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Artificial AnalysisK

Artificial AnalysisModels

Coding Agents

Image, Speech, Video

Inference

Leaderboards

About

AI Trends

Google

•Proprietary model

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Model summary

IntelligenceUpdated

#8 / 223

Artificial Analysis Intelligence Index

4 out of 4 units for Intelligence.

Speed

N/A

Output tokens per second

Unknown out of 4 units for Speed.

Cost

#77 / 223

In $2.00Out $10.00Cache Discount 95%

$1.99

Cost per Intelligence Index task

3 out of 4 units for Cost.

Verbosity

#74 / 223

110M

Output tokens from Intelligence Index

3 out of 4 units for Verbosity.

Comparison Summary

Gemini 4 Argon (High) is amongst the leading models in intelligence and reasonably priced when comparing to other models of similar price. The model supports text and image input, outputs text, and has a 1M tokens context window.

Gemini 4 Argon (High) scores 53 on the Artificial Analysis Intelligence Index, placing it well above average among comparable models (median: 26). When evaluating the Intelligence Index, it generated 110M tokens, which is somewhat verbose in comparison to the median of 82M.

Pricing for Gemini 4 Argon (High) is $2.00 per 1M input tokens (moderately priced, median: $2.00) and $10.00 per 1M output tokens (moderately priced, median: $10.00). On average, it costs $1.99 per task to evaluate Gemini 4 Argon (High) on the Intelligence Index.

ReasoningYesThis page shows the reasoning version of this model.

Context window1M~1500 A4 pages of size 12 Arial font

223 models in this class

Metrics are compared against models of the same class:

IntelligenceUpdated

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v4.3.2 incorporates 10 evaluations: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1

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Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v4.3.2 includes: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

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ProprietaryOpen WeightsOpen Weights (Commercial Use Restricted)

Artificial Analysis Intelligence Index

Open Weights

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.

Measures the performance of models on specific capabilities and industries

Incorporates 7 evaluations: AA-Omniscience, GDPval-AA v2.1, AA-Briefcase v1.1, Humanity's Last Exam, AutomationBench-AA, AA-LCR v1.1, GDP.pdf · Higher is better

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Benchmarks

Intelligence Evaluations

Intelligence evaluations measured independently by Artificial Analysis · Higher is better

CodingAgenticTool UsePrivate DatasetUser InteractionFinanceMedicalLegalIntelligence IndexLong ContextMultimodalInstruction FollowingFaithfulnessWritingBusinessSee more

18 of 27 evaluations25 of 687 models

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AA-Briefcase v1.1Updated

Agentic knowledge work, (Elo-500)/2000

GDPval-AA v2.1Updated

Agentic real-world work tasks, (Elo-500)/2000

AutomationBench-AAUpdated

Agentic SaaS workflows

Terminal-Bench 4.0New

Agentic coding & terminal use

Humanity's Last Exam

Reasoning & knowledge

GDP.pdfNew

Professional document reasoning, All-pass

AA-Omniscience Non-Hallucination Rate

1 - hallucination rate

Harvey LAB-AA

Legal agentic work, criterion pass rate

EnterpriseOps-Gym-AA

Agentic business operations

Terminal-Bench-Science 0.1New

Agentic scientific research workflows in a terminal

AA-AnalystAgent

Quantitative analysis on spreadsheets & documents

ITBench-AA

Kubernetes incident root-cause analysis

Intelligence Evaluation Relevance

While model intelligence generally translates across use cases, specific evaluations may be more relevant for certain use cases.

Artificial Analysis Intelligence Index

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AA-Briefcase Elo

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.

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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.

25 of 687 models

Most attractive quadrant

Pareto line

GoogleXiaomiOpenAIAnthropicSpaceXAIMetaZ AIDeepSeek

Cost per Intelligence Index Task

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

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AnswerReasoning

Output Tokens per Intelligence Index Task

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).

25 of 687 models

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AnswerReasoningCache WriteCache HitInput

Cost per Intelligence Index Task

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OutputReasoningCache WriteCache ReadNon-Cache Input

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, cache hit, cache write, reasoning, and answer token prices and the number of tokens used across evaluations (excluding repeats).

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Cache HitInputOutput

Cache Hit

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.

4 more notes

Context Window

Context WindowIntelligence Index vs. Context Window

Context Window

Context window: tokens limit · Higher is better

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Context Window for RAG

Larger context windows are relevant to RAG (Retrieval Augmented Generation) LLM workflows which typically involve reasoning and information retrieval of large amounts of data.

Context Window

Maximum number of combined input & output tokens. Output tokens commonly have a significantly lower limit (varied by model).

Common questions about Gemini 4 Argon (High)

Gemini 4 Argon (High) was released on September 30, 2026.

Gemini 4 Argon (High) was created by Google.

Gemini 4 Argon (High) scores 53 on the Artificial Analysis Intelligence Index, placing it well above average among other reasoning models in a similar price tier (median: 26).

Gemini 4 Argon (High) costs $2.00 per 1M input tokens (better than average, median: $2.00) and $10.00 per 1M output tokens (better than average, median: $10.00), based on Google's API.

Gemini 4 Argon (High) costs $2.00 per 1M input tokens and $10.00 per 1M output tokens (based on Google's API). For a blended rate (7:2:1 cache hit/input/output ratio), this is $1.47 per 1M tokens. Pricing may vary by provider. Compare provider pricing

When evaluated on the Intelligence Index, Gemini 4 Argon (High) generated 110M output tokens, which is somewhat higher than average compared to other reasoning models in a similar price tier (median: 82M).

Yes, Gemini 4 Argon (High) is a reasoning model. It uses extended thinking or chain-of-thought reasoning to work through complex problems before providing an answer.

Gemini 4 Argon (High) supports text and image input.

Gemini 4 Argon (High) supports text output.

Yes, Gemini 4 Argon (High) supports image input and can analyze, describe, and answer questions about images.

Yes, Gemini 4 Argon (High) is multimodal. It can process text and image input and generate text output.

Gemini 4 Argon (High) has a context window of 1.0M tokens. This determines how much text and conversation history the model can process in a single request.

No, Gemini 4 Argon (High) is proprietary. The model weights are not publicly available.

Gemini 4 Argon (High) is a proprietary model and Google has not disclosed the model size or parameter count.

Gemini 4 Argon (High) achieves a score of 53 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across reasoning, knowledge, mathematics, and coding.

Yes, Gemini 4 Argon (High) is available via API through 1 provider. Compare API providers

Gemini 4 Argon (High) is available through 1 API provider. Compare providers

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Gemini 4 Argon (High): Intelligence, Performance and Price Analysis