This model is deprecated. We only continue performance benchmarking for the default 10k input token workload. Results for other workloads are historical and no longer updated.

DeepSeek has launched a newer release, DeepSeek V4 Flash 0731. We suggest considering it instead.

DeepSeek

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DeepSeek V4 Flash 0420

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Open weights model

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Released April 2026

DeepSeek V4 Flash 0420 (Reasoning, High Effort) Intelligence, Performance & Price Analysis

Name: Intelligence
Creator: Artificial Analysis
License: https://artificialanalysis.ai/docs/legal/Terms-of-Use.pdf

Try it out API Provider Benchmarks

Speed

N/A

Output tokens per second

Cost

In $0.112Out $0.238Cache Discount 75%

$0.13

Cost per Intelligence Index task

Verbosity

130M

Output tokens from Intelligence Index

DeepSeek V4 Flash 0420 (Reasoning, High Effort) is above average in intelligence and reasonably priced when comparing to other open weight models of similar size. The model supports text input, outputs text, and has a 1M tokens context window.

DeepSeek V4 Flash 0420 (Reasoning, High Effort) scores 24 on the Artificial Analysis Intelligence Index, placing it above average among comparable models (median: 18). When evaluating the Intelligence Index, it generated 130M tokens, which is fairly concise in comparison to the median of 140M.

Pricing for DeepSeek V4 Flash 0420 (Reasoning, High Effort) is $0.11 per 1M input tokens (moderately priced, median: $0.30) and $0.24 per 1M output tokens (moderately priced, median: $1.15). On average, it costs $0.13 per task to evaluate DeepSeek V4 Flash 0420 (Reasoning, High Effort) on the Intelligence Index.

Reasoning	Yes This page shows the reasoning version of this model. A non-reasoning variant may also exist.
Input modality	Supports: text
Output modality	Supports: text
Context window	1M ~1500 A4 pages of size 12 Arial font
Total parameters	284B
Active parameters	13B Number of parameters active per token during inference
License	MIT
Model weights	Hugging Face

Metrics are compared against models of the same class:

Non-reasoning models → compared only with other non-reasoning models
Reasoning models → compared across both reasoning and non-reasoning
Open weights models → compared only with other open weights models of the same size class:

Tiny: ≤4B parameters
Small: 4B–40B parameters
Medium: 40B–150B parameters
Large: >150B parameters

Proprietary models → compared across proprietary and open weights models of the same price range, using a blended 3:1 input/output price ratio:

<$0.15 per 1M tokens
$0.15–$1 per 1M tokens
>$1 per 1M tokens

Highlights

Updated

Intelligence

Artificial Analysis Intelligence Index · Higher is better

Speed

Output tokens per second · Higher is better

Cost per Task

Weighted average cost (USD) per Intelligence Index task · Lower is better

IntelligenceUpdated

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v4.3.2 incorporates 10 evaluations: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1

Artificial Analysis Intelligence Index v4.3.2 includes: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Artificial Analysis Intelligence Index by Open Weights / Proprietary

Indicates whether the model weights are available. Models are labelled as 'Commercial Use Restricted' if commercial use is limited by conditions, and as 'Non-commercial' if the license prohibits commercial use.

Capability Indexes

Measures the performance of models on specific capabilities and industries

Artificial Analysis Finance & Accounting Index

Incorporates 7 evaluations: AA-Omniscience, GDPval-AA v2.1, AA-Briefcase v1.1, Humanity's Last Exam, AutomationBench-AA, AA-LCR v1.1, GDP.pdf · Higher is better

Benchmarks

Intelligence Evaluations

Intelligence evaluations measured independently by Artificial Analysis · Higher is better

AA-Briefcase v1.1Updated

Agentic knowledge work, (Elo-500)/2000

GDPval-AA v2.1Updated

Agentic real-world work tasks, (Elo-500)/2000

AutomationBench-AAUpdated

Agentic SaaS workflows

Terminal-Bench 4.0New

Agentic coding & terminal use

SciCodeUnder review

Coding

Humanity's Last Exam

Reasoning & knowledge

GDP.pdfNew

Professional document reasoning, All-pass

CritPtUnder review

Physics reasoning

AA-Omniscience Accuracy

Knowledge

AA-Omniscience Non-Hallucination Rate

1 - hallucination rate

AA-LCR v1.1

Long context reasoning

Harvey LAB-AA

Legal agentic work, criterion pass rate

EnterpriseOps-Gym-AA

Agentic business operations

Terminal-Bench-Science 0.1New

Agentic scientific research workflows in a terminal

AA-AnalystAgent

Quantitative analysis on spreadsheets & documents

ITBench-AA

Kubernetes incident root-cause analysis

MMMU-Pro

Visual reasoning

MLCR-AA

Medical long context reasoning

While model intelligence generally translates across use cases, specific evaluations may be more relevant for certain use cases.

