DeepSeek, open weights

# DeepSeek-V3.1

> DeepSeek-V3.1 by DeepSeek, released August 2025. Ranked #108 of 354 with a Noometry Index of 42.8. API: $0.25 in / $0.95 out per M tokens. 164K context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/deepseek-v3-1
- Last updated: 2026-10-10
- Title: DeepSeek-V3.1 Benchmarks, Price & Rank (October 2026)

DeepSeek-V3.1 by DeepSeek ranks 108th of 354 ranked models on the Noometry Index as of October 2026, with a score of 42.8. Its strongest category is knowledge, where it ranks 90th. API pricing starts at $0.25 per million input tokens and $0.95 per million output tokens, with a 164K-token context window.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #108 of 354
- **Index score:** 42.8
- **Evidence:** Confirmed 27 results
- **Provider:** [![](/logos/deepseek.svg) DeepSeek](https://noometry.com/providers/deepseek)
- **Released:** August 21, 2025
- **Weights:** Open weights
- **Reasoning:** Yes
- **Context window:** 164K
- **Max output:** 8K
- **Input price:** $0.25 / M
- **Output price:** $0.95 / M
- **Blended price:** $0.42 / M
- **Output speed:** 328 tokens/s [Kagi](https://help.kagi.com/kagi/ai/llm-benchmark.html)
- **Value:** #59 of 219
- **Knowledge cutoff:** August 2025
- **Input:** text
- **Hugging Face:** [deepseek-ai/DeepSeek-V3.1](https://huggingface.co/deepseek-ai/DeepSeek-V3.1)

## Category scores

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

DeepSeek-V3.1 category scores

1.  Coding 40.3
2.  Reasoning 27.9
3.  Math 38.9
4.  Knowledge 43.7
5.  Multilingual 51.6
6.  Instruction Following 73.9
7.  Long Context 36.3
8.  Writing & Preference 60.3
9.  020406080

DeepSeek-V3.1 category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 40.3 | #144 | 2 |
| [Reasoning](https://noometry.com/best/reasoning) | 27.9 | #110 | 5 |
| [Math](https://noometry.com/best/math) | 38.9 | #122 | 1 |
| [Knowledge](https://noometry.com/best/knowledge) | 43.7 | #90 | 2 |
| [Multilingual](https://noometry.com/best/multilingual) | 51.6 | #106 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 73.9 | #110 | 1 |
| [Long Context](https://noometry.com/best/long-context) | 36.3 | #232 | 2 |
| [Writing & Preference](https://noometry.com/best/writing) | 60.3 | #98 | 4 |

## Strengths and weaknesses

Categories where DeepSeek-V3.1 places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

DeepSeek-V3.1: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Knowledge](https://noometry.com/best/knowledge) | 43.7 | +6.4 | #90 of 314, top 29% |
| [Writing & Preference](https://noometry.com/best/writing) | 60.3 | +6.5 | #98 of 312, top 32% |
| [Reasoning](https://noometry.com/best/reasoning) | 27.9 | +4.3 | #110 of 350, top 32% |

### Weakest categories

DeepSeek-V3.1: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Long Context](https://noometry.com/best/long-context) | 36.3 | −4.7 | #232 of 296, top 79% |
| [Coding](https://noometry.com/best/coding) | 40.3 | +1.6 | #144 of 340, top 43% |
| [Math](https://noometry.com/best/math) | 38.9 | +2.3 | #122 of 327, top 38% |

