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
IA Agentica e Strumenti AutonomiClaude 3.5 Haiku di Anthropic è un modello di IA progettato per i workflow agentici autonomi, il tool calling e l’automazione multi-step, con finestra di contesto da 200K, velocità di 130 token/s, indice di intelligenza di 36/100 e tariffa
$0.80 / 1M
$4.00 / 1M
200K tokens
40.6%
Llama 5
Modello Base a Pesi ApertiLlama 5 di Meta è un modello di IA progettato per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, con finestra di contesto da 5M, velocità di 65 token/s, indice di intelligenza di 94/100 e tariffa di
$0.00 / 1M
$0.00 / 1M
5M tokens
74%
Differenze Chiave a Colpo d’Occhio
- 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 |
Chi vince in ogni categoria?
At-a-glance decision frameworkPunteggi Benchmark Verificati
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.
Latenza e Tempo di Risposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulatore Interattivo dei Costi Token
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Claude 3.5 Haiku · Strengths & Key Advantages
- Finestra di contesto da 200K con capacità massima di output fino a 8K token (SWE-bench Verified 40.6% · GPQA Diamond 41.6%)
- Throughput elevato di 130 token/secondo con latenza al primo token (TTFT) di soli 380 ms
- Economia API competitiva ($0.8/$4 per 1M di token) con supporto per prompt caching, tool calling e output JSON strutturati
Considerations & Trade-offs
- Modello cloud proprietario chiuso; non consente il download locale dei pesi per l’esecuzione offline
- Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità
Llama 5 · Strengths & Key Advantages
- Finestra di contesto da 5M con capacità massima di output fino a 128K token (SWE-bench Verified 74% · GPQA Diamond 84%)
- Throughput elevato di 65 token/secondo con latenza al primo token (TTFT) di soli 850 ms
- Pesi aperti (600B+ total (~40-80B )) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama
Considerations & Trade-offs
- Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
- Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità
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 |
Specifiche Tecniche
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: Risposte Rapide (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.
Verdetto
Choose Claude 3.5 Haiku for ideale per i workflow agentici autonomi, il tool calling e l’automazione multi-step, sistemi rag aziendali e integrazioni api scalabili., or choose Llama 5 for ideale per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, sistemi rag aziendali e integrazioni api scalabili..
Domande Frequenti (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.
Fonti ufficiali: Anthropic & Meta Official Documentation