Gemini 3.1 Flash TTS vs GPT-6 Astra
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Gemini 3.1 Flash TTS by Google against GPT-6 Astra by OpenAI across real-world coding, reasoning, token economics, and latency.
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Gemini 3.1 Flash TTS
플래그십 프론티어 모델Google의 Gemini 3.1 Flash TTS 모델은 8K 토큰 컨텍스트 윈도우와 입력 $0.75/1M 토큰, 출력 $12/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$0.75 / 1M
$12.00 / 1M
8.2K 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
- Gemini 3.1 Flash TTS is 5.2x cheaper for API usage (input + output tokens combined).
- GPT-6 Astra has a larger context window (272K vs 8K tokens).
- Gemini 3.1 Flash TTS is faster at 120 tok/s vs 100 tok/s.
| Feature | Gemini 3.1 Flash TTS | GPT-6 Astra |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 72 | 96 |
Provider | OpenAI | |
Input Price Cost per million input tokens | $0.75 / 1M | $10.00 / 1M |
Output Price Cost per million output tokens | $12.00 / 1M | $50.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.35 / 1M | $7.70 / 1M |
Context Window Maximum input + output tokens | 8.2K tokens~12 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 | 120 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.
Gemini 3.1 Flash TTS · Strengths & Key Advantages
- Google의 첨단 Ultra-low-latency expressive acoustic speech model 아키텍처와 최대 8K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0.75/1M, 출력 $12/1M)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 | Gemini 3.1 Flash TTS | GPT-6 Astra |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.75 | $2.15 |
Output Tokens / Task Tokens generated per task | 4K | 58K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 32K |
기술 사양 (Specifications)
Architecture, release timeline, capabilities, and modalities.
| Feature | Gemini 3.1 Flash TTS | GPT-6 Astra |
|---|---|---|
| Official API Model ID (SDK) | models/gemini-3-1-flash-tts | gpt-6-astra |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2026-04 | 2026-09 |
| Knowledge Cutoff | January 2026 | June 2026 |
| Architecture | Ultra-low-latency expressive acoustic speech model | 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 ($0.75/1M in) across full 8K 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 | Text-only completion (No GUI action loop) | 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) | 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 | text, vision, audio |
| Output Modalities | audio | text, audio |
Gemini 3.1 Flash TTS vs GPT-6 Astra: 핵심 요약 문답 (FAQ)
Which is more intelligent, Gemini 3.1 Flash TTS or GPT-6 Astra?
GPT-6 Astra is more intelligent. It scores 96 compared with 72 on the composite Intelligence Index.
Which model is faster?
Gemini 3.1 Flash TTS is faster, generating 120 tokens per second compared with 100 tokens per second.
Which model is cheaper to run?
Gemini 3.1 Flash TTS is cheaper at $1.35 per 1M tokens blended, compared with $7.70 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Gemini 3.1 Flash TTS has lower latency, with a time to first token of 0.25s compared with 0.95s.
Which model has a larger context window?
GPT-6 Astra has a larger context window, supporting 272K tokens compared with 8.2K tokens.
종합 평가
Choose Gemini 3.1 Flash TTS 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: Gemini 3.1 Flash TTS or GPT-6 Astra?+
Gemini 3.1 Flash TTS costs $0.75/1M input and $12/1M output tokens, while GPT-6 Astra costs $10/1M input and $50/1M output tokens.
Which model has a larger context window: Gemini 3.1 Flash TTS or GPT-6 Astra?+
Gemini 3.1 Flash TTS supports 8K 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