GLM-5.2 (max) - Intelligence, Performance & Price Analysis

IntelligenceUpdatedArtificial Analysis Intelligence IndexArtificial Analysis Intelligence Index v4.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCRNot currently availableReasoning models are indicated by a lightbulb iconArtificial 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.Artificial Analysis Intelligence Index by Open Weights / ProprietaryArtificial Analysis Intelligence Index v4.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCRNot currently availableReasoning models are indicated by a lightbulb iconArtificial 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.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 EvaluationsIntelligence evaluations measured independently by Artificial Analysis · Higher is betterAgentic real-world work tasks, (Elo-500)/2000Agentic coding & terminal useAgentic tool useReasoning models are indicated by a lightbulb icon.While model intelligence generally translates across use cases, specific evaluations may be more relevant for certain use cases.

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.OpennessArtificial Analysis Openness Index: ScoreOpenness Index assesses model openness on a 0 to 100 normalized scale (higher is more open)Reasoning models are indicated by a lightbulb iconIntelligence Index ComparisonsIntelligence vs. Cost per Intelligence Index TaskArtificial Analysis Intelligence Index · Weighted average cost (USD) per Artificial Analysis Intelligence Index taskMost attractive quadrantReasoning models are indicated by a lightbulb icon.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 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.Token UseUpdatedOutput Tokens per Intelligence Index TaskWeighted average number of output tokens used to run one task in the Artificial Analysis Intelligence IndexReasoning models are indicated by a lightbulb iconThe 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 CostUpdatedCost per Intelligence Index TaskWeighted average cost (USD) per Artificial Analysis Intelligence Index task, segmented by token type. Lower is betterReasoning models are indicated by a lightbulb iconWeighted 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 IndexCost (USD) to run all evaluations in the Artificial Analysis Intelligence IndexReasoning models are indicated by a lightbulb iconThe 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 OutputPrice (USD per M Tokens)Reasoning models are indicated by a lightbulb iconPrice 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.Price per token included in the request/message sent to the API, represented as USD per million Tokens.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.Price per token generated by the model (received from 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).Context WindowContext WindowContext window: tokens limit · Higher is betterReasoning models are indicated by a lightbulb iconLarger 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).SpeedUpdatedMeasured by Output Speed (tokens per second)Output SpeedOutput tokens per second · Higher is betterReasoning models are indicated by a lightbulb iconTokens 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).Time per Intelligence Index TaskWeighted average wall clock time (minutes) per task; excludes TTFT and execution time · Lower is betterReasoning models are indicated by a lightbulb iconThe 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.LatencyMeasured by Time (seconds) to First TokenLatency: Time To First Answer TokenSeconds to first answer token received · Accounts for reasoning model 'thinking' timeReasoning models are indicated by a lightbulb iconTime 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 TimeSeconds to output 500 tokens, calculated based on time to first token, 'thinking' time for reasoning models, and output speedEnd-to-End Response TimeSeconds to output 500 tokens, including reasoning model 'thinking' time · Lower is betterReasoning models are indicated by a lightbulb iconSeconds to receive a 500 token response. Key components:Input time: Time to receive the first response tokenThinking 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 speedFigures 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 ParametersComparison between total model parameters and parameters active during inferenceReasoning models are indicated by a lightbulb iconThe 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.