GPT-6 Astra vs Grok-4
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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GPT-6 Astra by OpenAI and Grok-4 by xAI represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, Grok-4 holds a notable pricing advantage for token ingestion. On output generation, Grok-4 is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Grok-4 provides an expanded memory horizon with 1,000K tokens. In latency-sensitive production environments, Grok-4 leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
원하는 AI 모델 선택 및 실시간 비교
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
GPT-6 Astra
심층 추론 모델OpenAI의 GPT-6 Astra 모델은 1M 토큰 컨텍스트 윈도우와 입력 $10.0/1M 토큰, 출력 $50.0/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$10.00 / 1M
$50.00 / 1M
272K tokens
82.4%
Grok-4
심층 추론 모델xAI의 Grok-4 모델은 1M 토큰 컨텍스트 윈도우와 입력 $2.5/1M 토큰, 출력 $10/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$2.50 / 1M
$10.00 / 1M
1M tokens
76.5%
한눈에 보는 핵심 차이점
- Grok-4 is 4.7x cheaper for API usage (input + output tokens combined).
- Grok-4 has a larger context window (1M vs 272K tokens).
- Grok-4 is faster at 125 tok/s vs 100 tok/s.
- GPT-6 Astra leads on SWE-bench coding (82.4% vs 76.5%).
| Feature | GPT-6 Astra | Grok-4 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 96 | 92 |
Provider | OpenAI | xAI |
Input Price Cost per million input tokens | $10.00 / 1M | $2.50 / 1M |
Output Price Cost per million output tokens | $50.00 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $7.70 / 1M | $1.68 / 1M |
Context Window Maximum input + output tokens | 272K tokens~408 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 128K | 64K |
Throughput Speed Tokens generated per second | 100 tok/s | 125 tok/s |
SWE-bench Verified Real-world software engineering | 82.4% | 76.5% |
GPQA Diamond Graduate-level reasoning | 80.9% | 77.8% |
Cached Input Price Prompt caching discount per 1M tokens | $1.00 / 1M | $0.25 / 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.
GPT-6 Astra · Strengths & Key Advantages
- OpenAI의 첨단 Omni-Adaptive Autoregressive with Dynamic CoT 아키텍처와 최대 1M 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $10.0/1M, 출력 $50.0/1M)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
Grok-4 · Strengths & Key Advantages
- xAI의 첨단 Colossus-Scale Dense Mixture-of-Experts 아키텍처와 최대 1M 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $2.5/1M, 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 | GPT-6 Astra | Grok-4 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $2.15 | $1.65 |
Output Tokens / Task Tokens generated per task | 58K | 48K |
Reasoning Tokens / Task Thinking tokens burned per task | 32K | 24K |
기술 사양 (Specifications)
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-6 Astra | Grok-4 |
|---|---|---|
| Official API Model ID (SDK) | gpt-6-astra | grok-4-latest |
| API Protocol | OpenAI Responses API & Chat Completions v1 | xAI REST API (OpenAI-Compatible) |
| Release Date | 2026-09 | 2026-07 |
| Knowledge Cutoff | June 2026 | Real-time (Up to October 2026) |
| Architecture | Omni-Adaptive Autoregressive with Dynamic CoT | Colossus-Scale Dense Mixture-of-Experts |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM | 5,000 – 15,000 RPM (Production Tier) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($10/1M in) across full 272K window | Flat rate ($2.5/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Automatic prefix caching at $1/1M (no extra cache-write fee) | Cached input at $0.25/1M tokens |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | 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 | Native Web Search tool + Hosted Code Interpreter & File Search RAG | Real-time Web & X (Twitter) Search Grounding + Code Execution |
| Official Fine-Tuning Support | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | API Prompt Engineering & RAG Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| 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 | OpenAI Preparedness Framework Audited & System Card Published | xAI Production Safety & Alignment 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, audio | text, vision |
| Output Modalities | text, audio | text |
GPT-6 Astra vs Grok-4: 핵심 요약 문답 (FAQ)
Which is more intelligent, GPT-6 Astra or Grok-4?
GPT-6 Astra is more intelligent. It scores 96 compared with 92 on the composite Intelligence Index.
Which model is faster?
Grok-4 is faster, generating 125 tokens per second compared with 100 tokens per second.
Which model is cheaper to run?
Grok-4 is cheaper at $1.68 per 1M tokens blended, compared with $7.70 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Grok-4 has lower latency, with a time to first token of 0.68s compared with 0.95s.
Which model has a larger context window?
Grok-4 has a larger context window, supporting 1M tokens compared with 272K tokens.
종합 평가
Verdict & Model Selection Recommendation: Choose GPT-6 Astra if your priority is Autonomous browser/computer workflows, real-time voice agents, and high-difficulty mathematical reasoning. Its performance in demanding tasks justifies its operational token cost. Choose Grok-4 if your workflow prioritizes Breaking news analysis, real-time market sentiment, coding, and fast multimodal reasoning. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
자주 묻는 질문 (FAQ)
How does GPT-6 Astra compare to Grok-4 in API token pricing and cost per token?+
GPT-6 Astra costs $10.00 per 1M input tokens and $50.00 per 1M output tokens, whereas Grok-4 costs $2.50 input and $10.00 output. Overall, Grok-4 is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: GPT-6 Astra or Grok-4?+
GPT-6 Astra supports a context window of 272K tokens (~204k words), while Grok-4 supports 1,000K tokens (~750k words). Therefore, Grok-4 can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: GPT-6 Astra or Grok-4?+
GPT-6 Astra scores 82.4% on SWE-bench Verified while Grok-4 scores 76.5%. For autonomous software engineering and multi-file debugging, GPT-6 Astra holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between GPT-6 Astra and Grok-4?+
GPT-6 Astra features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Grok-4 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between GPT-6 Astra and Grok-4?+
GPT-6 Astra generates approximately 100 tokens per second, while Grok-4 delivers 125 tokens per second. In latency-sensitive production environments, Grok-4 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining GPT-6 Astra and Grok-4 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Grok-4 to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to GPT-6 Astra. This tiered routing pattern cuts overall API bills by up to 70%.
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: GetInfoUs Independent AI Benchmarks & Official API Specs