AA-Briefcase v1.1Updated

AA-Briefcase Elo

AA-Briefcase v1.1 is an agentic knowledge work benchmark developed by Artificial Analysis. AA-Briefcase Elo is a combined metric that aggregates rubric pass rate, analytical quality Elo and presentation Elo · Higher is better

AA-Briefcase Elo is a combined metric that aggregates analytical quality Elo, presentation Elo, and rubric pass rate, with rubric performance converted into Elo via synthetic head-to-head matches. Elo and 95% confidence interval bounds are clamped at 0.

AA-Omniscience

AA-Omniscience Index

AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct.

Openness Index

Artificial Analysis Openness Index: Score

Openness Index assesses model openness on a 0 to 100 normalized scale (higher is more open)

Intelligence Index Comparisons

Intelligence Index vs. Cost per Intelligence Index Task

Artificial Analysis Intelligence Index · Weighted average cost (USD) per Artificial Analysis Intelligence Index task

Most attractive quadrant

Pareto line

Weighted average cost per Intelligence Index task. Each evaluation’s cost is calculated from input, cache hit, cache write, reasoning, and answer token prices, divided by task count, and weighted by its Intelligence Index weight.

Token Use

Output Tokens per Intelligence Index Task

Weighted average number of output tokens used to run one task in the Artificial Analysis Intelligence Index

The number of tokens required per Intelligence Index task. This is calculated by multiplying the output tokens per eval by the relative weights of each benchmark in the Intelligence Index, then dividing by task count (excluding repeats).

Cost

Cost per Intelligence Index Task

Weighted average cost (USD) per Artificial Analysis Intelligence Index task, segmented by token type. Lower is better

Cost to Run Artificial Analysis Intelligence Index

Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index

The cost to run the evaluations in the Artificial Analysis Intelligence Index, calculated using the model's input, cache hit, cache write, reasoning, and answer token prices and the number of tokens used across evaluations (excluding repeats).

Pricing: Cache Hit, Input, and Output

Price (USD per M Tokens)

Price per token for cached prompts (previously processed), typically offering a significant discount compared to regular input price, represented as USD per million tokens. The values shown here are the cache hit price; cache write and cache storage are billed separately and vary by provider — see "Cache pricing by provider" for detail.

Context Window

Context window: tokens limit · Higher is better

Larger context windows are relevant to RAG (Retrieval Augmented Generation) LLM workflows which typically involve reasoning and information retrieval of large amounts of data.

Maximum number of combined input & output tokens. Output tokens commonly have a significantly lower limit (varied by model).

Model Size (Open Weights Models Only)

Model Size: Total and Active Parameters

Comparison between total model parameters and parameters active during inference (billions)

The total number of trainable weights and biases in the model, expressed in billions. These parameters are learned during training and determine the model's ability to process and generate responses.

The number of parameters actually executed during each inference forward pass, expressed in billions. For Mixture of Experts (MoE) models, a routing mechanism selects a subset of experts per token, resulting in fewer active than total parameters. Dense models use all parameters, so active equals total.

Frequently Asked Questions

Common questions about DeepSeek V4 Flash 0420 (Reasoning, High Effort)

DeepSeek V4 Flash 0420 (Reasoning, High Effort) was released on April 24, 2026.

DeepSeek V4 Flash 0420 (Reasoning, High Effort) was created by DeepSeek.

DeepSeek V4 Flash 0420 (Reasoning, High Effort) scores 24 on the Artificial Analysis Intelligence Index, placing it above average among other open weight models of similar size (median: 18).

DeepSeek V4 Flash 0420 (Reasoning, High Effort) costs $0.11 per 1M input tokens (very competitive, median: $0.44) and $0.24 per 1M output tokens (very competitive, median: $1.68), based on the median across providers serving the model.

DeepSeek V4 Flash 0420 (Reasoning, High Effort) costs $0.11 per 1M input tokens and $0.24 per 1M output tokens (based on the median across providers serving the model). For a blended rate (7:2:1 cache hit/input/output ratio), this is $0.07 per 1M tokens. Pricing may vary by provider. Compare provider pricing

When evaluated on the Intelligence Index, DeepSeek V4 Flash 0420 (Reasoning, High Effort) generated 130M output tokens, which is better than average compared to other open weight models of similar size (median: 140M).

Yes, DeepSeek V4 Flash 0420 (Reasoning, High Effort) is a reasoning model. It uses extended thinking or chain-of-thought reasoning to work through complex problems before providing an answer.

