Comparison of Models: Intelligence, Performance & Price Analysis
Comparison and analysis of AI models across key performance metrics including quality, price, output speed, latency, context window & others. Click on any model to see detailed metrics. For more details including relating to our methodology, see our FAQs.
Model Comparison Summary
Gemini 3.1 Pro Preview and 
GPT-5.4 (xhigh) are the highest intelligence models, followed by GPT-5.3 Codex (xhigh) & 
Claude Opus 4.6 (max).Output Speed (tokens/s): gpt-oss-20B (low) (861 t/s) and gpt-oss-20B (high) (847 t/s) are the fastest models, followed by 
Mercury 2 & 
Llama 3.1 8B.Latency (seconds): Gemini 2.5 Flash (1.75s) and 
Jamba 1.6 Mini (2.10s) are the lowest latency models, followed by Claude 4.5 Haiku & 
NVIDIA Nemotron 3 Nano.Price ($ per M tokens):
Nova Micro ($0.06) and NVIDIA Nemotron Nano 9B V2 ($0.07) are the cheapest models, followed by 
Devstral Small (May) & Llama 3.2 3B.Context Window: Llama 4 Scout (10m) and 
Grok 4.1 Fast (2m) are the largest context window models, followed by Grok 4.1 Fast & Gemini 2.0 Pro Experimental.Intelligence
Artificial Analysis Intelligence Index
Artificial Analysis Intelligence Index v4.0 includes: GDPval-AA, 𝜏²-Bench Telecom, Terminal-Bench Hard, SciCode, AA-LCR, AA-Omniscience, IFBench, Humanity's Last Exam, GPQA Diamond, CritPt. 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 v4.0 includes: GDPval-AA, 𝜏²-Bench Telecom, Terminal-Bench Hard, SciCode, AA-LCR, AA-Omniscience, IFBench, Humanity's Last Exam, GPQA Diamond, CritPt. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.
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 Evaluations
While model intelligence generally translates across use cases, specific evaluations may be more relevant for certain use cases.
Artificial Analysis Intelligence Index v4.0 includes: GDPval-AA, 𝜏²-Bench Telecom, Terminal-Bench Hard, SciCode, AA-LCR, AA-Omniscience, IFBench, Humanity's Last Exam, GPQA Diamond, CritPt. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.
Artificial Analysis 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.
Openness
Artificial Analysis Openness Index: Results
Intelligence Index Comparisons
Intelligence vs. Price
While higher intelligence models are typically more expensive, they do not all follow the same price-quality curve.
Artificial Analysis Intelligence Index v4.0 includes: GDPval-AA, 𝜏²-Bench Telecom, Terminal-Bench Hard, SciCode, AA-LCR, AA-Omniscience, IFBench, Humanity's Last Exam, GPQA Diamond, CritPt. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.
Price per token, represented as USD per million Tokens. Price is a blend of Input & Output token prices (3:1 ratio).
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).
Intelligence Index Token Use & Cost
Output Tokens Used to Run Artificial Analysis Intelligence Index
The number of tokens required to run all evaluations in the Artificial Analysis Intelligence Index (excluding repeats).
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 and output token pricing and the number of tokens used across evaluations (excluding repeats).
Context Window
Context Window
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).
Pricing
Pricing: Input and Output Prices
Price per token included in the request/message sent to the API, represented as USD per million Tokens.
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).
Intelligence vs. Price (Log Scale)
While higher intelligence models are typically more expensive, they do not all follow the same price-quality curve.
Artificial Analysis Intelligence Index v4.0 includes: GDPval-AA, 𝜏²-Bench Telecom, Terminal-Bench Hard, SciCode, AA-LCR, AA-Omniscience, IFBench, Humanity's Last Exam, GPQA Diamond, CritPt. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.
Price per token, represented as USD per million Tokens. Price is a blend of Input & Output token prices (3:1 ratio).
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).
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 (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).
Output Speed vs. Price
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).
Price per token, represented as USD per million Tokens. Price is a blend of Input & Output token prices (3:1 ratio).
Latency
Measured by Time (seconds) to First Token
Latency: 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
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 (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
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.
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
Gemini 3.1 Pro Preview currently leads the Artificial Analysis Intelligence Index with a score of 57, out of 242 models evaluated.
The top AI models by Intelligence Index are: 1. Gemini 3.1 Pro Preview (57), 2. GPT-5.4 (xhigh) (57), 3. GPT-5.3 Codex (xhigh) (54), 4. Claude Opus 4.6 (Adaptive Reasoning, Max Effort) (53), 5. Claude Sonnet 4.6 (Adaptive Reasoning, Max Effort) (52).
gpt-oss-20B (low) is the fastest at 860.6 tokens per second, followed by gpt-oss-20B (high) (846.8 t/s) and Mercury 2 (797.9 t/s).
Nova Micro is the most affordable at $0.06 per 1M tokens (blended), followed by NVIDIA Nemotron Nano 9B V2 (Reasoning) ($0.07) and Devstral Small (May '25) ($0.07).
Gemini 2.5 Flash (Non-reasoning) has the lowest time to first token at 1.75s, followed by Jamba 1.6 Mini (2.10s) and Claude 4.5 Haiku (Non-reasoning) (2.20s).
GLM-5 (Reasoning) is the highest-ranked open weights model with an Intelligence Index score of 50. There are 145 open weights models out of 242 total evaluated.
The top open weights AI models by Intelligence Index are: 1. GLM-5 (Reasoning) (50), 2. Kimi K2.5 (Reasoning) (47), 3. Qwen3.5 397B A17B (Reasoning) (45).
Gemini 3.1 Pro Preview leads among 124 reasoning models with an Intelligence Index score of 57. Reasoning models use extended thinking to work through complex problems before providing answers.
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 334 models.
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
Magistral Small 1.2
DeepSeek
Perplexity
R1 1776
TII UAE
Microsoft Azure
Upstage
MiniMax
Kimi
Kimi K2.5 (Non-reasoning)
StepFun
IBM
Mercury 2
Reka AI
Nous Research
Hermes 4 - Llama-3.1 405B (Reasoning)
LG AI Research
K-EXAONE (Non-reasoning)
Xiaomi
Baidu
Deep Cogito
Cogito v2.1 (Reasoning)
KwaiKAT
Prime Intellect
MBZUAI Institute of Foundation Models
Motif Technologies
Korea Telecom
Mi:dm K 2.5 Pro Preview
Naver
HyperCLOVA X SEED Think (32B)
Z AI
Cohere
ServiceNow
Alibaba
InclusionAI
Ling-flash-2.0
ByteDance Seed
Allen Institute for AI