Mercury 2.5 Intelligence, Performance & Price Analysis
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Model summary
Intelligence Updated
#89 / 173
12
Artificial Analysis Intelligence Index
2 out of 4 units for Intelligence.
Speed
#2 / 173
780.8
Output tokens per second
4 out of 4 units for Speed.
Cost
#21 / 173
In $0.25Out $0.75Cache Discount 90%
$0.06
Cost per Intelligence Index task
2 out of 4 units for Cost.
Verbosity
#14 / 173
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: 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).
Technical specifications
| Reasoning | YesThis 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 |
173 models in this class
Metrics are compared against models of the same class:
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Non-reasoning models → compared only with other non-reasoning models
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Reasoning models → compared across both reasoning and non-reasoning
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Open weights models → compared only with other open weights models of the same size class:
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Tiny: ≤4B parameters
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Small: 4B–40B parameters
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Medium: 40B–150B parameters
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Large: >150B parameters
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Proprietary models → compared across proprietary and open weights models of the same price range, using a blended 3:1 input/output price ratio:
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$1 per 1M tokens
Highlights
Updated
Intelligence
Artificial Analysis Intelligence Index · Higher is better
Claude Opus 5.5 (max with fallback)
Claude Fable 5.1 (max with fallback)
5853534846464541393712
Speed
Output tokens per second · Higher is better
Claude Fable 5.1 (max with fallback)
7812912322191326657524940
Cost per Task
Weighted average cost (USD) per Intelligence Index task · Lower is better
Claude Opus 5.5 (max with fallback)
Claude Fable 5.1 (max with fallback)
$0.06$0.07$0.13$0.27$1.24$1.60$2.01$3.26$3.74$5.98$7.63
Prompt Options
IntelligenceUpdated
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
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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 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
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ProprietaryOpen WeightsOpen 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 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
Finance & AccountingStrategy & OpsLegalEngineeringEconomics
Artificial Analysis Finance & Accounting Index
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
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Benchmarks
Intelligence Evaluations
Intelligence evaluations measured independently by Artificial Analysis · Higher is better
CodingAgenticTool UsePrivate DatasetUser InteractionFinanceMedicalLegalIntelligence IndexLong ContextMultimodalInstruction FollowingFaithfulnessWritingBusiness See more
18 of 26 evaluations
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AA-Briefcase v1.1 Updated
Agentic knowledge work, (Elo-500)/2000
GDPval-AA v2.1 Updated
Agentic real-world work tasks, (Elo-500)/2000
AutomationBench-AA Updated
Agentic SaaS workflows
Agentic coding & terminal use
Coding
Reasoning & knowledge
GDP.pdf New
Professional document reasoning, All-pass
CritPt Under review
Physics reasoning
Knowledge
AA-Omniscience Non-Hallucination Rate
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
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 for further details, including a breakdown of each evaluation and how we run them.
AA-Briefcase v1.1Updated
AA-Briefcase EloAA-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
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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 IndexAA-Omniscience AccuracyAA-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.
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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 TaskIntelligence Index vs. Time per TaskIntelligence Index vs. Output SpeedIntelligence 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
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Most attractive quadrant
Pareto line
InceptionXiaomiOpenAIAnthropicSpaceXAIGoogleMetaZ AIDeepSeekAlibabaMiniMaxMultiverse ComputingTencent
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 TaskIntelligence Index vs. Output Tokens per TaskIntelligence Index Token UseIntelligence 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
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AnswerReasoning
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 TaskIntelligence Index vs. Cost per TaskEvaluation Breakdown
Cost per Intelligence Index Task
Weighted average cost (USD) per Artificial Analysis Intelligence Index task, segmented by token type. Lower is better
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AnswerReasoningCache WriteCache HitInput
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 CostIntelligence Index vs. Total Cost
Cost to Run Artificial Analysis Intelligence Index
Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index
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OutputReasoningCache WriteCache ReadNon-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 PricingBlended PriceBlended Price (Stacked)Cache DiscountIntelligence Index vs. PriceIntelligence Index vs. Price (Log, Inverted)Image Input Pricing
Pricing: Cache Hit, Input, and Output
Price (USD per M Tokens)
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Cache HitInputOutput
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 WindowIntelligence Index vs. Context Window
Context Window
Context window: tokens limit · Higher is better
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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 SpeedOutput Speed by Prompt TypeOutput Speed VarianceOutput Speed Over TimeOutput Speed vs. PriceLatency vs. Output Speed
Output Speed
Output tokens per second · Higher is better
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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 TaskIntelligence Index vs. Time per TaskCost vs. Time per Task
Time per Intelligence Index Task
Weighted average decode time (minutes) per task; excludes TTFT and overhead time · Lower is better
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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 TokenTime To First TokenLatency by Prompt TypeLatency VarianceLatency Over Time
Latency: Time To First Answer Token
Seconds to first answer token received · Accounts for reasoning model 'thinking' time
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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 TimeEnd-to-End Response Time by Prompt TypeEnd-to-End Response Time Over Time
End-to-End Response Time
Seconds to output 500 tokens, including reasoning model 'thinking' time · Lower is better
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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).
- 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: 12).
How fast is Mercury 2.5?
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: 110.2 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: $0.91), 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
