DeepSeek V4 Flash 0731 logo
DeepSeek
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Proprietary model

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Released July 2026

DeepSeek V4 Flash 0731 Intelligence, Performance & Price Analysis

API Provider Benchmarks

Model summary

Intelligence

50
Artificial Analysis Intelligence Index
4 out of 4 units for Intelligence.

Speed

N/A
Output tokens per second
Unknown out of 4 units for Speed.

Price

Input
$0.14
per 1M tokens
Output
$0.28
per 1M tokens
1 out of 4 units for Price.

Cache Hit Price

$0.003
USD per 1M tokens
1 out of 4 units for Cache Hit Price.

Verbosity

210M
Output tokens from Intelligence Index
4 out of 4 units for Verbosity.

DeepSeek V4 Flash 0731 is amongst the leading models in intelligence and well priced when comparing to other models of similar price. The model supports text input, outputs text, and has a 1M tokens context window.

DeepSeek V4 Flash 0731 scores 50 on the Artificial Analysis Intelligence Index, placing it well above average among comparable models (median: 17). When evaluating the Intelligence Index, it generated 210M tokens, which is very verbose in comparison to the median of 62M.

Pricing for DeepSeek V4 Flash 0731 is $0.14 per 1M input tokens (competitively priced, median: $0.25) and $0.28 per 1M output tokens (competitively priced, median: $0.88). In total, it cost $72.02 to evaluate DeepSeek V4 Flash 0731 on the Intelligence Index.

ReasoningYes

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 window1M
~1500 A4 pages of size 12 Arial font

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

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

Intelligence

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v4.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR
Reasoning models are indicated by a lightbulb icon

Artificial Analysis Intelligence Index v4.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. 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

Artificial Analysis Intelligence Index v4.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR
Reasoning models are indicated by a lightbulb icon

Artificial Analysis Intelligence Index v4.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Benchmarks

Intelligence Evaluations

Intelligence evaluations measured independently by Artificial Analysis · Higher is better
GDPval-AA v2

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

𝜏³-Banking

Agentic tool use

Terminal-Bench v2.1

Agentic coding & terminal use

SciCode

Coding

Humanity's Last Exam

Reasoning & knowledge

GPQA Diamond

Scientific reasoning

CritPt

Physics reasoning

AA-Omniscience Accuracy

Knowledge

AA-Omniscience Non-Hallucination Rate

1 - hallucination rate

AA-LCR

Long context reasoning

AA-Briefcase

Agentic knowledge work, Elo

AutomationBench-AA

Agentic SaaS workflows

Harvey LAB-AA

Legal agentic work, criterion pass rate

EnterpriseOps-Gym-AANew

Agentic business operations

IFBench

Instruction following

APEX-Agents-AA

Long-horizon agentic tasks

ITBench-AA

Kubernetes incident root-cause analysis

MMMU-Pro

Visual reasoning

Reasoning models are indicated by a lightbulb icon

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

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.
Reasoning models are indicated by a lightbulb icon

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.

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
Reasoning models are indicated by a lightbulb icon

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
Reasoning models are indicated by a lightbulb icon

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
Reasoning models are indicated by a lightbulb icon

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.

Cost to Run Artificial Analysis Intelligence Index

Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index
Reasoning models are indicated by a lightbulb icon

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)
Reasoning models are indicated by a lightbulb icon

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

Context window: tokens limit · Higher is better
Reasoning models are indicated by a lightbulb icon

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

Frequently Asked Questions

Common questions about DeepSeek V4 Flash 0731

DeepSeek V4 Flash 0731 was released on July 31, 2026.

DeepSeek V4 Flash 0731 was created by DeepSeek.

DeepSeek V4 Flash 0731 scores 50 on the Artificial Analysis Intelligence Index, placing it well above average among other reasoning models in a similar price tier (median: 17).

DeepSeek V4 Flash 0731 costs $0.14 per 1M input tokens (very competitive, median: $0.25) and $0.28 per 1M output tokens (very competitive, median: $0.88), based on DeepSeek's API.

DeepSeek V4 Flash 0731 costs $0.14 per 1M input tokens and $0.28 per 1M output tokens (based on DeepSeek's API). For a blended rate (7:2:1 cache hit/input/output ratio), this is $0.06 per 1M tokens. Pricing may vary by provider. Compare provider pricing

When evaluated on the Intelligence Index, DeepSeek V4 Flash 0731 generated 210M output tokens, which is at the higher end compared to other reasoning models in a similar price tier (median: 62M).

Yes, DeepSeek V4 Flash 0731 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 0731 supports text input.

DeepSeek V4 Flash 0731 supports text output.

No, DeepSeek V4 Flash 0731 does not support image input. It can only process text.

No, DeepSeek V4 Flash 0731 is not multimodal. It only supports text input.

DeepSeek V4 Flash 0731 has a context window of 1.0M tokens. This determines how much text and conversation history the model can process in a single request.

No, DeepSeek V4 Flash 0731 is proprietary. The model weights are not publicly available.

DeepSeek V4 Flash 0731 has 284 billion parameters.

DeepSeek V4 Flash 0731 achieves a score of 50 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across reasoning, knowledge, mathematics, and coding.

Yes, DeepSeek V4 Flash 0731 is available via API through 1 provider. Compare API providers

DeepSeek V4 Flash 0731 is available through 1 API provider. Compare providers