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DeepSeek · Open weights

DeepSeek Chat 0324

Added Jan 1, 2024Open weights

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Use in the app

DeepSeek V3 0324, DeepSeek's 03 March 2025 V3 model, optimized for general-purpose tasks. Quantized at FP8.

  • Tools
  • Structured output
Input
Text
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.20
Output
$0.77
Cache read
$0.14

Reference prices as published by the model provider. API example

Specifications

Context window
128K
Max output
8.2K
Average output (7 days)
280 tokens
Longer than 29% of 465 models

Side by side

DeepSeek Chat 0324 compared

Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.

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DeepSeek Chat 0324DeepSeekThis modelDeepSeek R1 0528DeepSeekRemove
ProviderDeepSeekDeepSeek
Discovery tierOpen weightsOpen weights
AddedJan 1, 2024May 28, 2025
Limits
Context window128K163.84KBest
Max output8.19K32.77KBest
Average output280 tokens1K tokens
Pricing, USD per 1M tokens
Input$0.20Best$0.50
Output$0.77Best$2.15
Cache read$0.14Best$0.35
Performance
Throughput26.5 tok/s39.3 tok/sBest
Time to first token2.7 s1.2 sBest
Request success (24h)99.9%99.9%
Benchmark indexes
Intelligence Index9.713.1Best
Coding Index21.2—
Math Index41.076.0Best
Capabilities
ReasoningNoYes
ToolsYesYes
Structured outputYesNo
Reasoning effort——

Public evaluations

Benchmarks

Independent evaluation results, with how each score ranks among the models in the Noumen catalog.

  • DeepSeek Chat 0324
  • DeepSeek R1 0528

Intelligence Index

  • DeepSeek Chat 03249.7
  • DeepSeek R1 052813.1

DeepSeek Chat 0324: Better than 19% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • DeepSeek Chat 0324: 21.2
    • DeepSeek R1 0528: —

  • Math Index

    Composite of the math evaluations

    • DeepSeek Chat 0324: 41.0
    • DeepSeek R1 0528: 76.0Top

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • DeepSeek Chat 0324: 0.3%
    • DeepSeek R1 0528: —

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • DeepSeek Chat 0324: 0.0%
    • DeepSeek R1 0528: —

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • DeepSeek Chat 0324: 68 Elo
    • DeepSeek R1 0528: —

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • DeepSeek Chat 0324: 69 Elo
    • DeepSeek R1 0528: —

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • DeepSeek Chat 0324: 1.6%
    • DeepSeek R1 0528: —

  • AA-LCRv1.1

    Reasoning across long documents

    • DeepSeek Chat 0324: 40.7%
    • DeepSeek R1 0528: 55.7%Top

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • DeepSeek Chat 0324: 4.7%
    • DeepSeek R1 0528: 15.8%Top

  • GPQA Diamond

    Graduate-level science questions

    • DeepSeek Chat 0324: 65.5%
    • DeepSeek R1 0528: 81.3%Top

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • DeepSeek Chat 0324: 81.9%
    • DeepSeek R1 0528: 84.9%Top

  • IFBench

    Following precise output instructions

    • DeepSeek Chat 0324: 41.0%Top
    • DeepSeek R1 0528: 39.6%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • DeepSeek Chat 0324: 0.0%
    • DeepSeek R1 0528: —

  • SciCode

    Scientific computing in Python

    • DeepSeek Chat 0324: 39.0%
    • DeepSeek R1 0528: —

  • LiveCodeBench

    Recent competitive programming problems

    • DeepSeek Chat 0324: 40.5%
    • DeepSeek R1 0528: 77.0%Top

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • DeepSeek Chat 0324: 41.0%
    • DeepSeek R1 0528: 76.0%Top

  • AIME

    Competition mathematics

    • DeepSeek Chat 0324: 52.0%
    • DeepSeek R1 0528: 89.3%Top

  • MATH-500

    Competition math problems

    • DeepSeek Chat 0324: 94.2%
    • DeepSeek R1 0528: 98.3%Top

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • DeepSeek Chat 0324: 15.2%
    • DeepSeek R1 0528: 15.9%Top

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • DeepSeek Chat 0324: 47.1%Top
    • DeepSeek R1 0528: 36.5%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • DeepSeek Chat 0324: 40.7%
    • DeepSeek R1 0528: 55.7%Top

Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis

Live routing data

Performance and reliability

Recent averages across requests routed for this model. Your own speed depends on prompt length, reasoning effort and load.

Throughput
26.5 tok/s
Faster than 17% of 251 models
Time to first token
2.7 s
Quicker than 64% of 251 models
Prompt cache hits
69.7%
About $0.15 per 1M input tokens with caching
Request success (24h)
99.9%
Reliable

Cache hits are the share of input tokens recently served from a prompt cache across all requests. They are not a forecast of your own hit rate.

Hourly request success · last 72 hoursLowest hour 98.2%
  • Healthy (98% or more)
  • Degraded (90–98%)
  • Down (under 90%)
  • No requests

Developers

Use it through the API

Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.

Read the quickstart
curl https://www.noumen.xyz/api/v1/chat/completions \
  -H "Authorization: Bearer $NOUMEN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-v3-0324",
    "messages": [{ "role": "user", "content": "Explain this stack trace." }]
  }'
import os
from openai import OpenAI

client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
    model="deepseek-v3-0324",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

DeepSeek

Other DeepSeek models, starting with the same family.