Claude 3.5 Haiku vs Llama 5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Claude 3.5 Haiku by Anthropic against Llama 5 by Meta across real-world coding, reasoning, token economics, and latency.
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Claude 3.5 Haiku
自主智能体与工具专家Anthropic 推出的 Claude 3.5 Haiku 是一款专为长周期自主智能体 (Agent)、工具调用与自动化工作流打造的大模型，具备 200K 上下文窗口、130 Token/秒生成速度与 36/100 综合智能指数（每百万 Token $0.8 输入 / $4 输出）。
$0.80 / 1M
$4.00 / 1M
200K tokens
40.6%
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%
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 200K tokens).
- Claude 3.5 Haiku is faster at 130 tok/s vs 65 tok/s.
- Llama 5 leads on SWE-bench coding (74.0% vs 40.6%).
| Feature | Claude 3.5 Haiku | Llama 5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 36 | 94 |
Provider | Anthropic | Meta |
Input Price Cost per million input tokens | $0.80 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $4.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.62 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 200K tokens~300 A4 pages of text | 5M tokens~7,500 A4 pages of text |
Max Output Tokens Per single request | 8K | 128K |
Throughput Speed Tokens generated per second | 130 tok/s | 65 tok/s |
SWE-bench Verified Real-world software engineering | 40.6% | 74% |
GPQA Diamond Graduate-level reasoning | 41.6% | 84% |
Cached Input Price Prompt caching discount per 1M tokens | $0.08 / 1M | $0.00 / 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.
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.
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.
Claude 3.5 Haiku · Strengths & Key Advantages
- 提供 200K 上下文窗口与最高 8K 输出容量，核心基准表现优异（SWE-bench Verified 40.6% · GPQA Diamond 41.6%）
- 高达 130 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 380 毫秒
- 极具竞争力的 API 定价（每百万 Token $0.8 输入 / $4 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 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
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Claude 3.5 Haiku | Llama 5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.90 | $0.00 |
Output Tokens / Task Tokens generated per task | 45K | 40K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 20K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Claude 3.5 Haiku | Llama 5 |
|---|---|---|
| Official API Model ID (SDK) | claude-3-5-haiku-20241101 | meta-llama/Llama-5 |
| API Protocol | Anthropic Messages API + AWS Bedrock + Vertex AI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2024-11 | 2026-04 |
| Knowledge Cutoff | July 2024 | January 2026 |
| Architecture | Optimized Lightweight Transformer | Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active) |
| Parameters | Confidential | 600B+ total (~40-80B active) |
| Enterprise Rate Limits (RPM / TPM) | 4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 800,000 – 2,000,000 TPM | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | $0.08/1M cached read (90% off); 5-min default & 1-hour extended TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| 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 | Native Web Search Citations + Code Execution Tool + MCP Connectors | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Custom Model Tuning via Amazon Bedrock & Prompt Distillation | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | 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 | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 372 GB VRAM (4-bit Quantized) · 1290 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision | text, vision, video, audio |
| Output Modalities | text | text |
Claude 3.5 Haiku vs Llama 5: 常见问题解答 (FAQ)
Which is more intelligent, Claude 3.5 Haiku or Llama 5?
Llama 5 is more intelligent. It scores 94 compared with 36 on the composite Intelligence Index.
Which model is faster?
Claude 3.5 Haiku is faster, generating 130 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 $0.62 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Claude 3.5 Haiku has lower latency, with a time to first token of 0.38s compared with 0.85s.
Which model has a larger context window?
Llama 5 has a larger context window, supporting 5M tokens compared with 200K tokens.
综合结论
Choose Claude 3.5 Haiku for 专为长周期自主智能体 (agent)、工具调用与自动化工作流打造的任务、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。, or choose Llama 5 for 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。.
常见问题 (FAQ)
Which is cheaper: Claude 3.5 Haiku or Llama 5?+
Claude 3.5 Haiku costs $0.8/1M input and $4/1M output tokens, while Llama 5 costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: Claude 3.5 Haiku or Llama 5?+
Claude 3.5 Haiku supports 200K tokens whereas Llama 5 supports 5M 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.
数据来源: Anthropic & Meta Official Documentation