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GeezerVison is showing artificialanalysis.ai — Comparison of Open Source AI Models across Intelligence, Performance, Price, Context Window, and more | Artificial Analysis Comparison of Open Source ModelsComparison and analysis of open source AI models across key performance metrics including quality, performance, inference speed, context window, parameter count & licensing details. Models are considered open source (also commonly referred to as open weights) where their weights are accessible to download. This allows self-hosting on your own infrastructure and enables customizing the model such as through fine-tuning. For more details relating to our methodology, see our FAQs.  Kimi K3.1 Pro and  DeepSeek V4.1 Flash 1017 (max) are the highest intelligence open source models, followed by  GLM-5.3 (max) &  MiMo-V2.6-Pro.OpennessArtificial Analysis Openness Index: ScoreOpenness Index assesses model openness on a 0 to 100 normalized scale (higher is more open) Open Source ProgressOpen Weights vs. Proprietary: Artificial Analysis Progress Index, Over TimeArtificial Analysis Progress Index v1 is an item response theory estimate of general capability across Artificial Analysis evaluations · Highest-scoring effort variant shown Nov 2022Nov 2026 We calculate the Artificial Analysis Progress Index v1 using item response theory, which estimates model capability and task difficulty together from our benchmark results. This lets us compare models tested on different benchmarks over time. We estimate task difficulty from which models succeed or fail, while estimating each model's capability from its results across tasks. For example, an older model might have been tested on easier benchmarks and a newer model on harder ones. Models tested on both sets help us estimate the difference in difficulty and compare the older and newer models on the same scale. Each line shows the highest-scoring model available at that point in time within its license category, using each model’s highest-scoring effort variant. Read the Progress Index v1 methodology. 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. Open Source: Artificial Analysis Progress Index by Lab, Over TimeArtificial Analysis Progress Index v1 is an item response theory estimate of general capability across Artificial Analysis evaluations · Highest-scoring effort variant shown We calculate the Artificial Analysis Progress Index v1 using item response theory, which estimates model capability and task difficulty together from our benchmark results. This lets us compare models tested on different benchmarks over time. We estimate task difficulty from which models succeed or fail, while estimating each model's capability from its results across tasks. For example, an older model might have been tested on easier benchmarks and a newer model on harder ones. Models tested on both sets help us estimate the difference in difficulty and compare the older and newer models on the same scale. Each lab’s line shows its highest-scoring open weights model available at that point in time, using each model’s highest-scoring effort variant. Read the Progress Index v1 methodology. Open Source: Artificial Analysis Progress Index by Size, Over TimeArtificial Analysis Progress Index v1 is an item response theory estimate of general capability across Artificial Analysis evaluations · Highest-scoring effort variant shown We calculate the Artificial Analysis Progress Index v1 using item response theory, which estimates model capability and task difficulty together from our benchmark results. This lets us compare models tested on different benchmarks over time. We estimate task difficulty from which models succeed or fail, while estimating each model's capability from its results across tasks. For example, an older model might have been tested on easier benchmarks and a newer model on harder ones. Models tested on both sets help us estimate the difference in difficulty and compare the older and newer models on the same scale. Each line shows the highest-scoring open weights model available at that point in time within its size category, using each model’s highest-scoring effort variant. Read the Progress Index v1 methodology.
IntelligenceUpdatedArtificial Analysis Intelligence IndexArtificial Analysis Intelligence Index v5.0.1 incorporates 9 evaluations: AA-Briefcase v1.1, AutomationBench-AA, Terminal-Bench 4.0, FrontierCode-AA 2.0, AA-Omniscience v1.1, GDP.pdf, HLE-Diamond, CritPt v2 (Hard Subset), and Terminal-Bench-Science 0.1 Artificial Analysis Intelligence Index v5.0.1 includes: AA-Briefcase v1.1, AutomationBench-AA, Terminal-Bench 4.0, FrontierCode-AA 2.0, AA-Omniscience v1.1, GDP.pdf, HLE-Diamond, CritPt v2 (Hard Subset), Terminal-Bench-Science 0.1. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them. EvaluationsEvaluations measured independently by Artificial Analysis · AA-PersonalityBench scores are percentile ranks from 1 to 100 Agentic knowledge work, Elo AA-Omniscience v1.1 AccuracyUpdated Knowledge 1 - hallucination rate Agentic SaaS workflows Agentic scientific research workflows in a terminal Agentic software engineering in open-source repositories HLE-DiamondNew Reasoning & knowledge Professional document reasoning, All-pass Agentic coding & terminal use Physics reasoning Quantitative analysis on spreadsheets & documents Harvey LAB-AA v1.1Updated Legal agentic work, Hallucination-Gated All-Pass Rate Agentic business operations Agentic real-world work tasks, Elo AA-3DNew Agentic interactive 3D experiences, Elo Product and brand videos designed in code, Elo Kubernetes incident root-cause analysis MLCR-AAUpdated Medical long context reasoning Visual reasoning Rich, varied, well-crafted language Well-structured, scannable, answer-first Truthful, acknowledges uncertainty, non-sycophantic Use of filler and generic language While model intelligence generally translates across use cases, specific evaluations may be more relevant for certain use cases. Artificial Analysis Intelligence Index v5.0.1 includes: AA-Briefcase v1.1, AutomationBench-AA, Terminal-Bench 4.0, FrontierCode-AA 2.0, AA-Omniscience v1.1, GDP.pdf, HLE-Diamond, CritPt v2 (Hard Subset), Terminal-Bench-Science 0.1. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them. SizeIntelligence Index By Model SizeArtificial Analysis Intelligence Index v5.0.1 incorporates 9 evaluations: AA-Briefcase v1.1, AutomationBench-AA, Terminal-Bench 4.0, FrontierCode-AA 2.0, AA-Omniscience v1.1, GDP.pdf, HLE-Diamond, CritPt v2 (Hard Subset), and Terminal-Bench-Science 0.1 Artificial Analysis Intelligence Index v5.0.1 includes: AA-Briefcase v1.1, AutomationBench-AA, Terminal-Bench 4.0, FrontierCode-AA 2.0, AA-Omniscience v1.1, GDP.pdf, HLE-Diamond, CritPt v2 (Hard Subset), Terminal-Bench-Science 0.1. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them. Model Size: Total and Active ParametersComparison between total model parameters and parameters active during inference (billions) 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. Intelligence Index vs. Active ParametersArtificial Analysis Intelligence Index · Active parameters at inference time (billions) Most attractive quadrant Pareto line Artificial Analysis Intelligence Index v5.0.1 includes: AA-Briefcase v1.1, AutomationBench-AA, Terminal-Bench 4.0, FrontierCode-AA 2.0, AA-Omniscience v1.1, GDP.pdf, HLE-Diamond, CritPt v2 (Hard Subset), Terminal-Bench-Science 0.1. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them. 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. Intelligence Index vs. Total ParametersArtificial Analysis Intelligence Index · Size in parameters (billions) Most attractive quadrant Pareto line Artificial Analysis Intelligence Index v5.0.1 includes: AA-Briefcase v1.1, AutomationBench-AA, Terminal-Bench 4.0, FrontierCode-AA 2.0, AA-Omniscience v1.1, GDP.pdf, HLE-Diamond, CritPt v2 (Hard Subset), Terminal-Bench-Science 0.1. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them. 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. Context WindowContext WindowContext window: tokens limit · Higher is better 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). Further details
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