## Closest competitors

The models ranked just above and below DeepSeek-V3.1. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to DeepSeek-V3.1
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [Amazon Nova Experimental Chat 12 10](https://noometry.com/models/amazon-nova-experimental-chat-12-10) | #104 | 42.9 | — | — | [Compare](https://noometry.com/compare/amazon-nova-experimental-chat-12-10-vs-deepseek-v3-1) |
| [o3-pro](https://noometry.com/models/o3-pro) | #105 | 42.9 | $35 | 1 | [Compare](https://noometry.com/compare/deepseek-v3-1-vs-o3-pro) |
| [Qwen3.5 Plus](https://noometry.com/models/qwen3-5-plus) | #106 | 42.9 | $0.90 | — | [Compare](https://noometry.com/compare/deepseek-v3-1-vs-qwen3-5-plus) |
| [Amazon Nova Experimental Chat 26 01 10](https://noometry.com/models/amazon-nova-experimental-chat-26-01-10) | #107 | 42.8 | — | — | [Compare](https://noometry.com/compare/amazon-nova-experimental-chat-26-01-10-vs-deepseek-v3-1) |
| [GPT-5.3 Chat](https://noometry.com/models/gpt-5-3-chat) | #109 | 42.8 | $4.81 | — | [Compare](https://noometry.com/compare/deepseek-v3-1-vs-gpt-5-3-chat) |
| [GPT-5.5 Instant](https://noometry.com/models/gpt-5-5-instant) | #110 | 42.7 | — | — | [Compare](https://noometry.com/compare/deepseek-v3-1-vs-gpt-5-5-instant) |
| [GPT-5.2 Codex](https://noometry.com/models/gpt-5-2-codex) | #111 | 42.6 | $4.81 | — | [Compare](https://noometry.com/compare/deepseek-v3-1-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-v3-1-vs-qwen3-5-flash) |

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-V3.1 Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 37.5% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 38.4% | #83 of 119, top 70% | thinking | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1417 | #119 of 294, top 41% | thinking | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Reasoning

DeepSeek-V3.1 Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [SimpleBench](https://noometry.com/benchmarks/simplebench) | 40% | #54 of 77, top 71% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Kagi LLM Benchmark](https://noometry.com/benchmarks/kagi-reasoning) | 53.2% | #55 of 99, top 56% |  | [Kagi LLM Benchmark](https://help.kagi.com/kagi/ai/llm-benchmark.html) |  |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1417 | #108 of 297, top 37% | thinking | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 82.7% | #61 of 151, top 41% | thinking | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 24.3% | #87 of 125, top 70% | thinking | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 139.92 | #109 of 213, top 52% |  | [Epoch AI](https://epoch.ai/eci) | 2025-08-21 |
| [ForecastBench](https://noometry.com/benchmarks/forecastbench) | 58 | #52 of 72, top 73% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Math

DeepSeek-V3.1 Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1420 | #101 of 285, top 36% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Knowledge

DeepSeek-V3.1 Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Vectara Hallucination Rate](https://noometry.com/benchmarks/vectara-hallucination) (lower is better) | 5.5% | #16 of 96, top 17% |  | [Vectara Hallucination Leaderboard](https://github.com/vectara/hallucination-leaderboard) |  |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1405 | #119 of 273, top 44% | thinking | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Multilingual

DeepSeek-V3.1 Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1400 | #107 of 297, top 37% | thinking | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1469 | #75 of 285, top 27% | thinking | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1447 | #76 of 223, top 35% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1411 | #86 of 231, top 38% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1378 | #81 of 211, top 39% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1337 | #111 of 213, top 53% | thinking | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1405 | #100 of 283, top 36% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1431 | #82 of 226, top 37% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

DeepSeek-V3.1 Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1400 | #104 of 298, top 35% | thinking | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

DeepSeek-V3.1 Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Fiction.LiveBench](https://noometry.com/benchmarks/fiction-livebench) | 52.8% | #33 of 47, top 71% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1422 | #90 of 291, top 31% | thinking | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

DeepSeek-V3.1 Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1420 | #93 of 297, top 32% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1401 | #78 of 295, top 27% | thinking | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [EQ-Bench Creative Writing](https://noometry.com/benchmarks/eqbench-creative-writing) | 1436 | #64 of 115, top 56% |  | [EQ-Bench](https://eqbench.com/creative_writing.html) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1408 | #114 of 295, top 39% | thinking | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

## API pricing by provider

DeepSeek-V3.1 API prices
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
| --- | --- | --- | --- | --- |
| [bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html) | $0.58 | $1.68 | — | 2026-10-10 |
| [deepinfra](https://deepinfra.com/models) | $0.25 | $0.95 | $0.13 | 2026-10-10 |
| [openrouter](https://openrouter.ai/deepseek/deepseek-chat-v3.1) | $0.25 | $0.95 | $0.13 | 2026-10-10 |
| [together](https://docs.together.ai/docs/serverless-models) | $0.60 | $1.70 | — | 2026-10-10 |
| [vertex](https://cloud.google.com/vertex-ai/generative-ai/docs/models) | $0.60 | $1.70 | $0.06 | 2026-10-10 |

