DeepSeek: DeepSeek V3.1

by DeepSeek

DeepSeek-V3.1 is a large hybrid reasoning model (671B parameters, 37B active) that supports both thinking and non-thinking modes via prompt templates. It extends the DeepSeek-V3 base with a two-phase long-context training process, reaching up to 128K tokens, and uses FP8 microscaling for efficient inference. Users can control the reasoning behaviour with the `reasoning` `enabled` boolean. [Learn more in our docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#enable-reasoning-with-default-config) The model improves tool use, code generation, and reasoning efficiency, achieving performance comparable to DeepSeek-R1 on difficult benchmarks while responding more quickly. It supports structured tool calling, code agents, and search agents, making it suitable for research, coding, and agentic workflows. It succeeds the [DeepSeek V3-0324](/deepseek/deepseek-chat-v3-0324) model and performs well on a variety of tasks.

Avg Score

85.8%

12 answers

Avg Latency

60.0s

8 runs

Pricing

$0.15

input

$0.75

output

per 1M tokens

Context

33K

tokens

Alternatives

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Other Models from DeepSeek

Compare performance with other models from the same creator

Model	Score	Latency	Cost/1M
DeepSeek: R1	96.2%	109.7s	$1.60
DeepSeek: DeepSeek V3.1 Terminus	91.9%	41.8s	$0.50
DeepSeek: DeepSeek V3.2 Exp	89.6%	96.8s	$0.27
DeepSeek: DeepSeek V3	88.5%	21.2s	$0.75
DeepSeek: R1 0528	85.0%	36.9s	Free
DeepSeek: DeepSeek V3.1 Terminus	83.1%	21.4s	$0.50
DeepSeek: R1 0528	81.5%	120.7s	$1.07
DeepSeek: DeepSeek V3.2 Speciale	80.7%	152.3s	$0.34
DeepSeek: DeepSeek V3.2	79.9%	37.8s	$0.32
DeepSeek: DeepSeek V3 0324	78.7%	15.3s	$0.53
DeepSeek: R1 Distill Llama 70B	49.2%	33.8s	$0.07
DeepSeek: R1 Distill Qwen 32B	40.8%	53.5s	$0.29
DeepSeek: DeepSeek R1 0528 Qwen3 8B	—	—	$0.07
DeepSeek: DeepSeek Prover V2	—	—	$1.34
DeepSeek: R1 Distill Qwen 14B	—	—	$0.15

Benchmark Performance

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Price vs Performance

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Current model (baseline)

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Y-axis shows score difference from shared benchmarks. X-axis uses log scale.

Score Over Time

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Benchmark Activity

Number of benchmark runs over time

Quickstart

Get started with this model using OpenRouter

View on OpenRouter

import { OpenRouter } from "@openrouter/sdk";

const openrouter = new OpenRouter({
  apiKey: "<OPENROUTER_API_KEY>"
});

const completion = await openrouter.chat.completions.create({
  model: "deepseek/deepseek-chat-v3.1",
  messages: [
    {
      role: "user",
      content: "Hello!"
    }
  ]
});

console.log(completion.choices[0].message.content);

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