deepseek-v3

completions

byfireworksai

deepseek-v3

completions

Published by: deepseekProvider:

fireworksai

A strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token from Deepseek.

Released

Dec 25, 2024

Knowledge

Jun 28, 2024

License

MIT + Model License (Commercial use allowed)

Context

128K

Input

$0.9 / 1M tokens

Output

$0.9 / 1M tokens

Accepts: text

Returns: text

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

Context: 128K Input: $0.9 / 1M tokensOutput: $0.9 / 1M tokensAccepts: textReturns: text

Access deepseek-v3 through LangDB AI Gateway

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Integrate with deepseek's deepseek-v3 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

Category Performance Scores:

Academia: Score 45.30 (Top 48% - Rank #156)
Finance: Score 45.10 (Top 40% - Rank #130)
Marketing: Score 49.40 (Top 10% - Rank #33)
Maths: Score 57.00 (Top 43% - Rank #140)
Programming: Score 35.60 (Top 35% - Rank #114)
Science: Score 57.40 (Top 51% - Rank #166)
Writing: Score 35.86 (Top 22% - Rank #72)

Overall Performance: 46.52214285714286 average score across all categories

Detailed Benchmark Scores:

Benchmark	Score	Percentile	Domain
AIME	25.30	Top 48%	Mathematics
AA Coding Index	35.60	Top 35%	Programming
AAII	33.20	Top 37%	General
AA Math Index	57.00	Top 43%	Mathematics
DROP	91.60	Top 18%	General Knowledge
GPQA	57.40	Top 51%	STEM (Physics, Chemistry, Biology)
HLE	3.60	Top 90%	General Knowledge
LiveCodeBench	35.90	Top 38%	Programming
MATH-500	88.70	Top 39%	Mathematics
MMLU	88.50	Top 16%	General Knowledge
MMLU-Pro	75.55	Top 39%	General Knowledge
SciCode	35.40	Top 26%	Scientific

MMLU Score: 88.50 - Massive Multitask Language Understanding benchmark

GPQA Score: 57.40 - Graduate-level reasoning benchmark

Model Comparison:

Provider: fireworksai

Model Type: completions

Context Size: 128000 tokens

Comparing against 324 models in the database

Performance Analytics for deepseek-v3

Usage Statistics (Last 2 Days):

Total Requests: 14 API calls
Average TPS: 875.46 tokens per second
Average Response Time: 3171.70ms
Average Time to First Token: 572.80ms
Total Cost: $0.04
Average Request Cost: $0.0030

Daily Performance Breakdown:

Date	Requests	TPS	Response Time	TTFT	Cost
9/7/2025	10	702.76	2284.70ms	446.20ms	$0.02
9/9/2025	4	1058.50	5389.20ms	889.10ms	$0.02

Performance Summary:

Model: deepseek-v3 by fireworksai

Monitoring Period: 9/4/2025 to 9/11/2025

Average Daily Requests: 7

Peak Daily Requests: 10

Statistics

View Full Statistics

Request Volume

Daily API requests

Performance (TPS)

Tokens per second

875.46 tokens/s

Category Scores

marketing#12

Benchmark Tests

View Other Benchmarks

AIME

25.3

Mathematics

AA Coding Index

35.6

Programming

AAII

33.2

General

AA Math Index

57.0

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	33.2	57.0	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

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",
    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",
    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",
    "messages": [
        {"role": "user", "content": "Hello, how are you?"}
    ]
  }'

Model: deepseek-v3

Provider: fireworksai

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

Create API Key

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deepseek-v3 by fireworksai - AI Model Details, Pricing, and Performance Metrics

deepseek-v3

deepseek-v3

Access deepseek-v3 through LangDB AI Gateway

Statistics

Category Scores

Benchmark Tests

Compare with Similar Models

Code Examples

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