Mercury 2.5 LLM hits 770 tokens per second

Proprietary model Released September 2026 Mercury 2.5 Intelligence, Performance & Price Analysis Model summary IntelligenceUpdated Speed Cost Verbosity Comparison Summary 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: 13). 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: $0.90). On average, it costs $0.06 per task to evaluate Mercury 2.5 on the Intelligence Index. At 770 tokens per second, Mercury 2.5 is notably fast (109). Technical specifications This page shows the reasoning version of this model. A non-reasoning variant may also exist. Supports: text Supports: text 175 models in this class 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 Speed Cost per Task IntelligenceUpdated Artificial Analysis Intelligence Index 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. Artificial Analysis Intelligence Index by Open Weights / Proprietary 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. Capability Indexes Measures the performance of models on specific capabilities and industries Artificial Analysis Finance & Accounting Index Benchmarks Intelligence Evaluations Agentic knowledge work, (Elo-500)/2000 Agentic real-world work tasks, (Elo-500)/2000 Agentic SaaS workflows Agentic coding & terminal use Coding Reasoning & knowledge Professional document reasoning, All-pass Physics reasoning Knowledge 1 - hallucination rate Long context reasoning Legal agentic work, criterion pass rate Agentic business operations Quantitative analysis on spreadsheets & documents Agentic tool use Kubernetes incident root-cause analysis Visual reasoning Medical long context reasoning 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. AA-Briefcase v1.1Updated AA-Briefcase Elo 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. AA-Omniscience AA-Omniscience Index 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. Intelligence Index Comparisons Intelligence Index vs. Cost per Intelligence Index Task 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. Token Use Output Tokens per Intelligence Index Task 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). Cost Cost per Intelligence Index Task 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. Cost to Run Artificial Analysis Intelligence Index 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). Pricing: Cache Hit, Input, and Output 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. Context Window Context Window 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). Speed Measured by Output Speed (tokens per second) Output Speed 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. Time per Intelligence Index Task 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. Latency Measured by Time (seconds) to First Token Latency: Time To First Answer Token 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. 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 End-to-End Response Time 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 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. Frequently Asked Questions Common questions about Mercury 2.5 When was Mercury 2.5 released? Mercury 2.5 was released on September 8, 2026. Who created Mercury 2.5? Mercury 2.5 was created by Inception. How intelligent is Mercury 2.5? 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: 13). How fast is Mercury 2.5? Mercury 2.5 generates output at 770.4 tokens per second (based on Inception's API), which is well above average compared to other reasoning models in a similar price tier (median: 108.6 t/s). What is the latency of Mercury 2.5? 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). How much does Mercury 2.5 cost? 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: $0.90), based on Inception's API. What is Mercury 2.5 API pricing? 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 How verbose is Mercury 2.5? 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). Is Mercury 2.5 a reasoning model? 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. What input modalities does Mercury 2.5 support? Mercury 2.5 supports text input. What output modalities does Mercury 2.5 support? Mercury 2.5 supports text output. Can Mercury 2.5 process images? No, Mercury 2.5 does not support image input. It can only process text. Is Mercury 2.5 multimodal? No, Mercury 2.5 is not multimodal. It only supports text input. What is the context window of Mercury 2.5? 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. Is Mercury 2.5 open source? No, Mercury 2.5 is proprietary. The model weights are not publicly available. How many parameters does Mercury 2.5 have? Mercury 2.5 is a proprietary model and Inception has not disclosed the model size or parameter count. How does Mercury 2.5 perform on benchmarks? 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. Is Mercury 2.5 available via API? Yes, Mercury 2.5 is available via API through 1 provider. Compare API providers Where can I use Mercury 2.5? Mercury 2.5 is available through 1 API provider. Compare providers