Grok Build 0.1 vs Gemini 4 Argon
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Grok Build 0.1 by xAI against Gemini 4 Argon by Google across real-world coding, reasoning, token economics, and latency.
원하는 AI 모델 선택 및 실시간 비교
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Live comparison: no in-depth editorial review has been published for this pair yet.
Grok Build 0.1
심층 추론 모델xAI의 Grok Build 0.1 모델은 256K 토큰 컨텍스트 윈도우와 입력 $1.0/1M 토큰, 출력 $2.0/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$1.00 / 1M
$2.00 / 1M
256K tokens
69.8%
Gemini 4 Argon
심층 추론 모델Google의 Gemini 4 Argon 모델은 1M 토큰 컨텍스트 윈도우와 입력 $2/1M 토큰, 출력 $10/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$2.00 / 1M
$10.00 / 1M
1M tokens
81.9%
한눈에 보는 핵심 차이점
- Grok Build 0.1 is 3.3x cheaper for API usage (input + output tokens combined).
- Gemini 4 Argon has a larger context window (1M vs 256K tokens).
- Grok Build 0.1 is faster at 120 tok/s vs 115 tok/s.
- Gemini 4 Argon leads on SWE-bench coding (81.9% vs 69.8%).
| Feature | Grok Build 0.1 | Gemini 4 Argon |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 65 | 97 |
Provider | xAI | |
Input Price Cost per million input tokens | $1.00 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $2.00 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.57 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 256K tokens~384 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 66K | 1000K |
Throughput Speed Tokens generated per second | 120 tok/s | 115 tok/s |
SWE-bench Verified Real-world software engineering | 69.8% | 81.9% |
GPQA Diamond Graduate-level reasoning | 68.2% | 81.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.25 / 1M | $0.10 / 1M |
부문별 우위 모델
At-a-glance decision framework검증된 벤치마크 점수 비교
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
지연 시간(Latency) 및 응답 속도
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
실시간 API 토큰 비용 시뮬레이터
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Grok Build 0.1 · Strengths & Key Advantages
- xAI의 첨단 Code-Specialized Autoregressive Transformer 아키텍처와 최대 256K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $1.0/1M, 출력 $2.0/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
Gemini 4 Argon · Strengths & Key Advantages
- Google의 첨단 Pathways Multimodal Sparse Mixture-of-Experts 아키텍처와 최대 1M 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $2/1M, 출력 $10/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Grok Build 0.1 | Gemini 4 Argon |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.95 | $2.45 |
Output Tokens / Task Tokens generated per task | 42K | 75K |
Reasoning Tokens / Task Thinking tokens burned per task | 18K | 36K |
기술 사양 (Specifications)
Architecture, release timeline, capabilities, and modalities.
| Feature | Grok Build 0.1 | Gemini 4 Argon |
|---|---|---|
| Official API Model ID (SDK) | grok-build-0-1-latest | models/gemini-4-argon |
| API Protocol | xAI REST API (OpenAI-Compatible) | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2026-04 | 2026-09 |
| Knowledge Cutoff | January 2026 | June 2026 |
| Architecture | Code-Specialized Autoregressive Transformer | Pathways Multimodal Sparse Mixture-of-Experts |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($1/1M in) across full 256K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Cached input at $0.25/1M tokens | Implicit + Explicit Context Caching at $0.1/1M with configurable TTL |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| MCP & Parallel Tool Calling | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Real-time Web & X (Twitter) Search Grounding + Code Execution | Built-in Google Search Grounding + Managed Python Code Execution Sandbox |
| Official Fine-Tuning Support | API Prompt Engineering & RAG Distillation | Vertex AI Supervised Adapter Tuning & Context Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) |
| Quantization & Min GPU VRAM | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | xAI Production Safety & Alignment Verified | Google DeepMind Frontier Safety Framework Verified |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text, vision | text, vision, audio, video |
| Output Modalities | text | text |
Grok Build 0.1 vs Gemini 4 Argon: 핵심 요약 문답 (FAQ)
Which is more intelligent, Grok Build 0.1 or Gemini 4 Argon?
Gemini 4 Argon is more intelligent. It scores 97 compared with 65 on the composite Intelligence Index.
Which model is faster?
Grok Build 0.1 is faster, generating 120 tokens per second compared with 115 tokens per second.
Which model is cheaper to run?
Grok Build 0.1 is cheaper at $0.57 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Grok Build 0.1 has lower latency, with a time to first token of 0.55s compared with 0.85s.
Which model has a larger context window?
Gemini 4 Argon has a larger context window, supporting 1M tokens compared with 256K tokens.
종합 평가
Choose Grok Build 0.1 for 기업 업무 자동화, 소프트웨어 개발, 자율형 ai 에이전트 및 xai 기반 프로덕션 파이프라인, or choose Gemini 4 Argon for 기업 업무 자동화, 소프트웨어 개발, 자율형 ai 에이전트 및 google 기반 프로덕션 파이프라인.
자주 묻는 질문 (FAQ)
Which is cheaper: Grok Build 0.1 or Gemini 4 Argon?+
Grok Build 0.1 costs $1/1M input and $2/1M output tokens, while Gemini 4 Argon costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Grok Build 0.1 or Gemini 4 Argon?+
Grok Build 0.1 supports 256K tokens whereas Gemini 4 Argon supports 1M tokens.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
공식 출처: xAI & Google Official Documentation