Llama 3 (8B) vs Llama 3 (70B)
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Llama 3 (8B) by Meta and Llama 3 (70B) 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 3 (8B) holds a notable pricing advantage for token ingestion. On output generation, Llama 3 (8B) is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical memory horizon with 8K tokens. In latency-sensitive production environments, Llama 3 (8B) 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.
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Llama 3 (8B)
开源权重模型Meta 推出的 Llama 3 (8B) 人工智能模型，具备 8K 上下文窗口，输入价格为 $0.05/百万 Token，输出价格为 $0.1/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$0.05 / 1M
$0.10 / 1M
8.2K tokens
12%
Llama 3 (70B)
开源权重模型Meta 推出的 Llama 3 (70B) 人工智能模型，具备 8K 上下文窗口，输入价格为 $0.35/百万 Token，输出价格为 $0.8/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$0.35 / 1M
$0.80 / 1M
8.2K tokens
24%
核心差异一览
- Llama 3 (8B) is 7.5x cheaper for API usage (input + output tokens combined).
- Llama 3 (8B) is faster at 210 tok/s vs 110 tok/s.
- Llama 3 (70B) leads on SWE-bench coding (24.0% vs 12.0%).
| Feature | Llama 3 (8B) | Llama 3 (70B) |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 34 | 42 |
Provider | Meta | Meta |
Input Price Cost per million input tokens | $0.05 / 1M | $0.35 / 1M |
Output Price Cost per million output tokens | $0.10 / 1M | $0.80 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.03 / 1M | $0.21 / 1M |
Context Window Maximum input + output tokens | 8.2K tokens~12 A4 pages of text | 8.2K tokens~12 A4 pages of text |
Max Output Tokens Per single request | 4K | 4K |
Throughput Speed Tokens generated per second | 210 tok/s | 110 tok/s |
SWE-bench Verified Real-world software engineering | 12% | 24% |
GPQA Diamond Graduate-level reasoning | 34.2% | 46% |
Cached Input Price Prompt caching discount per 1M tokens | $0.01 / 1M | $0.09 / 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.
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 3 (8B) · Strengths & Key Advantages
- 由 Meta 研发的高精度 Dense GQA Transformer with 128K Tokenizer 架构，支持 8K 上下文窗口
- 极具竞争力的 API 定价（输入 $0.05/1M，输出 $0.1/1M），支持高速推理与工具调用
- 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
- 超长上下文请求需要规划 Token 成本与并发配额
- 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果
Llama 3 (70B) · Strengths & Key Advantages
- 由 Meta 研发的高精度 GQA High-Capacity Transformer 架构，支持 8K 上下文窗口
- 极具竞争力的 API 定价（输入 $0.35/1M，输出 $0.8/1M），支持高速推理与工具调用
- 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
- 超长上下文请求需要规划 Token 成本与并发配额
- 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Llama 3 (8B) | Llama 3 (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 3 (8B) | Llama 3 (70B) |
|---|---|---|
| Official API Model ID (SDK) | meta-llama/Llama-3-(8B) | meta-llama/Llama-3-(70B) |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2024-04 | 2024-04 |
| Knowledge Cutoff | March 2024 | March 2024 |
| Architecture | Dense GQA Transformer with 128K Tokenizer | GQA High-Capacity Transformer |
| Parameters | 8B | 70B |
| 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) | Flat rate ($0.05/1M in) across full 8K window | Flat rate ($0.35/1M in) across full 8K window |
| Prompt Cache Policy & TTL | Cached input at $0.0125/1M tokens | Cached input at $0.0875/1M tokens |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) |
| 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) | 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) · 5 GB VRAM (4-bit Quantized) · 17 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 3 (8B) vs Llama 3 (70B): 常见问题解答 (FAQ)
Which is more intelligent, Llama 3 (8B) or Llama 3 (70B)?
Llama 3 (70B) is more intelligent. It scores 42 compared with 34 on the composite Intelligence Index.
Which model is faster?
Llama 3 (8B) is faster, generating 210 tokens per second compared with 110 tokens per second.
Which model is cheaper to run?
Llama 3 (8B) is cheaper at $0.03 per 1M tokens blended, compared with $0.21 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Llama 3 (8B) has lower latency, with a time to first token of 0.22s compared with 0.42s.
综合结论
Verdict & Model Selection Recommendation: Choose Llama 3 (8B) if your priority is Local edge computing, offline laptop inference, robotics, and low-latency API routers. Its performance in demanding tasks justifies its operational token cost. Choose Llama 3 (70B) if your workflow prioritizes Enterprise private LLM hosting, on-premises compliance workflows, and domain fine-tuning. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
常见问题 (FAQ)
How does Llama 3 (8B) compare to Llama 3 (70B) in API token pricing and cost per token?+
Llama 3 (8B) costs $0.05 per 1M input tokens and $0.10 per 1M output tokens, whereas Llama 3 (70B) costs $0.35 input and $0.80 output. Overall, Llama 3 (8B) 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 3 (8B) or Llama 3 (70B)?+
Both Llama 3 (8B) and Llama 3 (70B) offer an identical context window of 8K tokens (~6k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.
Which AI model is better for software engineering and coding: Llama 3 (8B) or Llama 3 (70B)?+
Llama 3 (8B) scores 12% on SWE-bench Verified while Llama 3 (70B) scores 24%. For autonomous software engineering and multi-file debugging, Llama 3 (70B) holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Llama 3 (8B) and Llama 3 (70B)?+
Llama 3 (8B) is optimized for low-latency direct generation. In contrast, Llama 3 (70B) focuses on standard fast inference.
How does inference speed (tokens per second) compare between Llama 3 (8B) and Llama 3 (70B)?+
Llama 3 (8B) generates approximately 210 tokens per second, while Llama 3 (70B) delivers 110 tokens per second. In latency-sensitive production environments, Llama 3 (8B) provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Llama 3 (8B) and Llama 3 (70B) to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Llama 3 (8B) to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Llama 3 (70B). 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.
数据来源: GetInfoUs Independent AI Benchmarks & Official API Specs