# Inception Mercury 2.5 评测页：输出速度 780.8 token/秒，智能指数 12 - 来源：Hacker News 热门（buzzing.cc 中文翻译） - 作者：Retro_Dev - 发布时间：2026-09-24 16:40 - AIHOT 分数：51 - AIHOT 链接：https://aihot.news/items/cmufawsi3045nroag314hzs8v - 原文链接：https://artificialanalysis.ai/models/mercury-2-5 ## AI 摘要 Artificial Analysis 页面收录了 Inception 于 2026 年 9 月 8 日发布的 Mercury 2.5，这是一款专有推理模型，输出速度达 780.8 token/秒，远高于同价位推理模型中位数 110.3。 ## 正文 Model summary IntelligenceUpdated 12 Artificial Analysis Intelligence Index 2 out of 4 units for Intelligence. Speed 780.8 Output tokens per second 4 out of 4 units for Speed. Cost In $0.25 Out $0.75 Cache Discount 90% $0.06 Cost per Intelligence Index task 2 out of 4 units for Cost. Verbosity 35M Output tokens from Intelligence Index 2 out of 4 units for Verbosity. Mercury 2.5 is below average in intelligence, but well priced when comparing to other models of similar price. It's also notably fast and fairly concise. The model supports text input, outputs text, and has a 260k tokens context window. Mercury 2.5 scores 12 on the Artificial Analysis Intelligence Index, placing it below average among comparable models (median: 12). When evaluating the Intelligence Index, it generated 35M tokens, which is fairly concise in comparison to the median of 85M. Pricing for Mercury 2.5 is $0.25 per 1M input tokens (moderately priced, median: $0.25) and $0.75 per 1M output tokens (moderately priced, median: $0.91). On average, it costs $0.06 per task to evaluate Mercury 2.5 on the Intelligence Index. At 781 tokens per second, Mercury 2.5 is notably fast (110). 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 window260k ~390 A4 pages of size 12 Arial font 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 Intelligence Speed Cost per Task IntelligenceUpdated Artificial Analysis Intelligence Index Artificial Analysis Intelligence Index by Open Weights / Proprietary Capability Indexes Artificial Analysis Finance & Accounting Index Benchmarks Intelligence Evaluations Under review Legal agentic work, criterion pass rate EnterpriseOps-Gym-AA Agentic business operations 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. 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 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 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). Speed Measured by Output Speed (tokens per second) 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). Figures represent performance of the model's first-party API or the median across providers where a first-party API is not available. Time per Intelligence Index Task Weighted average decode time (minutes) per task; excludes TTFT and overhead time · Lower is better 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 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 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 or the median across providers where a first-party API is not available. Frequently Asked Questions Common questions about Mercury 2.5 Mercury 2.5 was released on September 8, 2026. Mercury 2.5 was created by Inception. Mercury 2.5 scores 12 on the Artificial Analysis Intelligence Index, placing it below average among other reasoning models in a similar price tier (median: 12). Mercury 2.5 generates output at 780.8 tokens per second (based on Inception's API), which is well above average compared to other reasoning models in a similar price tier (median: 110.3 t/s). Mercury 2.5 has a time to first token (TTFT) of 2.91s (based on Inception's API), which is somewhat higher than average compared to other reasoning models in a similar price tier (median: 2.22s). Mercury 2.5 costs $0.25 per 1M input tokens (better than average, median: $0.25) and $0.75 per 1M output tokens (better than average, median: $0.91), based on Inception's API. Mercury 2.5 costs $0.25 per 1M input tokens and $0.75 per 1M output tokens (based on Inception's API). For a blended rate (7:2:1 cache hit/input/output ratio), this is $0.14 per 1M tokens. Pricing may vary by provider. Compare provider pricing When evaluated on the Intelligence Index, Mercury 2.5 generated 35M output tokens, which is better than average compared to other reasoning models in a similar price tier (median: 85M). Yes, Mercury 2.5 is a reasoning model. It uses extended thinking or chain-of-thought reasoning to work through complex problems before providing an answer. Mercury 2.5 supports text input. Mercury 2.5 supports text output. No, Mercury 2.5 does not support image input. It can only process text. No, Mercury 2.5 is not multimodal. It only supports text input. Mercury 2.5 has a context window of 260k tokens. This determines how much text and conversation history the model can process in a single request. No, Mercury 2.5 is proprietary. The model weights are not publicly available. Mercury 2.5 is a proprietary model and Inception has not disclosed the model size or parameter count. Mercury 2.5 achieves a score of 12 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across reasoning, knowledge, mathematics, and coding. Yes, Mercury 2.5 is available via API through 1 provider. Compare API providers Mercury 2.5 is available through 1 API provider. 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