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DeepSeek · Open weights

DeepSeek Latest

Added May 3, 2026Open weights

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Use in the app

Compatibility alias that routes to the newest thinking DeepSeek model. Currently routes to DeepSeek V4 Pro 0813 Thinking, the general-availability release built for coding, tool use, cybersecurity, automation, and long-horizon agent workflows. Requests are routed through privacy-friendly third-party providers and are not sent directly to DeepSeek.

  • Reasoning
  • Tools
  • Structured output
Input
Text
Output
Text
Reasoning effort
none · low · high · max
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.23
Output
$0.69
Cache read
$0.0073

Reference prices as published by the model provider. API example

Specifications

Context window
1M
Max output
384K

Side by side

DeepSeek Latest compared

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

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DeepSeek LatestDeepSeekThis modelDeepSeek V3/Deepseek ChatDeepSeekRemove
ProviderDeepSeekDeepSeek
Discovery tierOpen weightsOpen weights
AddedMay 3, 2026Feb 27, 2025
Limits
Context window1.05MBest128K
Max output384KBest8.19K
Average output—293 tokens
Pricing, USD per 1M tokens
Input$0.23$0.10Best
Output$0.69$0.43Best
Cache read$0.0073—
Performance
Throughput74.3 tok/sBest27.1 tok/s
Time to first token2.7 s2.5 sBest
Request success (24h)99.7%99.9%Best
Benchmark indexes
Intelligence Index36.0Best16.0
Coding Index68.8—
Math Index—59.0
Capabilities
ReasoningYesNo
ToolsYesYes
Structured outputYesYes
PDF inputNoYes
Reasoning effortnone, low, high, max—

Public evaluations

Benchmarks

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

  • DeepSeek Latest
  • DeepSeek V3/Deepseek Chat

Intelligence Index

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

DeepSeek Latest tested at max reasoning effort

DeepSeek Latest: Better than 79% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

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

  • Math Index

    Composite of the math evaluations

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

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

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

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

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

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

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

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

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

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

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

  • AA-LCRv1.1

    Reasoning across long documents

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

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

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

  • GPQA Diamond

    Graduate-level science questions

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

  • MMLU-Pro

    Multi-subject knowledge and reasoning

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

  • IFBench

    Following precise output instructions

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

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

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

  • SciCode

    Scientific computing in Python

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

  • LiveCodeBench

    Recent competitive programming problems

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

Math

  • AIME 2025

    Competition mathematics, 2025 problems

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

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

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

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

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

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

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

Scores from Artificial Analysis, updated Oct 26, 2026. DeepSeek Latest was tested at max reasoning effort; compare scores measured at the same effort.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
74.3 tok/s
Faster than 50% of 250 models
Time to first token
2.7 s
Quicker than 62% of 250 models
Prompt cache hits
71.3%
About $0.0711 per 1M input tokens with caching
Request success (24h)
99.7%
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 96.8%
  • 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/deepseek-latest",
    "reasoning_effort": "none",
    "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/deepseek-latest",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

DeepSeek

Other DeepSeek models, starting with the same family.