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

## Compare DeepSeek-V3.1

-   [DeepSeek-V3.1 vs DeepSeek-R1](https://noometry.com/compare/deepseek-r1-vs-deepseek-v3-1)
-   [DeepSeek-V3.1 vs Amazon Nova Experimental Chat 26 01 10](https://noometry.com/compare/amazon-nova-experimental-chat-26-01-10-vs-deepseek-v3-1)
-   [DeepSeek-V3.1 vs GPT-5.3 Chat](https://noometry.com/compare/deepseek-v3-1-vs-gpt-5-3-chat)
-   [DeepSeek-V3.1 vs Qwen3.5 Plus](https://noometry.com/compare/deepseek-v3-1-vs-qwen3-5-plus)
-   [DeepSeek-V3.1 vs GPT-5.5 Instant](https://noometry.com/compare/deepseek-v3-1-vs-gpt-5-5-instant)
-   [DeepSeek-V3.1 vs o3-pro](https://noometry.com/compare/deepseek-v3-1-vs-o3-pro)
-   [DeepSeek-V3.1 vs GPT-5.2 Codex](https://noometry.com/compare/deepseek-v3-1-vs-gpt-5-2-codex)
-   [DeepSeek-V3.1 vs GPT-6 Astra](https://noometry.com/compare/deepseek-v3-1-vs-gpt-6-astra)
-   [DeepSeek-V3.1 vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-deepseek-v3-1)
-   [DeepSeek-V3.1 vs Gemini 3.8 Flash](https://noometry.com/compare/deepseek-v3-1-vs-gemini-3-8-flash)
-   [DeepSeek-V3.1 vs Kimi K3](https://noometry.com/compare/deepseek-v3-1-vs-kimi-k3)
-   [DeepSeek-V3.1 vs Grok 4.6](https://noometry.com/compare/deepseek-v3-1-vs-grok-4-6)
-   [DeepSeek-V3.1 vs Qwen3.8 Max](https://noometry.com/compare/deepseek-v3-1-vs-qwen3-8-max)
-   [DeepSeek-V3.1 vs GLM-5.3](https://noometry.com/compare/deepseek-v3-1-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-R1](https://noometry.com/models/deepseek-r1)42.3
-   [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-V3.1?

DeepSeek-V3.1 by DeepSeek ranks 108th of 354 ranked models on the Noometry Index as of October 2026, with a score of 42.8. Its strongest category is knowledge, where it ranks 90th. API pricing starts at $0.25 per million input tokens and $0.95 per million output tokens, with a 164K-token context window.

### How much does DeepSeek-V3.1 cost?

DeepSeek-V3.1 costs $0.25 per million input tokens and $0.95 per million output tokens on deepinfra, with cached input at $0.13.

### What is DeepSeek-V3.1's context window?

DeepSeek-V3.1 accepts up to 164K tokens of input and can write up to 8K tokens in one response.

### Is DeepSeek-V3.1 open source?

Yes. DeepSeek-V3.1's weights are downloadable from Hugging Face (deepseek-ai/DeepSeek-V3.1); check the license for commercial terms.

### How fast is DeepSeek-V3.1?

DeepSeek-V3.1 generated about 328 output tokens per second in the Kagi LLM Benchmark's timed runs. Speed varies by provider, load and reasoning effort.

### What are DeepSeek-V3.1's strengths and weaknesses?

Relative to other ranked models, DeepSeek-V3.1 places best in knowledge, writing & preference, reasoning and lowest in long context, coding, math.

### What is DeepSeek-V3.1 best at?

Its best category is knowledge, where it ranks 90th on Noometry.

### Cite this page

Noometry. (2026). DeepSeek-V3.1 benchmarks and pricing. Retrieved October 10, 2026, from https://noometry.com/models/deepseek-v3-1

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/models/deepseek-v3-1.md).