DeepSeek V4 Flash 0731 Intelligence, Performance and Price Analysis

Artificial Analysis DeepSeek • Open weights model • Released July 2026 DeepSeek V4 Flash 0731 (Reasoning, Max Effort) 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 (Reasoning, Max Effort) is amongst the leading models in intelligence and well 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 0731 (Reasoning, Max Effort) scores 50 on the Artificial Analysis Intelligence Index, placing it well above average among comparable models (median: 25). When evaluating the Intelligence Index, it generated 210M tokens, which is very verbose in comparison to the median of 100M. Pricing for DeepSeek V4 Flash 0731 (Reasoning, Max Effort) is $0.14 per 1M input tokens (competitively priced, median: $0.43) and $0.28 per 1M output tokens (competitively priced, median: $1.20). In total, it cost $72.02 to evaluate DeepSeek V4 Flash 0731 (Reasoning, Max 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 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-AA New 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. Openness Index Artificial Analysis Openness Index: Score Openness Index assesses model openness on a 0 to 100 normalized scale (higher is more open) Reasoning models are indicated by a lightbulb icon 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 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. Model Size (Open Weights Models Only) Model Size: Total and Active Parameters Comparison between total model parameters and parameters active during inference Reasoning models are indicated by a lightbulb icon 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. Frequently Asked Questions Common questions about DeepSeek V4 Flash 0731 (Reasoning, Max Effort) DeepSeek V4 Flash 0731 (Reasoning, Max Effort) was released on July 31, 2026. DeepSeek V4 Flash 0731 (Reasoning, Max Effort) was created by DeepSeek. DeepSeek V4 Flash 0731 (Reasoning, Max Effort) scores 50 on the Artificial Analysis Intelligence Index, placing it well above average among other open weight models of similar size (median: 25). DeepSeek V4 Flash 0731 (Reasoning, Max Effort) costs $0.14 per 1M input tokens (very competitive, median: $0.58) and $0.28 per 1M output tokens (very competitive, median: $2.20), based on DeepSeek's API. DeepSeek V4 Flash 0731 (Reasoning, Max Effort) 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 (Reasoning, Max Effort) generated 210M output tokens, which is at the higher end compared to other open weight models of similar size (median: 100M). Yes, DeepSeek V4 Flash 0731 (Reasoning, Max 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 0731 (Reasoning, Max Effort) supports text input. DeepSeek V4 Flash 0731 (Reasoning, Max Effort) supports text output. No, DeepSeek V4 Flash 0731 (Reasoning, Max Effort) does not support image input. It can only process text. No, DeepSeek V4 Flash 0731 (Reasoning, Max Effort) is not multimodal. It only supports text input. DeepSeek V4 Flash 0731 (Reasoning, Max 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 0731 (Reasoning, Max Effort) is open weights. The model weights are publicly available and can be downloaded for self-hosting. DeepSeek V4 Flash 0731 (Reasoning, Max Effort) has 284 billion parameters (13 billion active). DeepSeek V4 Flash 0731 (Reasoning, Max 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 0731 (Reasoning, Max Effort) is released under the Mit license. This license allows commercial use. View license DeepSeek V4 Flash 0731 (Reasoning, Max Effort) 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 (Reasoning, Max Effort) is available via API through 1 provider. Compare API providers DeepSeek V4 Flash 0731 (Reasoning, Max Effort) is available through 1 API provider. Compare providers