DeepSeek V4.1 Flash 1017 (Max)

Artificial AnalysisK

Artificial AnalysisModels

Coding Agents

Image, Speech, Video

Inference

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DeepSeek

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Model summary

Intelligence

#2 / 121

45

Artificial Analysis Intelligence Index

4 out of 4 units for Intelligence.

Speed

#6 / 79

230.4

Output tokens per second

4 out of 4 units for Speed.

Cost

#21 / 43

In $0.30Out $1.20Cache Discount 98%

$0.26

Cost per Intelligence Index task

2 out of 4 units for Cost.

Verbosity

#39 / 47

210M

Output tokens from Intelligence Index

4 out of 4 units for Verbosity.

Comparison Summary

DeepSeek V4.1 Flash 1017 (Max) is amongst the leading models in intelligence and reasonably priced when comparing to other open weight models of similar size. It's also notably fast, however very verbose. The model supports text and image input, outputs text, and has a 1M tokens context window.

DeepSeek V4.1 Flash 1017 (Max) scores 45 on the Artificial Analysis Intelligence Index, placing it well above average among comparable models (median: 19). When evaluating the Intelligence Index, it generated 210M tokens, which is very verbose in comparison to the median of 140M.

Pricing for DeepSeek V4.1 Flash 1017 (Max) is $0.30 per 1M input tokens (moderately priced, median: $0.43) and $1.20 per 1M output tokens (moderately priced, median: $1.68). On average, it costs $0.26 per task to evaluate DeepSeek V4.1 Flash 1017 (Max) on the Intelligence Index.

At 230 tokens per second, DeepSeek V4.1 Flash 1017 (Max) is notably fast (78).

ReasoningYesThis page shows the reasoning version of this model.

Context window1M~1500 A4 pages of size 12 Arial font

Total parameters552BActive parameters16BNumber of parameters active per token during inference

LicenseMITModel weightsHugging Face

122 models in this class

Metrics are compared against models of the same class:

Intelligence

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

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Artificial Analysis Intelligence Index

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.

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No publicly available API

ProprietaryOpen WeightsOpen Weights (Commercial Use Restricted)

Artificial Analysis Intelligence Index

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.

Open Weights

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.

27 of 705 models

Most attractive quadrant

Pareto line

StepFunZ AIXiaomiMiniMaxOpenAIAnthropicMistralMetaSpaceXAIReflectionGoogleAlibabaNVIDIADeepSeekKimi

Cost per Intelligence Index Task

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.

Artificial Analysis Intelligence Index

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.

Measures the performance of models on specific capabilities and industries

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

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Benchmarks

Evaluations

Intelligence evaluations measured independently by Artificial Analysis · Higher is better

CodingAgenticTool UsePrivate DatasetUser InteractionFinanceMedicalLegalIntelligence IndexLong ContextMultimodalInstruction FollowingFaithfulnessWritingBusinessSee more

19 of 28 evaluations27 of 705 models

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AA-Briefcase v1.1Updated

Agentic knowledge work, (Elo-500)/2000

AA-Omniscience Non-Hallucination Rate

1 - hallucination rate

AutomationBench-AA

Agentic SaaS workflows

GDPval-AA v2.1Updated

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

Humanity's Last Exam

Reasoning & knowledge

GDP.pdf

Professional document reasoning, All-pass

Terminal-Bench 4.0

Agentic coding & terminal use

Terminal-Bench-Science 0.1New

Agentic scientific research workflows in a terminal

FrontierCode 1.1New

Agentic software engineering in open-source repositories

AA-AnalystAgent

Quantitative analysis on spreadsheets & documents

Harvey LAB-AA v1.1Updated

Legal agentic work, Hallucination-Gated All-Pass Rate

EnterpriseOps-Gym-AA

Agentic business operations

ITBench-AA

Kubernetes incident root-cause analysis

Intelligence Evaluation Relevance

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

Artificial Analysis Intelligence Index

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.

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AA-Briefcase Elo

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.

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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.

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Intelligence Index Comparisons

Intelligence Index vs. Cost per TaskIntelligence Index vs. Time per TaskIntelligence Index vs. Output SpeedIntelligence Index vs. End-to-End Response Time

Intelligence Index vs. Cost per Task

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

27 of 705 models

Most attractive quadrant

Pareto line

StepFunZ AIXiaomiMiniMaxOpenAIAnthropicMistralMetaSpaceXAIReflectionGoogleAlibabaNVIDIADeepSeekKimi

Cost per Intelligence Index Task

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.

Artificial Analysis Intelligence Index

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.

27 of 705 models

No publicly available API

AnswerReasoning

Output Tokens per Intelligence Index Task

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).

27 of 705 models

AnswerReasoningCache WriteCache HitInput

Cost per Intelligence Index Task

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.

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OutputReasoningCache WriteCache ReadNon-Cache Input

Cost to Run 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).

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Cache HitInputOutput

Cache Hit

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.

4 more notes

Context Window

Context WindowIntelligence Index vs. Context Window

Context Window

Context window: tokens limit · Higher is better

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Context Window for RAG

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

Context Window

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

Measured by Output Speed (tokens per second)

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Output Speed

Tokens per second received while the model is generating tokens (ie. after first chunk has been received from the API for models which support streaming).

Model Performance Representation

Figures represent performance of the model's first-party API or the median across providers where a first-party API is not available.

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Time per Intelligence Index Task

The weighted average time (seconds) per Artificial Analysis Intelligence Index task. This is calculated by dividing output tokens per task by output speed, weighted by the relative weights of each benchmark in the Intelligence Index.

Measured by Time (seconds) to First Token

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Thinking (reasoning models, when applicable)Input processing

Time to First Answer Token

Time to first answer token received, in seconds, after API request sent. For reasoning models, this includes the 'thinking' time of the model before providing an answer. For models which do not support streaming, this represents time to receive the completion.

Seconds to output 500 tokens, calculated based on time to first token, 'thinking' time for reasoning models, and output speed

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Outputting time'Thinking' time (reasoning models)Input processing time

End-to-End Response Time

Seconds to receive a 500 token response. Key components:

Model Performance Representation

Figures represent performance of the model's first-party API or the median across providers where a first-