338 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Multilingual

DeepSeek-R1 Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1412 | #85 of 297, top 29% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1357 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1400 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1442 | #110 of 285, top 39% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1417 | #105 of 223, top 48% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1366 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1404 | #92 of 231, top 40% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1384 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1324 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1391 | #68 of 211, top 33% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1329 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1360 | #94 of 213, top 45% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1354 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1423 | #76 of 283, top 27% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1411 | #100 of 226, top 45% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1379 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

DeepSeek-R1 Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LiveBench Instruction Following](https://noometry.com/benchmarks/livebench-if) | 80.5% | #11 of 39, top 29% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [IFEval](https://noometry.com/benchmarks/ifeval) | 78.4% | #45 of 57, top 79% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1357 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1382 | #120 of 298, top 41% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

DeepSeek-R1 Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Fiction.LiveBench](https://noometry.com/benchmarks/fiction-livebench) | 69.4% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Fiction.LiveBench](https://noometry.com/benchmarks/fiction-livebench) | 75% | #14 of 47, top 30% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1391 | #125 of 291, top 43% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1355 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

DeepSeek-R1 Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1428 | #81 of 297, top 28% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1373 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1354 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1405 | #68 of 295, top 24% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [Short-Story Creative Writing](https://noometry.com/benchmarks/lech-mazur-writing) | 81.9% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Short-Story Creative Writing](https://noometry.com/benchmarks/lech-mazur-writing) | 83% | #9 of 39, top 24% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [EQ-Bench Creative Writing](https://noometry.com/benchmarks/eqbench-creative-writing) | 1421 |  |  | [EQ-Bench](https://eqbench.com/creative_writing.html) |  |
| [EQ-Bench Creative Writing](https://noometry.com/benchmarks/eqbench-creative-writing) | 1500 | #55 of 115, top 48% |  | [EQ-Bench](https://eqbench.com/creative_writing.html) |  |
| [WildBench](https://noometry.com/benchmarks/wildbench) | 82.8% | #20 of 57, top 36% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1391 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1405 | #118 of 295, top 40% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LiveBench Language](https://noometry.com/benchmarks/livebench-language) | 48.5% | #13 of 39, top 34% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

## API pricing by provider

DeepSeek-R1 API prices
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
| --- | --- | --- | --- | --- |
| [azure](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models) | $1.35 | $5.40 | — | 2026-10-10 |
| [bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html) | $1.35 | $5.40 | — | 2026-10-10 |
| [deepinfra](https://deepinfra.com/models) | $0.50 | $2.15 | $0.35 | 2026-10-10 |
| [openrouter](https://openrouter.ai/deepseek/deepseek-r1) | $0.70 | $2.50 | — | 2026-10-10 |
| [together](https://docs.together.ai/docs/serverless-models) | $3 | $7 | — | 2026-10-10 |

[All DeepSeek API prices →](https://noometry.com/llm-pricing/deepseek) [Estimate your cost →](https://noometry.com/tools/cost-calculator)

## Compare DeepSeek-R1

-   [DeepSeek-R1 vs DeepSeek-V3](https://noometry.com/compare/deepseek-r1-vs-deepseek-v3)
-   [DeepSeek-R1 vs Hunyuan T1 20250711](https://noometry.com/compare/deepseek-r1-vs-hunyuan-t1)
-   [DeepSeek-R1 vs Step 3.5 Flash](https://noometry.com/compare/deepseek-r1-vs-step-3-5-flash)
-   [DeepSeek-R1 vs Nemotron 3 Ultra](https://noometry.com/compare/deepseek-r1-vs-nemotron-3-ultra)
-   [DeepSeek-R1 vs Qwen3.6 27B](https://noometry.com/compare/deepseek-r1-vs-qwen3-6-27b)
-   [DeepSeek-R1 vs Qwen3.5-Flash](https://noometry.com/compare/deepseek-r1-vs-qwen3-5-flash)
-   [DeepSeek-R1 vs Amazon Nova Experimental Chat 10 20](https://noometry.com/compare/amazon-nova-experimental-chat-10-20-vs-deepseek-r1)
-   [DeepSeek-R1 vs GPT-6 Astra](https://noometry.com/compare/deepseek-r1-vs-gpt-6-astra)
-   [DeepSeek-R1 vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-deepseek-r1)
-   [DeepSeek-R1 vs Gemini 3.8 Flash](https://noometry.com/compare/deepseek-r1-vs-gemini-3-8-flash)
-   [DeepSeek-R1 vs Kimi K3](https://noometry.com/compare/deepseek-r1-vs-kimi-k3)
-   [DeepSeek-R1 vs Grok 4.6](https://noometry.com/compare/deepseek-r1-vs-grok-4-6)
-   [DeepSeek-R1 vs Qwen3.8 Max](https://noometry.com/compare/deepseek-r1-vs-qwen3-8-max)
-   [DeepSeek-R1 vs GLM-5.3](https://noometry.com/compare/deepseek-r1-vs-glm-5-3)

## Other DeepSeek models

-   [DeepSeek V4 Pro](https://noometry.com/models/deepseek-v4-pro)54.3
-   [DeepSeek V4 Flash](https://noometry.com/models/deepseek-v4-flash)53.6
-   [DeepSeek V4.1 Flash](https://noometry.com/models/deepseek-v4-1-flash)52.8
-   [DeepSeek-V3.2-Exp](https://noometry.com/models/deepseek-v3-2-exp)44.3
-   [DeepSeek-V3.1-Terminus](https://noometry.com/models/deepseek-v3-1-terminus)43.1
-   [DeepSeek-V3.1](https://noometry.com/models/deepseek-v3-1)42.8
-   [DeepSeek-V3.2-Speciale](https://noometry.com/models/deepseek-v3-2-speciale)39.7
-   [DeepSeek-V3](https://noometry.com/models/deepseek-v3)39.5

## Frequently asked questions

### How good is DeepSeek-R1?

DeepSeek-R1 by DeepSeek ranks 115th of 354 ranked models on the Noometry Index as of October 2026, with a score of 42.3. Its strongest category is long context, where it ranks 36th. API pricing starts at $0.50 per million input tokens and $2.15 per million output tokens, with a 164K-token context window.

### How much does DeepSeek-R1 cost?

DeepSeek-R1 costs $0.50 per million input tokens and $2.15 per million output tokens on deepinfra, with cached input at $0.35.

### What is DeepSeek-R1's context window?

DeepSeek-R1 accepts up to 164K tokens of input and can write up to 64K tokens in one response.

### Is DeepSeek-R1 open source?

No. DeepSeek-R1 is proprietary and available only through DeepSeek's API and partner platforms.

### How fast is DeepSeek-R1?

DeepSeek-R1 generated about 10 output tokens per second in the Kagi LLM Benchmark's timed runs. Speed varies by provider, load and reasoning effort.

### What are DeepSeek-R1's strengths and weaknesses?

Relative to other ranked models, DeepSeek-R1 places best in long context, coding, math and lowest in reasoning, agentic & tool use, instruction following.

### What is DeepSeek-R1 best at?

Its best category is long context, where it ranks 36th on Noometry.

### Cite this page

Noometry. (2026). DeepSeek-R1 benchmarks and pricing. Retrieved October 10, 2026, from https://noometry.com/models/deepseek-r1

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/models/deepseek-r1.md).