GPT-5.2 Codex vs GPT-6 Astra
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare GPT-5.2 Codex by OpenAI against GPT-6 Astra by OpenAI across real-world coding, reasoning, token economics, and latency.
원하는 AI 모델 선택 및 실시간 비교
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Live comparison: no in-depth editorial review has been published for this pair yet.
GPT-5.2 Codex
자율 에이전트 및 도구 호출 특화OpenAI의 GPT-5.2 Codex 모델은 자율 에이전트 워크플로우, 도구 호출(Tool Calling) 및 다단계 업무 자동화에 최적화된 AI 모델로, 400K 컨텍스트 윈도우, 초당 118 토큰의 생성 속도, 종합 지능 지수 66/100점(1M 토큰당 입력 $1.75 / 출력 $14)을 제공합니다.
$1.75 / 1M
$14.00 / 1M
400K tokens
78.1%
GPT-6 Astra
네이티브 음성 및 오디오 모델OpenAI의 GPT-6 Astra 모델은 자연스러운 음성 합성(TTS), 실시간 오디오 처리 및 고정밀 음성 인식에 최적화된 AI 모델로, 272K 컨텍스트 윈도우, 초당 100 토큰의 생성 속도, 종합 지능 지수 96/100점(1M 토큰당 입력 $10 / 출력 $50)을 제공합니다.
$10.00 / 1M
$50.00 / 1M
272K tokens
82.4%
한눈에 보는 핵심 차이점
- GPT-5.2 Codex is 4.0x cheaper for API usage (input + output tokens combined).
- GPT-5.2 Codex has a larger context window (400K vs 272K tokens).
- GPT-5.2 Codex is faster at 118 tok/s vs 100 tok/s.
- GPT-6 Astra leads on SWE-bench coding (82.4% vs 78.1%).
| Feature | GPT-5.2 Codex | GPT-6 Astra |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 66 | 96 |
Provider | OpenAI | OpenAI |
Input Price Cost per million input tokens | $1.75 / 1M | $10.00 / 1M |
Output Price Cost per million output tokens | $14.00 / 1M | $50.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.87 / 1M | $7.70 / 1M |
Context Window Maximum input + output tokens | 400K tokens~600 A4 pages of text | 272K tokens~408 A4 pages of text |
Max Output Tokens Per single request | 128K | 128K |
Throughput Speed Tokens generated per second | 118 tok/s | 100 tok/s |
SWE-bench Verified Real-world software engineering | 78.1% | 82.4% |
GPQA Diamond Graduate-level reasoning | 78.2% | 80.9% |
Cached Input Price Prompt caching discount per 1M tokens | $0.17 / 1M | $1.00 / 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-5.2 Codex · Strengths & Key Advantages
- 400K 컨텍스트 윈도우 및 최대 128K 출력 토큰 생성 지원 (SWE-bench Verified 78.1% · GPQA Diamond 78.2%)
- 초당 118 토큰의 빠른 처리 속도와 1280ms에 불과한 첫 토큰 지연 시간(TTFT)
- 합리적인 API 요금 체계(1M 토큰당 입력 $1.75 / 출력 $14)와 프롬프트 캐싱, 함수 호출 및 정형화된 JSON 출력 완벽 지원
Considerations & Trade-offs
- 독점 클라우드 전용 모델로 로컬 오프라인 서버에 가중치를 다운로드하여 실행할 수 없습니다
- 초고난도 학술 연구 및 수학 증명 과제에서는 상위 대형 추론 특화 모델이 더 깊은 분석을 제공할 수 있습니다
GPT-6 Astra · Strengths & Key Advantages
- 272K 컨텍스트 윈도우 및 최대 128K 출력 토큰 생성 지원 (SWE-bench Verified 82.4% · GPQA Diamond 80.9%)
- 초당 100 토큰의 빠른 처리 속도와 950ms에 불과한 첫 토큰 지연 시간(TTFT)
- 합리적인 API 요금 체계(1M 토큰당 입력 $10 / 출력 $50)와 프롬프트 캐싱, 함수 호출 및 정형화된 JSON 출력 완벽 지원
Considerations & Trade-offs
- 독점 클라우드 전용 모델로 로컬 오프라인 서버에 가중치를 다운로드하여 실행할 수 없습니다
- 대규모 프로덕션 호출 환경에서는 비용 절감을 위해 프롬프트 캐싱과 배치 API(Batch API) 활용이 권장됩니다
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-5.2 Codex | GPT-6 Astra |
