Veo 3.1 vs GPT-6 Astra
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Veo 3.1 by Google against GPT-6 Astra by OpenAI 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.
Veo 3.1
플래그십 프론티어 모델Google의 Veo 3.1 모델은 4K 토큰 컨텍스트 윈도우와 입력 $7/1M 토큰, 출력 $450/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$7.00 / 1M
$450.00 / 1M
4.1K tokens
N/A
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%
한눈에 보는 핵심 차이점
- GPT-6 Astra is 6.8x cheaper for API usage (input + output tokens combined).
- GPT-6 Astra has a larger context window (272K vs 4K tokens).
- GPT-6 Astra is faster at 100 tok/s vs 14 tok/s.
| Feature | Veo 3.1 | GPT-6 Astra |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 88 | 96 |
Provider | OpenAI | |
Input Price Cost per million input tokens | $7.00 / 1M | $10.00 / 1M |
Output Price Cost per million output tokens | $450.00 / 1M | $50.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $46.40 / 1M | $7.70 / 1M |
Context Window Maximum input + output tokens | 4.1K tokens~6 A4 pages of text | 272K tokens~408 A4 pages of text |
Max Output Tokens Per single request | 4K | 128K |
Throughput Speed Tokens generated per second | 14 tok/s | 100 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 82.4% |
GPQA Diamond Graduate-level reasoning | N/A | 80.9% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $1.00 / 1M |
부문별 우위 모델
At-a-glance decision frameworkEvaluations ongoing
검증된 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.
Veo 3.1 · Strengths & Key Advantages
- Google의 첨단 Controllable audio-video generation and in-context video editor 아키텍처와 최대 4K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $7/1M, 출력 $450/1M)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
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
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Veo 3.1 | GPT-6 Astra |
|---|---|---|
Cost per Task Estimated USD to finish one task | $3.00 | $2.15 |
Output Tokens / Task Tokens generated per task | 0K | 58K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 32K |
기술 사양 (Specifications)
Architecture, release timeline, capabilities, and modalities.
| Feature | Veo 3.1 | GPT-6 Astra |
|---|---|---|
| Official API Model ID (SDK) | models/veo-3-1 | gpt-6-astra |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-10 | 2026-09 |
| Knowledge Cutoff | July 2025 | June 2026 |
| Architecture | Controllable audio-video generation and in-context video editor | Omni-Adaptive Autoregressive with Dynamic CoT |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($7/1M in) across full 4K window | Flat rate ($10/1M in) across full 272K window |
| Prompt Cache Policy & TTL | Implicit + Explicit Context Caching at $0/1M with configurable TTL | Automatic prefix caching at $1/1M (no extra cache-write fee) |
| 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 | Prompt-based JSON extraction only | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Built-in Google Search Grounding + Managed Python Code Execution Sandbox | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Vertex AI Supervised Adapter Tuning & Context Distillation | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) |
| 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) |
| 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 | Google DeepMind Frontier Safety Framework Verified | OpenAI Preparedness Framework Audited & System Card Published |
| 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 |
| Output Modalities | video | text, audio |
Veo 3.1 vs GPT-6 Astra: 핵심 요약 문답 (FAQ)
Which is more intelligent, Veo 3.1 or GPT-6 Astra?
GPT-6 Astra is more intelligent. It scores 96 compared with 88 on the composite Intelligence Index.
Which model is faster?
GPT-6 Astra is faster, generating 100 tokens per second compared with 14 tokens per second.
Which model is cheaper to run?
GPT-6 Astra is cheaper at $7.70 per 1M tokens blended, compared with $46.40 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GPT-6 Astra has lower latency, with a time to first token of 0.95s compared with 4.20s.
Which model has a larger context window?
GPT-6 Astra has a larger context window, supporting 272K tokens compared with 4.1K tokens.
종합 평가
Choose Veo 3.1 for 기업 업무 자동화, 소프트웨어 개발, 자율형 ai 에이전트 및 google 기반 프로덕션 파이프라인, or choose GPT-6 Astra for 기업 업무 자동화, 소프트웨어 개발, 자율형 ai 에이전트 및 openai 기반 프로덕션 파이프라인.
자주 묻는 질문 (FAQ)
Which is cheaper: Veo 3.1 or GPT-6 Astra?+
Veo 3.1 costs $7/1M input and $450/1M output tokens, while GPT-6 Astra costs $10/1M input and $50/1M output tokens.
Which model has a larger context window: Veo 3.1 or GPT-6 Astra?+
Veo 3.1 supports 4K tokens whereas GPT-6 Astra supports 272K 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: Google & OpenAI Official Documentation