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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 R1 0528DeepSeekRemove
ProviderDeepSeekDeepSeek
Discovery tierOpen weightsOpen weights
AddedMay 3, 2026May 28, 2025
Limits
Context window1.05MBest163.84K
Max output384KBest32.77K
Average output—1K tokens
Pricing, USD per 1M tokens
Input$0.23Best$0.50
Output$0.69Best$2.15
Cache read$0.0073Best$0.35
Performance
Throughput74.3 tok/sBest33.0 tok/s
Time to first token2.7 s1.3 sBest
Request success (24h)99.7%99.9%Best
Benchmark indexes
Intelligence Index36.0Best13.1
Coding Index68.8—
Math Index—76.0
Capabilities
ReasoningYesYes
ToolsYesYes
Structured outputYesNo
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 R1 0528

Intelligence Index

  • DeepSeek Latest36.0
  • DeepSeek R1 052813.1

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 R1 0528: —

  • Math Index

    Composite of the math evaluations

    • DeepSeek Latest: —
    • DeepSeek R1 0528: 76.0

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • DeepSeek Latest: 56.7%
    • DeepSeek R1 0528: —

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • DeepSeek Latest: 25.9%
    • DeepSeek R1 0528: —

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • DeepSeek Latest: 1,256 Elo
    • DeepSeek R1 0528: —

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • DeepSeek Latest: 1,449 Elo
    • DeepSeek R1 0528: —

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • DeepSeek Latest: 11.4%
    • DeepSeek R1 0528: —

  • AA-LCRv1.1

    Reasoning across long documents

    • DeepSeek Latest: 80.3%Top
    • DeepSeek R1 0528: 55.7%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • DeepSeek Latest: 41.0%Top
    • DeepSeek R1 0528: 15.8%

  • GPQA Diamond

    Graduate-level science questions

    • DeepSeek Latest: 92.8%Top
    • DeepSeek R1 0528: 81.3%

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • DeepSeek Latest: —
    • DeepSeek R1 0528: 84.9%

  • IFBench

    Following precise output instructions

    • DeepSeek Latest: —
    • DeepSeek R1 0528: 39.6%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • DeepSeek Latest: 14.1%
    • DeepSeek R1 0528: —

  • SciCode

    Scientific computing in Python

    • DeepSeek Latest: 51.0%
    • DeepSeek R1 0528: —

  • LiveCodeBench

    Recent competitive programming problems

    • DeepSeek Latest: —
    • DeepSeek R1 0528: 77.0%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • DeepSeek Latest: —
    • DeepSeek R1 0528: 76.0%

  • AIME

    Competition mathematics

    • DeepSeek Latest: —
    • DeepSeek R1 0528: 89.3%

  • MATH-500

    Competition math problems

    • DeepSeek Latest: —
    • DeepSeek R1 0528: 98.3%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • DeepSeek Latest: —
    • DeepSeek R1 0528: 15.9%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • DeepSeek Latest: —
    • DeepSeek R1 0528: 36.5%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • DeepSeek Latest: 80.3%Top
    • DeepSeek R1 0528: 55.7%

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
70.1%
About $0.0738 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.