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.
Cambia y Compara Cualquier Modelo
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 3.5 Haiku
IA Agéntica y Uso de HerramientasClaude 3.5 Haiku de Anthropic es un modelo de IA diseñado para flujos de trabajo agénticos autónomos, llamada a herramientas y automatización compleja, con ventana de contexto de 200K, velocidad de 130 tokens/s, índice de inteligencia de 36
$0.80 / 1M
$4.00 / 1M
200K tokens
40.6%
Llama 5
Modelo Base de Pesos AbiertosLlama 5 de Meta es un modelo de IA diseñado para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, con ventana de contexto de 5M, velocidad de 65 tokens/s, índice de inteligencia de 94/100 y tarifa de ac
$0.00 / 1M
$0.00 / 1M
5M tokens
74%
Diferencias Clave de un Vistazo
- 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%).
| Característica | 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 |
¿Quién gana en cada área?
At-a-glance decision frameworkPuntuaciones de Benchmarks Verificadas
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.
Latencia y Tiempo de Respuesta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interactivo de Costos de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Claude 3.5 Haiku · Strengths & Key Advantages
- Ventana de contexto de 200K con hasta 8K tokens de salida máxima (SWE-bench Verified 40.6% · GPQA Diamond 41.6%)
- Rendimiento ágil de 130 tokens/segundo con latencia inicial (TTFT) de solo 380 ms
- Economía de API competitiva ($0.8/$4 por 1M de tokens) con soporte para caché de prompts y salidas JSON estructuradas
Considerations & Trade-offs
- Modelo propietario en la nube; no permite descargar los pesos para ejecución local sin conexión
- En demostraciones científicas extremas, los modelos de razonamiento pesado de gama alta ofrecen mayor profundidad
Llama 5 · Strengths & Key Advantages
- Ventana de contexto de 5M con hasta 128K tokens de salida máxima (SWE-bench Verified 74% · GPQA Diamond 84%)
- Rendimiento ágil de 65 tokens/segundo con latencia inicial (TTFT) de solo 850 ms
- Pesos abiertos (600B+ total (~40-80B )) listos para autoalojamiento local o en nube privada mediante vLLM y Ollama
Considerations & Trade-offs
- Requiere infraestructura GPU dedicada con suficiente memoria VRAM para inferencia local sin cuantización
- En demostraciones científicas extremas, los modelos de razonamiento pesado de gama alta ofrecen mayor profundidad
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 |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | 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: Respuestas Rápidas (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.
Veredicto
Choose Claude 3.5 Haiku for ideal para flujos de trabajo agénticos autónomos, llamada a herramientas y automatización compleja, sistemas rag empresariales y automatización mediante api., or choose Llama 5 for ideal para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, sistemas rag empresariales y automatización mediante api..
Preguntas frecuentes
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.
Related reading
- Aion-3.0 vs. Aion-Mini: Comparativa de Benchmarks, Precios API y Velocidad (2026)Comparar
- Aion-3.0 vs. Gemini 3.6 Flash: Comparativa de Benchmarks, Precios API y Velocidad (2026)Comparar
- Aion-Mini vs. Laguna XS 2.1: Comparativa de Benchmarks, Precios API y Velocidad (2026)Comparar
- Aya Expanse 32B vs. Llama 3.2 90B Vision: Comparativa de Benchmarks, Precios API y Velocidad (2026)Comparar
- Aya Expanse 8B vs. Command R+: Comparativa de Benchmarks, Precios API y Velocidad (2026)Comparar
- Aya Expanse 8B vs. Qwen 2.5 7B Instruct: Comparativa de Benchmarks, Precios API y Velocidad (2026)Comparar
Las puntuaciones de benchmarks, precios por token y límites de tasa evolucionan rápidamente. Verifique siempre las especificaciones actuales en la documentación oficial del proveedor.
Fuentes: Anthropic & Meta Official Documentation