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DeepSeek · Open weights

DeepSeek V3/Deepseek Chat

Added Feb 27, 2025Open weights

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Use in the app

DeepSeek original V3 model, trained on nearly 15 trillion tokens, matches leading closed-source models at a far lower price. Quantized at FP8.

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

Pricing

USD per 1M tokens
Input
$0.10
Output
$0.43

Reference prices as published by the model provider. API example

Specifications

Context window
128K
Max output
8.2K
Average output (7 days)
293 tokens
Longer than 30% of 465 models

Side by side

DeepSeek V3/Deepseek Chat compared

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

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DeepSeek V3/Deepseek ChatDeepSeekThis modelDeepSeek LatestDeepSeekRemove
ProviderDeepSeekDeepSeek
Discovery tierOpen weightsOpen weights
AddedFeb 27, 2025May 3, 2026
Limits
Context window128K1.05MBest
Max output8.19K384KBest
Average output293 tokens—
Pricing, USD per 1M tokens
Input$0.10Best$0.23
Output$0.43Best$0.69
Cache read—$0.0073
Performance
Throughput27.1 tok/s74.3 tok/sBest
Time to first token2.5 sBest2.7 s
Request success (24h)99.9%Best99.7%
Benchmark indexes
Intelligence Index16.036.0Best
Coding Index—68.8
Math Index59.0—
Capabilities
ReasoningNoYes
ToolsYesYes
Structured outputYesYes
PDF inputYesNo
Reasoning effort—none, low, high, max

Public evaluations

Benchmarks

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

  • DeepSeek V3/Deepseek Chat
  • DeepSeek Latest

Intelligence Index

  • DeepSeek V3/Deepseek Chat16.0
  • DeepSeek Latest36.0

DeepSeek V3/Deepseek Chat: Better than 40% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • DeepSeek V3/Deepseek Chat: —
    • DeepSeek Latest: 68.8

  • Math Index

    Composite of the math evaluations

    • DeepSeek V3/Deepseek Chat: 59.0
    • DeepSeek Latest: —

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • DeepSeek V3/Deepseek Chat: —
    • DeepSeek Latest: 56.7%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • DeepSeek V3/Deepseek Chat: —
    • DeepSeek Latest: 25.9%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • DeepSeek V3/Deepseek Chat: —
    • DeepSeek Latest: 1,256 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • DeepSeek V3/Deepseek Chat: —
    • DeepSeek Latest: 1,449 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • DeepSeek V3/Deepseek Chat: —
    • DeepSeek Latest: 11.4%

  • AA-LCRv1.1

    Reasoning across long documents

    • DeepSeek V3/Deepseek Chat: 45.7%
    • DeepSeek Latest: 80.3%Top

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • DeepSeek V3/Deepseek Chat: 11.2%
    • DeepSeek Latest: 41.0%Top

  • GPQA Diamond

    Graduate-level science questions

    • DeepSeek V3/Deepseek Chat: 75.1%
    • DeepSeek Latest: 92.8%Top

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • DeepSeek V3/Deepseek Chat: 83.7%
    • DeepSeek Latest: —

  • IFBench

    Following precise output instructions

    • DeepSeek V3/Deepseek Chat: 49.0%
    • DeepSeek Latest: —

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • DeepSeek V3/Deepseek Chat: —
    • DeepSeek Latest: 14.1%

  • SciCode

    Scientific computing in Python

    • DeepSeek V3/Deepseek Chat: —
    • DeepSeek Latest: 51.0%

  • LiveCodeBench

    Recent competitive programming problems

    • DeepSeek V3/Deepseek Chat: 59.3%
    • DeepSeek Latest: —

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • DeepSeek V3/Deepseek Chat: 59.0%
    • DeepSeek Latest: —

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • DeepSeek V3/Deepseek Chat: 32.6%
    • DeepSeek Latest: —

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • DeepSeek V3/Deepseek Chat: 78.9%
    • DeepSeek Latest: —

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • DeepSeek V3/Deepseek Chat: 45.7%
    • DeepSeek Latest: 80.3%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
27.1 tok/s
Faster than 18% of 250 models
Time to first token
2.5 s
Quicker than 67% of 250 models
Prompt cache hits
68.1%
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-chat",
    "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-chat",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

DeepSeek

Other DeepSeek models, starting with the same family.