deepseek-v3.1-base

completions

byopenrouter

deepseek-v3.1-base

completions

Published by: deepseekProvider:

openrouter

This is a base model, trained only for raw next-token prediction. Unlike instruct/chat models, it has not been fine-tuned to follow user instructions. Prompts need to be written more like training text or examples rather than simple requests (e.g., “Translate the following sentence…” instead of just “Translate this”). DeepSeek-V3.1 Base is a 671B parameter open Mixture-of-Experts (MoE) language model with 37B active parameters per forward pass and a context length of 128K tokens. Trained on 14.8T tokens using FP8 mixed precision, it achieves high training efficiency and stability, with strong performance across language, reasoning, math, and coding tasks.

Released

Dec 25, 2024

Knowledge

Jun 28, 2024

License

MIT + Model License (Commercial use allowed)

Context

163840

Input

$0.2 / 1M tokens

Output

$0.8 / 1M tokens

Accepts: text

Returns: text

Released Dec 25, 2024Knowledge Cutoff: Jun 28, 2024License: MIT + Model License (Commercial use allowed)

Context: 163840 Input: $0.2 / 1M tokensOutput: $0.8 / 1M tokensAccepts: textReturns: text

Access deepseek-v3.1-base through LangDB AI Gateway

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Integrate with deepseek's deepseek-v3.1-base and 250+ other models through a unified API. Monitor usage, control costs, and enhance security.

Unified API

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Instant Setup

99.9% Uptime

10,000+Monthly Requests

Benchmark Results for deepseek-v3.1-base

Category Performance Scores:

Academia: Score 44.95 (Top 44% - Rank #152)
Finance: Score 29.25 (Top 54% - Rank #187)
Marketing: Score 44.15 (Top 42% - Rank #145)
Maths: Score 26.00 (Top 53% - Rank #183)
Programming: Score 35.60 (Top 33% - Rank #114)
Science: Score 58.48 (Top 48% - Rank #166)
Writing: Score 43.81 (Top 47% - Rank #163)

Overall Performance: 40.31936507936508 average score across all categories

Detailed Benchmark Scores:

Benchmark	Score	Percentile	Domain
AIME	25.30	Top 45%	Mathematics
AA Coding Index	35.60	Top 33%	Programming
AAII	32.50	Top 36%	General
AA Math Index	26.00	Top 53%	Mathematics
DROP	91.60	Top 0%	General Knowledge
GPQA	57.40	Top 47%	STEM (Physics, Chemistry, Biology)
HLE	3.60	Top 87%	General Knowledge
LiveCodeBench	35.90	Top 36%	Programming
MATH-500	88.70	Top 36%	Mathematics
MMLU	88.50	Top 15%	General Knowledge
MMLU-Pro	75.55	Top 37%	General Knowledge
SciCode	35.40	Top 23%	Scientific

MMLU Score: 88.50 - Massive Multitask Language Understanding benchmark

GPQA Score: 57.40 - Graduate-level reasoning benchmark

Model Comparison:

Provider: openrouter

Model Type: completions

Context Size: 163840 tokens

Comparing against 345 models in the database

Performance Analytics for deepseek-v3.1-base

Usage Statistics (Last 5 Days):

Total Requests: 11 API calls
Average TPS: 109.56 tokens per second
Average Response Time: 57774.90ms
Average Time to First Token: 36348.70ms
Total Cost: $0.02
Average Request Cost: $0.0022

Daily Performance Breakdown:

Date	Requests	TPS	Response Time	TTFT	Cost
9/17/2025	1	251.44	11303.00ms	2477.30ms	$0.00
9/19/2025	1	0.00	166802.90ms	1266.40ms	$0.00
9/20/2025	4	22.02	96981.30ms	96981.30ms	$0.00
9/21/2025	2	865.82	33609.90ms	2947.20ms	$0.02
9/23/2025	3	18.48	757.50ms	757.50ms	$0.00

Performance Summary:

Model: deepseek-v3.1-base by openrouter

Monitoring Period: 9/16/2025 to 9/23/2025

Average Daily Requests: 2

Peak Daily Requests: 4

Statistics

View Full Statistics

Request Volume

Daily API requests

Performance (TPS)

Tokens per second

109.56 tokens/s

Category Scores

Benchmark Tests

View Other Benchmarks

AIME

25.3

Mathematics

AA Coding Index

35.6

Programming

AAII

32.5

General

AA Math Index

26.0

Mathematics

DROP

91.6

General Knowledge

GPQA

57.4

STEM (Physics, Chemistry, Biology)

HLE

3.6

General Knowledge

LiveCodeBench

35.9

Programming

MATH-500

88.7

Mathematics

MMLU

88.5

General Knowledge

MMLU-Pro

75.6

General Knowledge

SciCode

35.4

Scientific

Metric	AIME	AA Coding Index	AAII	AA Math Index	DROP	GPQA	HLE	LiveCodeBench	MATH-500	MMLU	MMLU-Pro	SciCode
Score	25.3	35.6	32.5	26.0	91.6	57.4	3.6	35.9	88.7	88.5	75.6	35.4

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Code Examples

Integration samples and API usage

Code Samples for deepseek-v3.1-base

Python SDK Example:

from openai import OpenAI

client = OpenAI(
    base_url="https://api.us-east-1.langdb.ai/projects/<your_project_id>",
    api_key="<your_api_key>"
)

response = client.chat.completions.create(
    model="deepseek-v3.1-base",
    messages=[
        {"role": "user", "content": "Hello, how are you?"}
    ]
)

print(response.choices[0].message.content)

TypeScript SDK Example:

import OpenAI from 'openai';

const client = new OpenAI({
    baseURL: "https://api.us-east-1.langdb.ai/projects/<your_project_id>",
    apiKey: "<your_api_key>"
});

const response = await client.chat.completions.create({
    model: "deepseek-v3.1-base",
    messages: [
        { role: "user", content: "Hello, how are you?" }
    ]
});

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

cURL Example:

curl -X POST "https://api.us-east-1.langdb.ai/projects/<your_project_id>/v1/chat/completions" \
  -H "Authorization: Bearer <your_api_key>" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-v3.1-base",
    "messages": [
        {"role": "user", "content": "Hello, how are you?"}
    ]
  }'

Model: deepseek-v3.1-base

Provider: openrouter

API Endpoint: $https://api.us-east-1.langdb.ai

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deepseek-v3.1-base by openrouter - AI Model Details, Pricing, and Performance Metrics

deepseek-v3.1-base

deepseek-v3.1-base

Access deepseek-v3.1-base through LangDB AI Gateway

Statistics

Category Scores

Benchmark Tests

Compare with Similar Models

Code Examples

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