Claude 3 Haiku vs Llama 5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Claude 3 Haiku by Anthropic against Llama 5 by Meta across real-world coding, reasoning, token economics, and latency.
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Claude 3 Haiku
高性能 AI 模型Anthropic 推出的 Claude 3 Haiku 人工智能模型，具备 200K 上下文窗口，输入价格为 $0.25/百万 Token，输出价格为 $1.25/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$0.25 / 1M
$1.25 / 1M
200K tokens
10.5%
Llama 5
开源权重模型Meta 推出的 Llama 5 人工智能模型，具备 5M 上下文窗口，输入价格为 $0/百万 Token，输出价格为 $0/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$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 Haiku is faster at 125 tok/s vs 65 tok/s.
- Llama 5 leads on SWE-bench coding (74.0% vs 10.5%).
| Feature | Claude 3 Haiku | Llama 5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 33 | 94 |
Provider | Anthropic | Meta |
Input Price Cost per million input tokens | $0.25 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $1.25 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.19 / 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 | 4K | 128K |
Throughput Speed Tokens generated per second | 125 tok/s | 65 tok/s |
SWE-bench Verified Real-world software engineering | 10.5% | 74% |
GPQA Diamond Graduate-level reasoning | 33% | 84% |
Cached Input Price Prompt caching discount per 1M tokens | $0.03 / 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.
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.
Claude 3 Haiku · Strengths & Key Advantages
- 由 Anthropic 研发的高精度 Compact High-Throughput Transformer 架构，支持 200K 上下文窗口
- 极具竞争力的 API 定价（输入 $0.25/1M，输出 $1.25/1M），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 5 · Strengths & Key Advantages
- 由 Meta 研发的高精度 Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active) 架构，支持 5M 上下文窗口
- 极具竞争力的 API 定价（输入 $0/1M，输出 $0/1M），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 | Claude 3 Haiku | Llama 5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.20 | $0.00 |
Output Tokens / Task Tokens generated per task | 24K | 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 Haiku | Llama 5 |
|---|---|---|
| Official API Model ID (SDK) | claude-3-haiku-20240301 | meta-llama/Llama-5 |
| API Protocol | Anthropic Messages API + AWS Bedrock + Vertex AI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2024-03 | 2026-04 |
| Knowledge Cutoff | August 2023 | January 2026 |
| Architecture | Compact High-Throughput 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 | $0.025/1M 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 Haiku vs Llama 5: 常见问题解答 (FAQ)
Which is more intelligent, Claude 3 Haiku or Llama 5?
Llama 5 is more intelligent. It scores 94 compared with 33 on the composite Intelligence Index.
Which model is faster?
Claude 3 Haiku is faster, generating 125 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 $0.19 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Claude 3 Haiku has lower latency, with a time to first token of 0.35s 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 Haiku for 企业级 ai 自动化、代码研发、智能体（agent）工作流以及 anthropic 生态深度集成, or choose Llama 5 for 企业级 ai 自动化、代码研发、智能体（agent）工作流以及 meta 生态深度集成.
常见问题 (FAQ)
Which is cheaper: Claude 3 Haiku or Llama 5?+
Claude 3 Haiku costs $0.25/1M input and $1.25/1M output tokens, while Llama 5 costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: Claude 3 Haiku or Llama 5?+
Claude 3 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