待翻譯：DeepSeek V4 Flash Vision Intelligence, Performance and Price Analysis

AI 服務暫時不可用，以下為來源正文，待恢復後補全翻譯。

Artificial Analysis DeepSeek • DeepSeek V4 Flash 0731 • Proprietary model • Released August 2026 DeepSeek V4 Flash Vision (Reasoning, Max Effort) Intelligence, Performance & Price Analysis API Provider Benchmarks Model summary Intelligence 51 Artificial Analysis Intelligence Index 4 out of 4 units for Intelligence. Speed 120.0 Output tokens per second 3 out of 4 units for Speed. Cost In $0.44Out $1.32Cache Discount 97% $0.12 Cost per Intelligence Index task 3 out of 4 units for Cost. Verbosity 130M Output tokens from Intelligence Index 4 out of 4 units for Verbosity. DeepSeek V4 Flash Vision (Reasoning, Max Effort) is amongst the leading models in intelligence and reasonably priced when comparing to other models of similar price. It's also faster than average, however very verbose. The model supports text and image input, outputs text, and has a 1M tokens context window. DeepSeek V4 Flash Vision (Reasoning, Max Effort) scores 51 on the Artificial Analysis Intelligence Index, placing it well above average among comparable models (median: 17). When evaluating the Intelligence Index, it generated 130M tokens, which is very verbose in comparison to the median of 61M. Pricing for DeepSeek V4 Flash Vision (Reasoning, Max Effort) is $0.44 per 1M input tokens (expensive, median: $0.25) and $1.32 per 1M output tokens (somewhat expensive, median: $0.91). In total, it cost $235.89 to evaluate DeepSeek V4 Flash Vision (Reasoning, Max Effort) on the Intelligence Index. At 120 tokens per second, DeepSeek V4 Flash Vision (Reasoning, Max Effort) is faster than average (109). ReasoningYes This page shows the reasoning version of this model. A non-reasoning variant may also exist. Input modality Supports: text and image 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: $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 v4.1.1 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 v4.1.1 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 v4.1.1 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 v4.1.1 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. 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. Benchmarks Intelligence Evaluations Intelligence evaluations measured independently by Artificial Analysis · Higher is better See more GDPval-AA v2 Agentic real-world work tasks, (Elo-500)/2000 𝜏³-BankingUpdated Agentic tool use Terminal-Bench v2.1 Agentic coding & terminal use SciCode Coding Humanity's Last ExamUpdated Reasoning & knowledge GPQA Diamond Scientific reasoning CritPt Physics reasoning AA-Omniscience AccuracyUpdated Knowledge AA-Omniscience Non-Hallucination RateUpdated 1 - hallucination rate AA-LCRUpdated 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 Agentic business operations AA-AnalystAgentNew Quantitative analysis on spreadsheets & documents 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. Artificial Analysis Intelligence Index v4.1.1 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. 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 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. Artificial Analysis Intelligence Index v4.1.1 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. 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. Maximum number of combined input & output tokens. Output tokens commonly have a significantly lower limit (varied by model). Speed Measured by Output Speed (tokens per second) Output Speed Output tokens per second · Higher is better Reasoning models are indicated by a lightbulb icon 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). Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models). Time per Intelligence Index Task Weighted average decode time (minutes) per task; excludes TTFT and overhead time · Lower is better Reasoning models are indicated by a lightbulb icon 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. Latency Measured by Time (seconds) to First Token Latency: Time To First Answer Token Seconds to first answer token received · Accounts for reasoning model 'thinking' time Reasoning models are indicated by a lightbulb icon 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. End-to-End Response Time Seconds to output 500 tokens, calculated based on time to first token, 'thinking' time for reasoning models, and output speed End-to-End Response Time Seconds to output 500 tokens, including reasoning model 'thinking' time · Lower is better Reasoning models are indicated by a lightbulb icon Seconds to receive a 500 token response. Key components: Input time: Time to receive the first response token Thinking time (only for reasoning models): Time reasoning models spend outputting tokens to reason prior to providing an answer. Amount of tokens based on the average reasoning tokens across a diverse set of 60 prompts (methodology details). Answer time: Time to generate 500 output tokens, based on output speed Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models). Frequently Asked Questions Common questions about DeepSeek V4 Flash Vision (Reasoning, Max Effort) DeepSeek V4 Flash Vision (Reasoning, Max Effort) was released on August 21, 2026. DeepSeek V4 Flash Vision (Reasoning, Max Effort) was created by DeepSeek. DeepSeek V4 Flash Vis [truncated for AI cost control]