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.
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Muse Spark 1.1
ネイティブ・マルチモーダルAIMeta の Muse Spark 1.1 は、テキストと画像のネイティブ・マルチモーダル理解および図表・ドキュメント解析向けに設計されたAIモデルです。1M のコンテキストウィンドウ、毎秒 105 トークンの生成速度、知能指数 90/100（1Mトークンあたり入力$3・出力$15）を実現しています。
$3.00 / 1M
$15.00 / 1M
1M tokens
76.5%
GPT-6 Astra
音声・スピーチAIモデルOpenAI の GPT-6 Astra は、自然な音声合成（TTS）、リアルタイム音声対話、高精度な文字起こし向けに設計されたAIモデルです。272K のコンテキストウィンドウ、毎秒 100 トークンの生成速度、知能指数 96/100（1Mトークンあたり入力$10・出力$50）を実現しています。
$10.00 / 1M
$50.00 / 1M
272K tokens
82.4%
ひと目でわかる主な違い
- 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 |
分野別勝者スコアカード
At-a-glance decision framework検証済みベンチマークスコア比較
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.
レイテンシ＆応答時間
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
リアルタイムAPIトークン料金シミュレーター
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Muse Spark 1.1 · Strengths & Key Advantages
- 1M のコンテキストウィンドウと最大 64K トークンの出力容量（SWE-bench Verified 76.5% · GPQA Diamond 83.5%）
- 毎秒 105 トークンの高速スループットと、初回トークン到達時間（TTFT）わずか 600ms の低遅延
- 優れたAPI経済性（1Mトークンあたり入力$3・出力$15）に加え、プロンプトキャッシュや構造化JSON出力・関数呼び出しに完全対応
Considerations & Trade-offs
- クラウド専用のプロプライエタリモデルのため、モデル重みをダウンロードしてオフライン実行することはできません
- 大量のトークンを処理する本番環境では、プロンプトキャッシュや Batch API の併用によるコスト最適化が推奨されます
GPT-6 Astra · Strengths & Key Advantages
- 272K のコンテキストウィンドウと最大 128K トークンの出力容量（SWE-bench Verified 82.4% · GPQA Diamond 80.9%）
- 毎秒 100 トークンの高速スループットと、初回トークン到達時間（TTFT）わずか 950ms の低遅延
- 優れたAPI経済性（1Mトークンあたり入力$10・出力$50）に加え、プロンプトキャッシュや構造化JSON出力・関数呼び出しに完全対応
Considerations & Trade-offs
- クラウド専用のプロプライエタリモデルのため、モデル重みをダウンロードしてオフライン実行することはできません
- 大量のトークンを処理する本番環境では、プロンプトキャッシュや Batch API の併用によるコスト最適化が推奨されます
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 |
技術仕様・スペック
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: よくある質問 (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.
総合評価
Choose Muse Spark 1.1 for テキストと画像のネイティブ・マルチモーダル理解および図表・ドキュメント解析、エンタープライズrag基盤、スケーラブルな本番api統合に最適。, or choose GPT-6 Astra for 自然な音声合成（tts）、リアルタイム音声対話、高精度な文字起こし、エンタープライズrag基盤、スケーラブルな本番api統合に最適。.
よくある質問 (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.
公的ソース: Meta & OpenAI Official Documentation