LLaMA-1 (65B) vs Llama 2 (70B)
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Meta 的 LLaMA-1 (65B) 与 Meta 的 Llama 2 (70B) 是当前备受关注的两款主力 AI 模型。LLaMA-1 (65B) 提供 2K Token 上下文窗口，API 定价为每百万 Token 输入 $0.00 / 输出 $0.00；而 Llama 2 (70B) 提供 4.1K Token 上下文窗口，定价为每百万 Token 输入 $0.40 / 输出 $0.80。以下为两者的基准测试、延迟与成本全面对比。
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LLaMA-1 (65B)
开源权重基础大模型Meta 推出的 LLaMA-1 (65B) 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 2K 上下文窗口、45 Token/秒生成速度与 25/100 综合智能指数（免费开源权重）。
$0.00 / 1M
$0.00 / 1M
2K tokens
N/A
Llama 2 (70B)
开源权重基础大模型Meta 推出的 Llama 2 (70B) 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 4K 上下文窗口、55 Token/秒生成速度与 31/100 综合智能指数（每百万 Token $0.4 输入 / $0.8 输出）。
$0.40 / 1M
$0.80 / 1M
4.1K tokens
N/A
核心差异一览
- LLaMA-1 (65B) is 1.0x cheaper for API usage (input + output tokens combined).
- Llama 2 (70B) has a larger context window (4K vs 2K tokens).
- Llama 2 (70B) is faster at 55 tok/s vs 45 tok/s.
| Feature | LLaMA-1 (65B) | Llama 2 (70B) |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 25 | 31 |
Provider | Meta | Meta |
Input Price Cost per million input tokens | $0.00 / 1M | $0.40 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $0.80 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $0.23 / 1M |
Context Window Maximum input + output tokens | 2K tokens~3 A4 pages of text | 4.1K tokens~6 A4 pages of text |
Max Output Tokens Per single request | 2K | 4K |
Throughput Speed Tokens generated per second | 45 tok/s | 55 tok/s |
SWE-bench Verified Real-world software engineering | N/A | N/A |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.10 / 1M |
各项指标谁更胜一筹？
At-a-glance decision frameworkEvaluations ongoing
权威基准测试得分对比
Official evaluation metrics verified by independent third-party research labs.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
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-1 (65B) · Strengths & Key Advantages
- 提供 2K 上下文窗口与最高 2K 输出容量，核心基准表现优异（MMLU-Pro 48% · Arena Elo 1020）
- 高达 45 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 750 毫秒
- 开放模型权重 (65B (also 7B, 13B, 33B))，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
Llama 2 (70B) · Strengths & Key Advantages
- 提供 4K 上下文窗口与最高 4K 输出容量，核心基准表现优异（MMLU-Pro 56.8% · Arena Elo 1105）
- 高达 55 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 600 毫秒
- 开放模型权重 (70B (also 7B, 13B))，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
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | LLaMA-1 (65B) | Llama 2 (70B) |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.20 | $0.20 |
Output Tokens / Task Tokens generated per task | 24K | 24K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | LLaMA-1 (65B) | Llama 2 (70B) |
|---|---|---|
| Official API Model ID (SDK) | meta-llama/LLaMA-1-(65B) | meta-llama/Llama-2-(70B) |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2023-02 | 2023-07 |
| Knowledge Cutoff | 2022 | September 2022 |
| Architecture | Decoder-Only Transformer with RoPE & SwiGLU | Grouped-Query Attention (GQA) Transformer |
| Parameters | 65B (also 7B, 13B, 33B) | 70B (also 7B, 13B) |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Free across full context window (only KV-cache VRAM scales) | Flat rate ($0.4/1M in) across full 4K window |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Cached input at $0.1/1M tokens |
| Computer Use / GUI Agent | Text-only completion (No GUI action loop) | Text-only completion (No GUI action loop) |
| MCP & Parallel Tool Calling | Prompt-based JSON extraction only | Prompt-based JSON extraction only |
| 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) | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 40 GB VRAM (4-bit Quantized) · 140 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 43 GB VRAM (4-bit Quantized) · 151 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text | text |
| Output Modalities | text | text |
LLaMA-1 (65B) vs Llama 2 (70B): 常见问题解答 (FAQ)
Which is more intelligent, LLaMA-1 (65B) or Llama 2 (70B)?
Llama 2 (70B) is more intelligent. It scores 31 compared with 25 on the composite Intelligence Index.
Which model is faster?
Llama 2 (70B) is faster, generating 55 tokens per second compared with 45 tokens per second.
Which model is cheaper to run?
LLaMA-1 (65B) is cheaper at $0.00 per 1M tokens blended, compared with $0.23 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Llama 2 (70B) has lower latency, with a time to first token of 0.60s compared with 0.75s.
Which model has a larger context window?
Llama 2 (70B) has a larger context window, supporting 4.1K tokens compared with 2K tokens.
综合结论
综合来看，LLaMA-1 (65B)（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 Llama 2 (70B)（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。）各具优势：Llama 2 (70B) 在综合智能与编程基准测试中表现更强，Llama 2 (70B) 拥有更快的 Token 生成速度，而 LLaMA-1 (65B) 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-1 (65B) 和 Llama 2 (70B) 哪个更强？+
在综合智能指数与编程基准评测中，Llama 2 (70B) 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-1 (65B) 与 Llama 2 (70B) 的 API 价格哪个更便宜？+
LLaMA-1 (65B) 的 API 价格为输入 $0.00 / 输出 $0.00（每百万 Token），Llama 2 (70B) 为输入 $0.40 / 输出 $0.80。综合混合成本来看，LLaMA-1 (65B) 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-1 (65B) 支持 2K Token 上下文窗口，而 Llama 2 (70B) 支持 4.1K Token。在流式生成速度方面，Llama 2 (70B) 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)。