|---|---|---|
Cost per Task Estimated USD to finish one task | $2.20 | $2.15 |
Output Tokens / Task Tokens generated per task | 60K | 58K |
Reasoning Tokens / Task Thinking tokens burned per task | 34K | 32K |
기술 사양 (Specifications)
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-5.2 Codex | GPT-6 Astra |
|---|---|---|
| Official API Model ID (SDK) | gpt-5-2-codex | gpt-6-astra |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-12 | 2026-09 |
| Knowledge Cutoff | August 2025 | June 2026 |
| Architecture | Code-specialised long-horizon reasoning 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 ($1.75/1M in) across full 400K window | Flat rate ($10/1M in) across full 272K window |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.175/1M (no extra cache-write fee) | 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 | 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 | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | 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 | OpenAI Preparedness Framework Audited & System Card Published | 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 | text | text, audio |
GPT-5.2 Codex vs GPT-6 Astra: 핵심 요약 문답 (FAQ)
Which is more intelligent, GPT-5.2 Codex or GPT-6 Astra?
GPT-6 Astra is more intelligent. It scores 96 compared with 66 on the composite Intelligence Index.
Which model is faster?
GPT-5.2 Codex is faster, generating 118 tokens per second compared with 100 tokens per second.
Which model is cheaper to run?
GPT-5.2 Codex is cheaper at $1.87 per 1M tokens blended, compared with $7.70 (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 1.28s.
Which model has a larger context window?
GPT-5.2 Codex has a larger context window, supporting 400K tokens compared with 272K tokens.
종합 평가
Choose GPT-5.2 Codex for 자율 에이전트 워크플로우, 도구 호출(tool calling) 및 다단계 업무 자동화, 엔터프라이즈 rag 지식베이스 및 대규모 프로덕션 api 연동에 최적., or choose GPT-6 Astra for 자연스러운 음성 합성(tts), 실시간 오디오 처리 및 고정밀 음성 인식, 엔터프라이즈 rag 지식베이스 및 대규모 프로덕션 api 연동에 최적..
자주 묻는 질문 (FAQ)
Which is cheaper: GPT-5.2 Codex or GPT-6 Astra?+
GPT-5.2 Codex costs $1.75/1M input and $14/1M output tokens, while GPT-6 Astra costs $10/1M input and $50/1M output tokens.
Which model has a larger context window: GPT-5.2 Codex or GPT-6 Astra?+
GPT-5.2 Codex supports 400K 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.
Related reading
- Aion-3.0 vs Aion-Mini: 벤치마크, API 요금 및 속도 비교 (2026)비교
- Aion-3.0 vs Gemini 3.6 Flash: 벤치마크, API 요금 및 속도 비교 (2026)비교
- Aion-Mini vs Laguna XS 2.1: 벤치마크, API 요금 및 속도 비교 (2026)비교
- Aya Expanse 32B vs Llama 3.2 90B Vision: 벤치마크, API 요금 및 속도 비교 (2026)비교
- Aya Expanse 8B vs Command R+: 벤치마크, API 요금 및 속도 비교 (2026)비교
- Aya Expanse 8B vs Qwen 2.5 7B Instruct: 벤치마크, API 요금 및 속도 비교 (2026)비교
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: OpenAI & OpenAI Official Documentation