DeepSeek V4 Flash 0420 (Reasoning, High Effort) supports text input.

DeepSeek V4 Flash 0420 (Reasoning, High Effort) supports text output.

No, DeepSeek V4 Flash 0420 (Reasoning, High Effort) does not support image input. It can only process text.

No, DeepSeek V4 Flash 0420 (Reasoning, High Effort) is not multimodal. It only supports text input.

DeepSeek V4 Flash 0420 (Reasoning, High Effort) has a context window of 1.0M tokens. This determines how much text and conversation history the model can process in a single request.

Yes, DeepSeek V4 Flash 0420 (Reasoning, High Effort) is open weights. The model weights are publicly available and can be downloaded for self-hosting.

DeepSeek V4 Flash 0420 (Reasoning, High Effort) has 284 billion parameters (13 billion active).

DeepSeek V4 Flash 0420 (Reasoning, High Effort) is a Mixture of Experts (MoE) model with 284 billion total parameters, but only 13 billion active parameters are used during inference.

DeepSeek V4 Flash 0420 (Reasoning, High Effort) is released under the MIT license. This license allows commercial use. View license

DeepSeek V4 Flash 0420 (Reasoning, High Effort) achieves a score of 24 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across reasoning, knowledge, mathematics, and coding.

Yes, DeepSeek V4 Flash 0420 (Reasoning, High Effort) is available via API through 6 providers. Compare API providers

DeepSeek V4 Flash 0420 (Reasoning, High Effort) is available through 6 API providers. Compare providers

DeepSeek V4 Flash 0420 (Reasoning, High Effort) Intelligence, Performance & Price Analysis

Model summary

IntelligenceUpdated

Speed

Comparison Summary

Technical specifications

117 models in this class

Speed

IntelligenceUpdated

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index by Open Weights / Proprietary

Artificial Analysis Intelligence Index

Open Weights

Intelligence Evaluations

Intelligence Evaluation Relevance

Artificial Analysis Intelligence Index

AA-Briefcase v1.1Updated

AA-Briefcase Elo

AA-Briefcase Elo

AA-Omniscience

AA-Omniscience Index

AA-Omniscience Index

Openness Index

Artificial Analysis Openness Index: Score

Intelligence Index Comparisons

Intelligence Index vs. Cost per Intelligence Index Task

Cost per Intelligence Index Task

Artificial Analysis Intelligence Index

Token Use

Output Tokens per Intelligence Index Task

Output Tokens per Intelligence Index Task

Cost

Cost per Intelligence Index Task

Cost per Intelligence Index Task

Cost to Run Artificial Analysis Intelligence Index

Cost to Run Artificial Analysis Intelligence Index

Pricing: Cache Hit, Input, and Output

Cache Hit

Context Window

Context Window

Context Window for RAG

Context Window

Model Size (Open Weights Models Only)

Model Size: Total and Active Parameters

Total Parameters

Active Parameters at Inference Time

Frequently Asked Questions

When was DeepSeek V4 Flash 0420 (Reasoning, High Effort) released?

Who created DeepSeek V4 Flash 0420 (Reasoning, High Effort)?

How intelligent is DeepSeek V4 Flash 0420 (Reasoning, High Effort)?

How much does DeepSeek V4 Flash 0420 (Reasoning, High Effort) cost?

What is DeepSeek V4 Flash 0420 (Reasoning, High Effort) API pricing?

How verbose is DeepSeek V4 Flash 0420 (Reasoning, High Effort)?

Is DeepSeek V4 Flash 0420 (Reasoning, High Effort) a reasoning model?

What input modalities does DeepSeek V4 Flash 0420 (Reasoning, High Effort) support?

What output modalities does DeepSeek V4 Flash 0420 (Reasoning, High Effort) support?

Can DeepSeek V4 Flash 0420 (Reasoning, High Effort) process images?

Is DeepSeek V4 Flash 0420 (Reasoning, High Effort) multimodal?

What is the context window of DeepSeek V4 Flash 0420 (Reasoning, High Effort)?

Is DeepSeek V4 Flash 0420 (Reasoning, High Effort) open source?

How many parameters does DeepSeek V4 Flash 0420 (Reasoning, High Effort) have?

What are the active parameters of DeepSeek V4 Flash 0420 (Reasoning, High Effort)?

What is the license for DeepSeek V4 Flash 0420 (Reasoning, High Effort)?

How does DeepSeek V4 Flash 0420 (Reasoning, High Effort) perform on benchmarks?

Is DeepSeek V4 Flash 0420 (Reasoning, High Effort) available via API?

Where can I use DeepSeek V4 Flash 0420 (Reasoning, High Effort)?