Llama 5 vs Muse Spark 1.1
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Llama 5 by Meta and Muse Spark 1.1 by Meta represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, Llama 5 holds a notable pricing advantage for token ingestion. On output generation, Llama 5 is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Llama 5 provides an expanded memory horizon with 5,000K tokens. In latency-sensitive production environments, Muse Spark 1.1 leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
任意のAIモデルを切り替えて比較
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Llama 5
オープンウェイトモデルMeta が提供する Llama 5 は、最大 5M トークンのコンテキストウィンドウを備え、入力 $0/100万トークン・出力 $0/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$0.00 / 1M
$0.00 / 1M
5M tokens
74%
Muse Spark 1.1
高度な推論モデルMeta が提供する Muse Spark 1.1 は、最大 1M トークンのコンテキストウィンドウを備え、入力 $3/100万トークン・出力 $15/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$3.00 / 1M
$15.00 / 1M
1M tokens
76.5%
ひと目でわかる主な違い
- Llama 5 is 1.0x cheaper for API usage (input + output tokens combined).
- Llama 5 has a larger context window (5M vs 1M tokens).
- Muse Spark 1.1 is faster at 105 tok/s vs 65 tok/s.
- Muse Spark 1.1 leads on SWE-bench coding (76.5% vs 74.0%).
| Feature | Llama 5 | Muse Spark 1.1 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 94 | 90 |
Provider | Meta | Meta |
Input Price Cost per million input tokens | $0.00 / 1M | $3.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $15.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $2.31 / 1M |
Context Window Maximum input + output tokens | 5M tokens~7,500 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 128K | 64K |
Throughput Speed Tokens generated per second | 65 tok/s | 105 tok/s |
SWE-bench Verified Real-world software engineering | 74% | 76.5% |
GPQA Diamond Graduate-level reasoning | 84% | 83.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.30 / 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.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
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.
Llama 5 · Strengths & Key Advantages
- Meta の高度な Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active) アーキテクチャと 5M トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $0/1M、出力 $0/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
Muse Spark 1.1 · Strengths & Key Advantages
- Meta の高度な Omni-modal native thinking agent architecture アーキテクチャと 1M トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $3/1M、出力 $15/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Llama 5 | Muse Spark 1.1 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $2.20 |
Output Tokens / Task Tokens generated per task | 40K | 45K |
Reasoning Tokens / Task Thinking tokens burned per task | 20K | 22K |
技術仕様・スペック
Architecture, release timeline, capabilities, and modalities.
| Feature | Llama 5 | Muse Spark 1.1 |
|---|---|---|
| Official API Model ID (SDK) | meta-llama/Llama-5 | meta-llama/Muse-Spark-1.1 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | Meta REST API (OpenAI-Compatible) |
| Release Date | 2026-04 | 2026-04 |
| Knowledge Cutoff | January 2026 | January 2026 |
| Architecture | Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active) | Omni-modal native thinking agent architecture |
| Parameters | 600B+ total (~40-80B active) | Confidential |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | 5,000 – 15,000 RPM (Production Tier) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Free across full context window (only KV-cache VRAM scales) | Flat rate ($3/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Cached input at $0.3/1M tokens |
| 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 | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | API Prompt Engineering & RAG Distillation |
| 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) · Video (~258–300 tokens/sec) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 372 GB VRAM (4-bit Quantized) · 1290 GB VRAM (Native BF16) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Meta Production Safety & Alignment Verified |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text, vision, video, audio | text, vision, video, audio |
| Output Modalities | text | text |
Llama 5 vs Muse Spark 1.1: よくある質問 (FAQ)
Which is more intelligent, Llama 5 or Muse Spark 1.1?
Llama 5 is more intelligent. It scores 94 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 65 tokens per second.
Which model is cheaper to run?
Llama 5 is cheaper at $0.00 per 1M tokens blended, compared with $2.31 (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 0.85s.
Which model has a larger context window?
Llama 5 has a larger context window, supporting 5M tokens compared with 1M tokens.
総合評価
Verdict & Model Selection Recommendation: Choose Llama 5 if your priority is Autonomous open-source multi-agent systems, deep software refactoring, and frontier research. Its performance in demanding tasks justifies its operational token cost. Choose Muse Spark 1.1 if your workflow prioritizes Enterprise multimodal agent systems, UI analysis, and complex collaborative task execution. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
よくある質問 (FAQ)
How does Llama 5 compare to Muse Spark 1.1 in API token pricing and cost per token?+
Llama 5 costs $0.00 per 1M input tokens and $0.00 per 1M output tokens, whereas Muse Spark 1.1 costs $3.00 input and $15.00 output. Overall, Llama 5 is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Llama 5 or Muse Spark 1.1?+
Llama 5 supports a context window of 5,000K tokens (~3,750k words), while Muse Spark 1.1 supports 1,000K tokens (~750k words). Therefore, Llama 5 can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Llama 5 or Muse Spark 1.1?+
Llama 5 scores 74% on SWE-bench Verified while Muse Spark 1.1 scores 76.5%. For autonomous software engineering and multi-file debugging, Muse Spark 1.1 holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Llama 5 and Muse Spark 1.1?+
Llama 5 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Muse Spark 1.1 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Llama 5 and Muse Spark 1.1?+
Llama 5 generates approximately 65 tokens per second, while Muse Spark 1.1 delivers 105 tokens per second. In latency-sensitive production environments, Muse Spark 1.1 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Llama 5 and Muse Spark 1.1 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Llama 5 to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Muse Spark 1.1. This tiered routing pattern cuts overall API bills by up to 70%.
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: GetInfoUs Independent AI Benchmarks & Official API Specs