Muse Spark 1.1 vs GPT-6 Astra
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Muse Spark 1.1 by Meta 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.
Muse Spark 1.1
네이티브 멀티모달 AIMeta의 Muse Spark 1.1 모델은 텍스트와 이미지의 네이티브 멀티모달 이해 및 복합 문서·차트 분석에 최적화된 AI 모델로, 1M 컨텍스트 윈도우, 초당 105 토큰의 생성 속도, 종합 지능 지수 90/100점(1M 토큰당 입력 $3 / 출력 $15)을 제공합니다.
$3.00 / 1M
$15.00 / 1M
1M tokens
76.5%
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
- Muse Spark 1.1 is 3.3x cheaper for API usage (input + output tokens combined).
- Muse Spark 1.1 has a larger context window (1M vs 272K tokens).
- Muse Spark 1.1 is faster at 105 tok/s vs 100 tok/s.
- GPT-6 Astra leads on SWE-bench coding (82.4% vs 76.5%).
| Feature | Muse Spark 1.1 | GPT-6 Astra |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 90 | 96 |
Provider | Meta | OpenAI |
Input Price Cost per million input tokens | $3.00 / 1M | $10.00 / 1M |
Output Price Cost per million output tokens | $15.00 / 1M | $50.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $2.31 / 1M | $7.70 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 A4 pages of text | 272K tokens~408 A4 pages of text |
Max Output Tokens Per single request | 64K | 128K |
Throughput Speed Tokens generated per second | 105 tok/s | 100 tok/s |
SWE-bench Verified Real-world software engineering | 76.5% | 82.4% |
GPQA Diamond Graduate-level reasoning | 83.5% | 80.9% |
Cached Input Price Prompt caching discount per 1M tokens | $0.30 / 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.
Muse Spark 1.1 · Strengths & Key Advantages
- 1M 컨텍스트 윈도우 및 최대 64K 출력 토큰 생성 지원 (SWE-bench Verified 76.5% · GPQA Diamond 83.5%)
- 초당 105 토큰의 빠른 처리 속도와 600ms에 불과한 첫 토큰 지연 시간(TTFT)
- 합리적인 API 요금 체계(1M 토큰당 입력 $3 / 출력 $15)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
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 | Muse Spark 1.1 | GPT-6 Astra |
|---|---|---|
Cost per Task Estimated USD to finish one task | $2.20 | $2.15 |
Output Tokens / Task Tokens generated per task | 45K | 58K |
Reasoning Tokens / Task Thinking tokens burned per task | 22K | 32K |
기술 사양 (Specifications)
Architecture, release timeline, capabilities, and modalities.
| Feature | Muse Spark 1.1 | GPT-6 Astra |
|---|---|---|
| Official API Model ID (SDK) | meta-llama/Muse-Spark-1.1 | gpt-6-astra |
| API Protocol | Meta REST API (OpenAI-Compatible) | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2026-04 | 2026-09 |
| Knowledge Cutoff | January 2026 | June 2026 |
| Architecture | Omni-modal native thinking agent architecture | Omni-Adaptive Autoregressive with Dynamic CoT |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 4,000,000 – 10,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($3/1M in) across full 1000K window | Flat rate ($10/1M in) across full 272K window |
| Prompt Cache Policy & TTL | Cached input at $0.3/1M tokens | 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 | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | API Prompt Engineering & RAG 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) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) | 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 | Meta Production Safety & Alignment 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, video, audio | text, vision, audio |
| Output Modalities | text | text, audio |
Muse Spark 1.1 vs GPT-6 Astra: 핵심 요약 문답 (FAQ)
Which is more intelligent, Muse Spark 1.1 or GPT-6 Astra?
GPT-6 Astra is more intelligent. It scores 96 compared with 90 on the composite Intelligence Index.
Which model is faster?
Muse Spark 1.1 is faster, generating 105 tokens per second compared with 100 tokens per second.
Which model is cheaper to run?
Muse Spark 1.1 is cheaper at $2.31 per 1M tokens blended, compared with $7.70 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Muse Spark 1.1 has lower latency, with a time to first token of 0.60s compared with 0.95s.
Which model has a larger context window?
Muse Spark 1.1 has a larger context window, supporting 1M tokens compared with 272K tokens.
종합 평가
Choose Muse Spark 1.1 for 텍스트와 이미지의 네이티브 멀티모달 이해 및 복합 문서·차트 분석, 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: Muse Spark 1.1 or GPT-6 Astra?+
Muse Spark 1.1 costs $3/1M input and $15/1M output tokens, while GPT-6 Astra costs $10/1M input and $50/1M output tokens.
Which model has a larger context window: Muse Spark 1.1 or GPT-6 Astra?+
Muse Spark 1.1 supports 1M 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: Meta & OpenAI Official Documentation