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.
원하는 AI 모델 선택 및 실시간 비교
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Live comparison: no in-depth editorial review has been published for this pair yet.
Claude 3 Haiku
고성능 AI 모델Anthropic의 Claude 3 Haiku 모델은 200K 토큰 컨텍스트 윈도우와 입력 $0.25/1M 토큰, 출력 $1.25/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$0.25 / 1M
$1.25 / 1M
200K tokens
10.5%
Llama 5
오픈 웨이트 모델Meta의 Llama 5 모델은 5M 토큰 컨텍스트 윈도우와 입력 $0/1M 토큰, 출력 $0/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$0.00 / 1M
$0.00 / 1M
5M tokens
74%
한눈에 보는 핵심 차이점
- 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 |
부문별 우위 모델
At-a-glance decision framework검증된 벤치마크 점수 비교
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.
지연 시간(Latency) 및 응답 속도
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
실시간 API 토큰 비용 시뮬레이터
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Claude 3 Haiku · Strengths & Key Advantages
- Anthropic의 첨단 Compact High-Throughput Transformer 아키텍처와 최대 200K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0.25/1M, 출력 $1.25/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
Llama 5 · Strengths & Key Advantages
- Meta의 첨단 Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active) 아키텍처와 최대 5M 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0/1M, 출력 $0/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
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 |
기술 사양 (Specifications)
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: 핵심 요약 문답 (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.
종합 평가
Choose Claude 3 Haiku for 기업 업무 자동화, 소프트웨어 개발, 자율형 ai 에이전트 및 anthropic 기반 프로덕션 파이프라인, or choose Llama 5 for 기업 업무 자동화, 소프트웨어 개발, 자율형 ai 에이전트 및 meta 기반 프로덕션 파이프라인.
자주 묻는 질문 (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.
Related reading
- Aion-3.0 vs Aion-Mini: Benchmarks, Pricing & Speed Comparison비교
- Aion-3.0 vs Gemini 3.6 Flash: Benchmarks, Pricing & Speed Comparison비교
- Aion-Mini vs Laguna XS 2.1: Benchmarks, Pricing & Speed Comparison비교
- Aya Expanse 32B vs Llama 3.2 90B Vision: Benchmarks, Pricing & Speed Comparison비교
- Aya Expanse 8B vs Command R+: Benchmarks, Pricing & Speed Comparison비교
- Aya Expanse 8B vs Qwen 2.5 7B Instruct: Benchmarks, Pricing & Speed Comparison비교
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.
공식 출처: Anthropic & Meta Official Documentation