--- title: "Mercury 2.5: Intelligence, Performance and Price Analysis" slug: mercury-2-5-intelligence-performance-and-price-analysis url: https://listedarticles.com/articles/mercury-2-5-intelligence-performance-and-price-analysis canonical_url: https://artificialanalysis.ai/models/mercury-2-5 content_type: research language: en published_at: 2026-09-23T00:00:00.000Z updated_at: 2026-09-24T00:25:08.710Z author: "Artificial Analysis" authored_by: human publisher: "Artificial Analysis" publisher_url: https://artificialanalysis.ai topics: ["AI", "LLMs", "Benchmarks", "Performance", "Machine Learning"] license: all-rights-reserved word_count: 2677 reading_minutes: 12 citation: "Artificial Analysis, Artificial Analysis. \"Mercury 2.5: Intelligence, Performance and Price Analysis.\" 23 Sept 2026. https://artificialanalysis.ai/models/mercury-2-5 (all-rights-reserved)" # The full text follows. The web page shows an extract and sends readers # to the source above; quote the citation and link the canonical URL. --- # Mercury 2.5: Intelligence, Performance and Price Analysis > Artificial Analysis profiles Inception's Mercury 2.5—Intelligence Index, ~770 output tokens/sec, pricing, and where the diffusion LLM sits on the quality-vs-speed frontier. # Mercury 2.5 Intelligence, Performance & Price Analysis Compare [Try it out ](https://artificialanalysis.ai/microevals)[API Provider Benchmarks ](https://artificialanalysis.ai/models/mercury-2-5/providers) ### Model summary #### [Intelligence](#intelligence) Updated # 91 / 175 12 Artificial Analysis Intelligence Index 2 out of 4 units for Intelligence. #### [Speed](#speed) # 2 / 175 770.4 Output tokens per second 4 out of 4 units for Speed. #### [Cost](#price-cost) # 21 / 175 In $0.25 Out $0.75 Cache Discount 90% $0.06 Cost per Intelligence Index task 2 out of 4 units for Cost. #### [Verbosity](#token-use) # 14 / 175 35M Output tokens from Intelligence Index 2 out of 4 units for 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 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 260k ~390 A4 pages of size 12 Arial font ### 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 [Model Comparison](https://artificialanalysis.ai/models/mercury-2-5)[API Provider Benchmarks](https://artificialanalysis.ai/models/mercury-2-5/providers) Highlights Updated ### [Intelligence](#intelligence) Artificial Analysis Intelligence Index · Higher is better ### [Speed](#speed) Output tokens per second · Higher is better ### [Cost per Task](#price-cost) Weighted average cost (USD) per Intelligence Index task · Lower is better [ Intelligence Updated ](#intelligence)[ Capability Indexes ](#capability-indices)[ Benchmarks ](#intelligence-breakdown)[ Intelligence Index Comparisons ](#intelligence-comparisons)[ Token Use ](#token-use)[ Cost ](#price-cost)[ Context Window ](#context-window)[ Speed ](#speed)[ Latency ](#latency)[ End-to-End Response Time ](#end-to-end-response-time) Prompt Options ## Intelligence Updated ### [Artificial Analysis Intelligence Index ](https://artificialanalysis.ai/evaluations/artificial-analysis-intelligence-index) Artificial Analysis Intelligence Index v4.3.2 incorporates 10 evaluations: 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 - 28 of 674 models Add model from specific provider ### 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](https://artificialanalysis.ai/methodology/intelligence-benchmarking) for further details, including a breakdown of each evaluation and how we run them. Open Weights / Proprietary Reasoning / Non-Reasoning Text Only / Multimodal Inputs ### Artificial Analysis Intelligence Index by Open Weights / Proprietary Artificial Analysis Intelligence Index v4.3.2 incorporates 10 evaluations: 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 28 of 674 models Add model from specific provider Proprietary Open Weights Open Weights (Commercial Use Restricted) ### 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](https://artificialanalysis.ai/methodology/intelligence-benchmarking) 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](https://artificialanalysis.ai/models/capabilities) Measures the performance of models on specific capabilities and industries Finance & Accounting Strategy & Ops Legal Engineering Economics ### [Artificial Analysis Finance & Accounting Index ](https://artificialanalysis.ai/models/capabilities/finance-and-accounting) Incorporates 7 evaluations: AA-Omniscience, GDPval-AA v2.1, AA-Briefcase v1.1, Humanity's Last Exam, AutomationBench-AA, AA-LCR v1.1, GDP.pdf · Higher is better 28 of 171 models Add model from specific provider ## [Benchmarks](https://artificialanalysis.ai/evaluations) ### Intelligence Evaluations Intelligence evaluations measured independently by Artificial Analysis · Higher is better Coding Agentic Tool Use Private Dataset User Interaction Finance Medical Legal Intelligence Index Long Context Multimodal Instruction Following Faithfulness Writing Business [See more ](https://artificialanalysis.ai/evaluations) 18 of 26 evaluations 28 of 674 models Add model from specific provider [AA-Briefcase v1.1 ](https://artificialanalysis.ai/evaluations/aa-briefcase) Updated Agentic knowledge work, (Elo-500)/2000 [GDPval-AA v2.1 ](https://artificialanalysis.ai/evaluations/gdpval-aa) Updated Agentic real-world work tasks, (Elo-500)/2000 [AutomationBench-AA ](https://artificialanalysis.ai/evaluations/automationbench-aa) Updated Agentic SaaS workflows [Terminal-Bench 4.0 ](https://artificialanalysis.ai/evaluations/terminalbench-4-0) New Agentic coding & terminal use [SciCode ](https://artificialanalysis.ai/evaluations/scicode) Coding [Humanity's Last Exam ](https://artificialanalysis.ai/evaluations/humanitys-last-exam) Reasoning & knowledge [GDP.pdf ](https://artificialanalysis.ai/evaluations/gdp-pdf) New Professional document reasoning, All-pass [CritPt ](https://artificialanalysis.ai/evaluations/critpt) Under review Physics reasoning [AA-Omniscience Accuracy ](https://artificialanalysis.ai/evaluations/omniscience) Knowledge [AA-Omniscience Non-Hallucination Rate ](https://artificialanalysis.ai/evaluations/omniscience) 1 - hallucination rate [AA-LCR v1.1 ](https://artificialanalysis.ai/evaluations/artificial-analysis-long-context-reasoning) Long context reasoning [Harvey LAB-AA ](https://artificialanalysis.ai/evaluations/harvey-lab-aa) Legal agentic work, criterion pass rate [EnterpriseOps-Gym-AA ](https://artificialanalysis.ai/evaluations/enterprise-ops-gym-aa) Agentic business operations [AA-AnalystAgent ](https://artificialanalysis.ai/evaluations/aa-analyst-agent) Quantitative analysis on spreadsheets & documents [𝜏³-Banking ](https://artificialanalysis.ai/evaluations/tau3-banking) Agentic tool use [ITBench-AA ](https://artificialanalysis.ai/evaluations/itbench-aa) Kubernetes incident root-cause analysis [MMMU-Pro ](https://artificialanalysis.ai/evaluations/mmmu-pro) Visual reasoning [MLCR-AA ](https://artificialanalysis.ai/evaluations/mlcr-aa) New 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](https://artificialanalysis.ai/methodology/intelligence-benchmarking) for further details, including a breakdown of each evaluation and how we run them. ### AA-Briefcase v1.1 Updated AA-Briefcase Elo AA-Briefcase Rubric Score (%) Analytical Quality & Presentation Elo ### 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 28 of 192 models Add model from specific provider ### 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 Accuracy AA-Omniscience Hallucination Rate ### 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. 28 of 549 models Add model from specific provider ### 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 Task Intelligence Index vs. Time per Task Intelligence Index vs. Output Speed Intelligence Index vs. End-to-End Response Time ### Intelligence Index vs. Cost per Intelligence Index Task Artificial Analysis Intelligence Index · Weighted average cost (USD) per Artificial Analysis Intelligence Index task 28 of 674 models Most attractive quadrant Pareto line Inception Xiaomi OpenAI Anthropic SpaceXAI Google Meta Z AI DeepSeek Alibaba MiniMax Multiverse Computing Tencent ### 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](https://artificialanalysis.ai/methodology/intelligence-benchmarking) for further details, including a breakdown of each evaluation and how we run them. ## Token Use Output Tokens per Task Intelligence Index vs. Output Tokens per Task Intelligence Index Token Use Intelligence Index vs. 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 28 of 674 models Answer Reasoning ### 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 Task Intelligence Index vs. Cost per Task Evaluation Breakdown ### Cost per Intelligence Index Task Weighted average cost (USD) per Artificial Analysis Intelligence Index task, segmented by token type. Lower is better 28 of 674 models Answer Reasoning Cache Write Cache Hit Input ### 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. Intelligence Index Total Cost Intelligence Index vs. Total Cost ### Cost to Run Artificial Analysis Intelligence Index Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index 28 of 674 models Add model from specific provider Output Reasoning Cache Write Cache Read Non-Cache Input ### 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). Cache Hit, Input, and Output Pricing Blended Price Blended Price (Stacked) Cache Discount Intelligence Index vs. Price Intelligence Index vs. Price (Log, Inverted) Image Input Pricing ### Pricing: Cache Hit, Input, and Output Price (USD per M Tokens) 28 of 674 models Add model from specific provider Cache Hit Input 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. 4 more notes ## Context Window Context Window Intelligence Index vs. Context Window ### Context Window Context window: tokens limit · Higher is better 28 of 674 models Add model from specific provider ### 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 by Prompt Type Output Speed Variance Output Speed Over Time Output Speed vs. Price Latency vs. Output Speed ### Output Speed Output tokens per second · Higher is better 28 of 674 models Add model from specific provider ### 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 Task Intelligence Index vs. Time per Task Cost vs. Time per Task ### Time per Intelligence Index Task Weighted average decode time (minutes) per task; excludes TTFT and overhead time · Lower is better 28 of 674 models ### 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 Time To First Answer Token Time To First Token Latency by Prompt Type Latency Variance Latency Over Time ### Latency: Time To First Answer Token Seconds to first answer token received · Accounts for reasoning model 'thinking' time 28 of 674 models Add model from specific provider Thinking (reasoning models, when applicable) Input processing ### 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 by Prompt Type End-to-End Response Time Over Time ### End-to-End Response Time Seconds to output 500 tokens, including reasoning model 'thinking' time · Lower is better 28 of 674 models Add model from specific provider Outputting time 'Thinking' time (reasoning models) Input processing 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](https://artificialanalysis.ai/methodology/performance-benchmarking)). - 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 2.95s (based on Inception's API), which is somewhat higher than average compared to other reasoning models in a similar price tier (median: 2.23s). ### 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](https://artificialanalysis.ai/models/mercury-2-5/providers) ### 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](https://artificialanalysis.ai/models/mercury-2-5/providers) ### Where can I use Mercury 2.5? Mercury 2.5 is available through 1 API provider. 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