GLM-5.2 (max) is currently the third best model available, across both open and proprietary.

# GLM-5.2 (max) - Intelligence, Performance & Price Analysis Source: [https://artificialanalysis.ai/models/glm-5-2](https://artificialanalysis.ai/models/glm-5-2) ## IntelligenceUpdated ### Artificial Analysis Intelligence Index Artificial 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\-LCR Reasoning models are indicated by a lightbulb icon Artificial Analysis Intelligence Index v4\.1includes: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](https://artificialanalysis.ai/methodology/intelligence-benchmarking)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\.1 incorporates 9 evaluations: GDPval\-AA v2, 𝜏³\-Banking, Terminal\-Bench v2\.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA\-Omniscience, AA\-LCR Reasoning models are indicated by a lightbulb icon Artificial Analysis Intelligence Index v4\.1includes: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](https://artificialanalysis.ai/methodology/intelligence-benchmarking)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 Intelligence evaluations measured independently by Artificial Analysis · Higher is better Agentic real\-world work tasks, \(Elo\-500\)/2000 Agentic coding & terminal use Reasoning 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\.1includes: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](https://artificialanalysis.ai/methodology/intelligence-benchmarking)for further details, including a breakdown of each evaluation and how we run them\. ## Openness ### Artificial Analysis Openness Index: Score Openness Index assesses model openness on a 0 to 100 normalized scale \(higher is more open\) Reasoning models are indicated by a lightbulb icon ## Intelligence Index Comparisons ### Intelligence vs\. Cost per Intelligence Index Task Artificial Analysis Intelligence Index · Weighted average cost \(USD\) per Artificial Analysis Intelligence Index task Reasoning 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\.1includes: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](https://artificialanalysis.ai/methodology/intelligence-benchmarking)for further details, including a breakdown of each evaluation and how we run them\. ## Token UseUpdated ### Output Tokens per Intelligence Index Task Weighted average number of output tokens used to run one task in the Artificial Analysis Intelligence Index Reasoning models are indicated by a lightbulb icon 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 CostUpdated ### Cost per Intelligence Index Task Weighted average cost \(USD\) per Artificial Analysis Intelligence Index task, segmented by token type\. Lower is better Reasoning 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\. ### Cost to Run Artificial Analysis Intelligence Index Cost \(USD\) to run all evaluations in the Artificial Analysis Intelligence Index Reasoning models are indicated by a lightbulb icon 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 Price \(USD per M Tokens\) Reasoning models are indicated by a lightbulb icon 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\. 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](https://artificialanalysis.ai/models/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 Window ### Context Window Context window: tokens limit · Higher is better Reasoning models are indicated by a lightbulb icon 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 \(varied by model\)\. ## SpeedUpdated Measured by Output Speed \(tokens per second\) ### Output Speed Output tokens per second · Higher is better Reasoning models are indicated by a lightbulb icon 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 \(e\.g\. Meta's Llama models\)\. ### Time per Intelligence Index Task Weighted average wall clock time \(minutes\) per task; excludes TTFT and execution time · Lower is better Reasoning models are indicated by a lightbulb icon 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 Seconds to first answer token received · Accounts for reasoning model 'thinking' time Reasoning models are indicated by a lightbulb icon 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 output 500 tokens, including reasoning model 'thinking' time · Lower is better Reasoning models are indicated by a lightbulb icon 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 \([methodology details](https://artificialanalysis.ai/methodology/performance-benchmarking)\)\. - 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 Comparison between total model parameters and parameters active during inference Reasoning models are indicated by a lightbulb icon 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\.