Claude 3.5 Haiku vs Claude 3 Haiku
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Claude 3.5 Haiku di Anthropic e Claude 3 Haiku di Anthropic sono due modelli di IA di riferimento. Claude 3.5 Haiku offre una finestra di contesto di 200K token a $0.80/$4.00 per milione di token, mentre Claude 3 Haiku mette a disposizione 200K token di contesto a $0.25/$1.25 per milione di token. Di seguito analizziamo benchmark, latenza e costi.
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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%
Claude 3 Haiku
Inferenza Ultra-RapidaClaude 3 Haiku di Anthropic è un modello di IA progettato per le interazioni in tempo reale a bassissima latenza e il routing ad alto volume, con finestra di contesto da 200K, velocità di 125 token/s, indice di intelligenza di 33/100 e tari
$0.25 / 1M
$1.25 / 1M
200K tokens
10.5%
Differenze Chiave a Colpo d’Occhio
- Claude 3 Haiku is 3.2x cheaper for API usage (input + output tokens combined).
- Claude 3.5 Haiku is faster at 130 tok/s vs 125 tok/s.
- Claude 3.5 Haiku leads on SWE-bench coding (40.6% vs 10.5%).
| Feature | Claude 3.5 Haiku | Claude 3 Haiku |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 36 | 33 |
Provider | Anthropic | Anthropic |
Input Price Cost per million input tokens | $0.80 / 1M | $0.25 / 1M |
Output Price Cost per million output tokens | $4.00 / 1M | $1.25 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.62 / 1M | $0.19 / 1M |
Context Window Maximum input + output tokens | 200K tokens~300 A4 pages of text | 200K tokens~300 A4 pages of text |
Max Output Tokens Per single request | 8K | 4K |
Throughput Speed Tokens generated per second | 130 tok/s | 125 tok/s |
SWE-bench Verified Real-world software engineering | 40.6% | 10.5% |
GPQA Diamond Graduate-level reasoning | 41.6% | 33% |
Cached Input Price Prompt caching discount per 1M tokens | $0.08 / 1M | $0.03 / 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
Claude 3 Haiku · Strengths & Key Advantages
- Finestra di contesto da 200K con capacità massima di output fino a 4K token (SWE-bench Verified 10.5% · GPQA Diamond 33%)
- Throughput elevato di 125 token/secondo con latenza al primo token (TTFT) di soli 350 ms
- Economia API competitiva ($0.25/$1.25 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
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Claude 3.5 Haiku | Claude 3 Haiku |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.90 | $0.20 |
Output Tokens / Task Tokens generated per task | 45K | 24K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Specifiche Tecniche
Architecture, release timeline, capabilities, and modalities.
| Feature | Claude 3.5 Haiku | Claude 3 Haiku |
|---|---|---|
| Official API Model ID (SDK) | claude-3-5-haiku-20241101 | claude-3-haiku-20240301 |
| API Protocol | Anthropic Messages API + AWS Bedrock + Vertex AI | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2024-11 | 2024-03 |
| Knowledge Cutoff | July 2024 | August 2023 |
| Architecture | Optimized Lightweight Transformer | Compact High-Throughput Transformer |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 800,000 – 2,000,000 TPM | 4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | $0.08/1M cached read (90% off); 5-min default & 1-hour extended TTL | $0.025/1M cached read (90% off); 5-min default & 1-hour extended TTL |
| 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 | Native Web Search Citations + Code Execution Tool + MCP Connectors |
| Official Fine-Tuning Support | Custom Model Tuning via Amazon Bedrock & Prompt Distillation | Custom Model Tuning via Amazon Bedrock & Prompt Distillation |
| 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) |
| Quantization & Min GPU VRAM | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated | Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text, vision | text, vision |
| Output Modalities | text | text |
Claude 3.5 Haiku vs Claude 3 Haiku: Risposte Rapide (FAQ)
Which is more intelligent, Claude 3.5 Haiku or Claude 3 Haiku?
Claude 3.5 Haiku is more intelligent. It scores 36 compared with 33 on the composite Intelligence Index.
Which model is faster?
Claude 3.5 Haiku is faster, generating 130 tokens per second compared with 125 tokens per second.
Which model is cheaper to run?
Claude 3 Haiku is cheaper at $0.19 per 1M tokens blended, compared with $0.62 (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 0.38s.
Verdetto
Sia Claude 3.5 Haiku (Ideale per i workflow agentici autonomi, il tool calling e l’automazione multi-step, sistemi RAG aziendali e integrazioni API scalabili.) sia Claude 3 Haiku (Ideale per le interazioni in tempo reale a bassissima latenza e il routing ad alto volume, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: Claude 3.5 Haiku guida nei benchmark di ragionamento e programmazione, Claude 3.5 Haiku garantisce una generazione più rapida e Claude 3 Haiku offre il miglior rapporto qualità-prezzo per carichi API elevati.
Domande Frequenti (FAQ)
Quale modello è migliore per coding e ragionamento, Claude 3.5 Haiku o Claude 3 Haiku?+
Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Claude 3.5 Haiku. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.
Qual è la differenza di prezzo API tra Claude 3.5 Haiku e Claude 3 Haiku?+
Claude 3.5 Haiku costa $0.80 / $4.00 per milione di token di input/output, mentre Claude 3 Haiku costa $0.25 / $1.25. Nel costo combinato, Claude 3 Haiku risulta più economico.
Quale modello ha la finestra di contesto più ampia e la velocità maggiore?+
Claude 3.5 Haiku supporta 200K token di contesto contro 200K token di Claude 3 Haiku, mentre Claude 3.5 Haiku offre una velocità di generazione superiore.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
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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: Documentazione API ufficiale di Anthropic e Anthropic, schede di sistema e benchmark indipendenti (2026).