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DeepSeek · Open weights

DeepSeek V4 Flash 0423

Added Oct 7, 2026Open weights

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Use in the app

The original 0423 checkpoint of DeepSeek V4 Flash, with a 1M-token context window, text input, tool calling, structured output, and optional reasoning.

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

Pricing

USD per 1M tokens
Input
$0.14
Output
$0.28
Cache read
$0.028

Reference prices as published by the model provider. API example

Specifications

Context window
1M
Max output
—
Average output (7 days)
245 tokens
Longer than 26% of 465 models

Side by side

DeepSeek V4 Flash 0423 compared

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

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DeepSeek V4 Flash 0423DeepSeekThis modelGLM 4.6 Turbo (Thinking)Z.aiRemove
ProviderDeepSeekZ.ai
Discovery tierOpen weightsOpen weights
AddedOct 7, 2026Oct 2, 2025
Limits
Context window1.05MBest204.8K
Max output—131.07K
Average output245 tokens1.9K tokens
Pricing, USD per 1M tokens
Input$0.14Best$1.00
Output$0.28Best$3.00
Cache read$0.028—
Performance
Throughput131.1 tok/s—
Time to first token2.0 s—
Request success (24h)98.3%—
Benchmark indexes
Intelligence Index18.9Best18.5
Coding Index—45.8
Math Index—86.0
Capabilities
ReasoningYesYes
ToolsYesNo
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 V4 Flash 0423
  • GLM 4.6 Turbo (Thinking)

Intelligence Index

  • DeepSeek V4 Flash 042318.9
  • GLM 4.6 Turbo (Thinking)18.5

DeepSeek V4 Flash 0423: Better than 48% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • DeepSeek V4 Flash 0423: —
    • GLM 4.6 Turbo (Thinking): 45.8

  • Math Index

    Composite of the math evaluations

    • DeepSeek V4 Flash 0423: —
    • GLM 4.6 Turbo (Thinking): 86.0

Agentic work

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • DeepSeek V4 Flash 0423: —
    • GLM 4.6 Turbo (Thinking): 756 Elo

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • DeepSeek V4 Flash 0423: 41.7%
    • GLM 4.6 Turbo (Thinking): 54.0%Top

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • DeepSeek V4 Flash 0423: 7.8%
    • GLM 4.6 Turbo (Thinking): 14.5%Top

  • GPQA Diamond

    Graduate-level science questions

    • DeepSeek V4 Flash 0423: 71.6%
    • GLM 4.6 Turbo (Thinking): 78.0%Top

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • DeepSeek V4 Flash 0423: —
    • GLM 4.6 Turbo (Thinking): 82.9%

  • IFBench

    Following precise output instructions

    • DeepSeek V4 Flash 0423: 47.2%Top
    • GLM 4.6 Turbo (Thinking): 43.4%

Coding

  • LiveCodeBench

    Recent competitive programming problems

    • DeepSeek V4 Flash 0423: —
    • GLM 4.6 Turbo (Thinking): 69.5%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • DeepSeek V4 Flash 0423: —
    • GLM 4.6 Turbo (Thinking): 86.0%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • DeepSeek V4 Flash 0423: 34.1%Top
    • GLM 4.6 Turbo (Thinking): 25.0%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • DeepSeek V4 Flash 0423: 94.4%Top
    • GLM 4.6 Turbo (Thinking): 70.5%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • DeepSeek V4 Flash 0423: 41.7%
    • GLM 4.6 Turbo (Thinking): 54.0%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
131.1 tok/s
Faster than 72% of 250 models
Time to first token
2.0 s
Quicker than 78% of 250 models
Prompt cache hits
92.6%
About $0.0363 per 1M input tokens with caching
Request success (24h)
98.4%
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 91.3%
  • 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-v4-flash-0423",
    "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-v4-flash-0423",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

DeepSeek

Other DeepSeek models, starting with the same family.