待翻譯：Grok Build 0.1: Intelligence, Performance and Price Analysis

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Artificial Analysis xAI • Proprietary model Grok Build 0.1 0616 Intelligence, Performance & Price Analysis API Provider Benchmarks Model summary IntelligenceUpdated 40 Artificial Analysis Intelligence Index 4 out of 4 units for Intelligence. Speed 93.3 Output tokens per second 3 out of 4 units for Speed. Price Input $1.00 per 1M tokens Output $2.00 per 1M tokens 1 out of 4 units for Price. Cache Hit Price $0.20 USD per 1M tokens 2 out of 4 units for Cache Hit Price. Verbosity 130M Output tokens from Intelligence Index 4 out of 4 units for Verbosity. Grok Build 0.1 0616 is amongst the leading models in intelligence and well priced when comparing to other models of similar price. It's also faster than average, however very verbose. The model supports text and image input, outputs text, and has a 256k tokens context window. Grok Build 0.1 0616 scores 40 on the Artificial Analysis Intelligence Index, placing it well above average among comparable models (averaging 29). When evaluating the Intelligence Index, it generated 130M tokens, which is very verbose in comparison to the average of 93M. Pricing for Grok Build 0.1 0616 is $1.00 per 1M input tokens (competitively priced, average: $1.50) and $2.00 per 1M output tokens (competitively priced, average: $8.00). In total, it cost $375.01 to evaluate Grok Build 0.1 0616 on the Intelligence Index. At 93 tokens per second, Grok Build 0.1 0616 is faster than average (86). ReasoningYes This page shows the reasoning version of this model. A non-reasoning variant may also exist. Input modality Supports: text, image Output modality Supports: text Context window256k ~384 A4 pages of size 12 Arial font Metrics are compared against models of the same class: Non-reasoning models → compared only with other non-reasoning models Reasoning models → compared across both reasoning and non-reasoning Open weights models → compared only with other open weights models of the same size class: Tiny: ≤4B parameters Small: 4B–40B parameters Medium: 40B–150B parameters Large: >150B parameters Proprietary models → compared across proprietary and open weights models of the same price range, using a blended 3:1 input/output price ratio: $1 per 1M tokens Highlights Updated Intelligence Artificial Analysis Intelligence Index · Higher is better Not currently available Speed Output tokens per second · Higher is better New Cost per Task Weighted average cost (USD) per Intelligence Index task · Lower is better Not currently available 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 Not currently available Reasoning models are indicated by a lightbulb icon 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. 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 Not currently available Reasoning models are indicated by a lightbulb icon 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. 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 GDPval-AA v2Updated Agentic real-world work tasks, (Elo-500)/2000 𝜏³-BankingNew Agentic tool use Terminal-Bench v2.1New Agentic coding & terminal use SciCode Coding Humanity's Last Exam Reasoning & knowledge GPQA Diamond Scientific reasoning CritPt Physics reasoning AA-Omniscience Accuracy Knowledge AA-Omniscience Non-Hallucination Rate 1 - hallucination rate AA-LCR Long context reasoning AA-BriefcaseNew Agentic knowledge work, (Elo-500)/2000 IFBench Instruction following APEX-Agents-AA Long-horizon agentic tasks ITBench-AA Kubernetes incident root-cause analysis MMMU-Pro Visual reasoning 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 v4.1 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. AA-BriefcaseNew AA-Briefcase Elo AA-Briefcase is an agentic knowledge work benchmark developed by Artificial Analysis. AA-Briefcase Elo is a combined metric that aggregates rubric pass rate, analytical quality Elo and presentation Elo · Higher is better Not currently available Reasoning models are indicated by a lightbulb icon 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. 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 Most attractive quadrant 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 v4.1 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 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 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 outpu [truncated for AI cost control]