Llama 5 vs Muse Spark 1.1
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Meta 的 Llama 5 与 Meta 的 Muse Spark 1.1 是当前备受关注的两款主力 AI 模型。Llama 5 提供 5M Token 上下文窗口，API 定价为每百万 Token 输入 $0.00 / 输出 $0.00；而 Muse Spark 1.1 提供 1M Token 上下文窗口，定价为每百万 Token 输入 $3.00 / 输出 $15.00。以下为两者的基准测试、延迟与成本全面对比。
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Llama 5
开源权重基础大模型Meta 推出的 Llama 5 是一款支持私有云服务器自托管、微调与完全数据主权的开源权重基础大模型，具备 5M 上下文窗口、65 Token/秒生成速度与 94/100 综合智能指数（免费开源权重）。
$0.00 / 1M
$0.00 / 1M
5M tokens
74%
Muse Spark 1.1
原生多模态视觉模型Meta 推出的 Muse Spark 1.1 是一款原生支持图文多模态理解、视觉图表解析与跨模态推理的大模型，具备 1M 上下文窗口、105 Token/秒生成速度与 90/100 综合智能指数（每百万 Token $3 输入 / $15 输出）。
$3.00 / 1M
$15.00 / 1M
1M tokens
76.5%
核心差异一览
- 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 |
各项指标谁更胜一筹？
At-a-glance decision framework权威基准测试得分对比
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.
延迟与响应速度
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
实时 Token 成本模拟器
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Llama 5 · Strengths & Key Advantages
- 提供 5M 上下文窗口与最高 128K 输出容量，核心基准表现优异（SWE-bench Verified 74% · GPQA Diamond 84%）
- 高达 65 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 850 毫秒
- 开放模型权重 (600B+ total (~40-80B ))，支持通过 vLLM、SGLang 或 Ollama 在私有 GPU 集群上本地部署与微调
Considerations & Trade-offs
- 在本地以满血精度运行需要较高的 GPU 显存 (VRAM) 与运维基础设施
- 在超长链路的前沿科研推导任务上，深度略逊于顶配重型旗舰推理模型
Muse Spark 1.1 · Strengths & Key Advantages
- 提供 1M 上下文窗口与最高 64K 输出容量，核心基准表现优异（SWE-bench Verified 76.5% · GPQA Diamond 83.5%）
- 高达 105 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 600 毫秒
- 极具竞争力的 API 定价（每百万 Token $3 输入 / $15 输出），原生支持提示词缓存、函数调用与结构化 JSON 输出
Considerations & Trade-offs
- 闭源专有云端模型，不支持下载权重进行离线本地物理隔离部署
- 在大规模高频调用场景下，建议结合提示词缓存 (Prompt Caching) 与批量 API 以控制成本
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 |
技术规格参数
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: 常见问题解答 (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.
综合结论
综合来看，Llama 5（适合：支持私有云服务器自托管、微调与完全数据主权的开源权重基础任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。）与 Muse Spark 1.1（适合：原生支持图文多模态理解、视觉图表解析与跨模态推理的任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。）各具优势：Llama 5 在综合智能与编程基准测试中表现更强，Muse Spark 1.1 拥有更快的 Token 生成速度，而 Llama 5 则在规模化 API 部署中具备更高的成本效益。
常见问题 (FAQ)
在编程和深度推理方面，Llama 5 和 Muse Spark 1.1 哪个更强？+
在综合智能指数与编程基准评测中，Llama 5 表现占优。建议结合上方的 SWE-bench Verified 与 GPQA Diamond 跑分根据具体场景选择。
Llama 5 与 Muse Spark 1.1 的 API 价格哪个更便宜？+
Llama 5 的 API 价格为输入 $0.00 / 输出 $0.00（每百万 Token），Muse Spark 1.1 为输入 $3.00 / 输出 $15.00。综合混合成本来看，Llama 5 更具性价比。
哪款模型拥有更大的上下文窗口和更快的响应速度？+
Llama 5 支持 5M Token 上下文窗口，而 Muse Spark 1.1 支持 1M Token。在流式生成速度方面，Muse Spark 1.1 的吞吐表现更快。
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.
数据来源: Meta 与 Meta 官方 API 文档、模型发布说明及独立基准测试报告 (2026)。