Comparison of Models: Intelligence, Performance & Price Analysis
Intelligence
Output Speed (tokens/s)
Latency (seconds)
Price ($ per M tokens)
Context Window
Highlights
Intelligence
Speed
Cost per Task
Intelligence
Artificial Analysis Intelligence Index
Artificial Analysis Intelligence Index
Artificial Analysis Intelligence Index v4.1 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.
Intelligence Index methodology
Artificial Analysis Intelligence Index by Open Weights / Proprietary
Artificial Analysis Intelligence Index
Artificial Analysis Intelligence Index v4.1 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.
Intelligence Index methodology
Open Weights
Indicates whether the model weights are available. Models are labelled as 'Commercial Use Restricted' if the weights are available but commercial use is limited (typically requires obtaining a paid license).
Intelligence Breakdown
Intelligence Evaluations
Agentic real-world work tasks, (Elo-500)/2000
Agentic tool use
Agentic coding & terminal use
Coding
Reasoning & knowledge
Scientific reasoning
Physics reasoning
Knowledge
AA-Omniscience Non-Hallucination Rate
1 - hallucination rate
Long context reasoning
Agentic knowledge work, Elo
Agentic SaaS workflows
Legal agentic work, criterion pass rate
Agentic business operations
Instruction following
Long-horizon agentic tasks
Kubernetes incident root-cause analysis
Visual 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 v4.1 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.
Intelligence Index methodology
AA-Briefcase
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.
Openness
Artificial Analysis Openness Index: Score
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 v4.1 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.
Intelligence Index methodology
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).
Price and 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.
Input Price
Price per token included in the request/message sent to the API, represented as USD per million Tokens.
Cache Pricing by Provider
The blended cache price shown here uses cache hit price only. Other caching costs differ by provider:
Anthropic: charges a separate cache write fee, with different rates for 5-minute and 1-hour TTLs (1-hour TTL is more expensive).
Google (Vertex/Gemini): charges a per-hour cache storage fee in addition to cache hit pricing. Some providers also use tiered pricing for prompts above 200K tokens.
OpenAI, DeepSeek, others: typically charge only cache hit pricing with no write or storage fee.
See Prompt Caching for the full breakdown.
Output Price
Price per token generated by the model (received from the API), represented as USD per million Tokens.
Model Performance Representation
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).
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 (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
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 (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).
Model Size (Open Weights Models Only)
Model Size: Total and Active Parameters
Total Parameters
The total number of trainable weights and biases in the model, expressed in billions. These parameters are learned during training and determine the model's ability to process and generate responses.
Active Parameters at Inference Time
The number of parameters actually executed during each inference forward pass, expressed in billions. For Mixture of Experts (MoE) models, a routing mechanism selects a subset of experts per token, resulting in fewer active than total parameters. Dense models use all parameters, so active equals total.
Frequently Asked Questions
Which is the most intelligent AI model?
Claude Opus 5 (Adaptive Reasoning, Max Effort) currently leads the Artificial Analysis Intelligence Index with a score of 61, out of 170 models evaluated.
What are the top AI models?
The top AI models by Intelligence Index are: 1. Claude Opus 5 (Adaptive Reasoning, Max Effort) (61), 2. Claude Opus 5 (Adaptive Reasoning, Xhigh Effort) (60), 3. Claude Fable 5 (Adaptive Reasoning, Max Effort, Opus 4.8 Fallback) (60), 4. GPT-5.6 Sol (max) (59), and 5. Claude Opus 5 (Adaptive Reasoning, High Effort) (59).
Which is the fastest AI model?
Mercury 2 is the fastest at 901.6 tokens per second, followed by HyperNova 60B 2605 (439.5 t/s) and Gemini 3.5 Flash-Lite (436.5 t/s).
Which is the cheapest AI model?
Nova Micro is the most affordable at $0.03 per 1M tokens (blended), followed by Sarvam 30B (high) ($0.03) and Gemma 4 E4B (Non-reasoning) ($0.03).
Which AI model has the lowest latency?
Gemini 2.5 Flash-Lite (Non-reasoning) has the lowest time to first token at 0.33s, followed by Command A+ (0.42s) and North Mini Code (0.49s).
Which is the best open weights AI model?
GLM-5.2 (max) is the highest-ranked open weights model with an Intelligence Index score of 51. There are 94 open weights models out of 170 total evaluated.
What are the top open weights AI models?
The top open weights AI models by Intelligence Index are: 1. GLM-5.2 (max) (51), 2. MiniMax-M3 (44), and 3. DeepSeek V4 Pro (Reasoning, Max Effort) (44).
Which is the best reasoning model?
Claude Opus 5 (Adaptive Reasoning, Max Effort) leads among 126 reasoning models with an Intelligence Index score of 61. Reasoning models use extended thinking to work through complex problems before providing answers.
How are AI models compared on Artificial Analysis?
Models are compared across multiple dimensions including intelligence (quality), pricing, output speed (tokens per second), latency (time to first token), end-to-end response time, and context window size. Performance metrics are measured directly using standardized prompts across 586 models.
How do I compare a specific model against others?
Click on any model name or row in the charts to view its dedicated page with detailed metrics and direct comparisons against similar models. You can also use the model selector to customize which models appear in each chart. View the leaderboard