Claude 3.7 Sonnet vs DeepSeek R1
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Claude 3.7 Sonnet di Anthropic e DeepSeek R1 di DeepSeek sono due modelli di IA di riferimento. Claude 3.7 Sonnet offre una finestra di contesto di 200K token a $3.00/$15.00 per milione di token, mentre DeepSeek R1 mette a disposizione 128K token di contesto a $0.55/$2.19 per milione di token. Di seguito analizziamo benchmark, latenza e costi.
Cambia e Confronta Qualsiasi Modello
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Claude 3.7 Sonnet
Leader in Programmazione e CodiceClaude 3.7 Sonnet di Anthropic è un modello di IA progettato per l’ingegneria del software autonoma, il refactoring multi-file e il debug avanzato, con finestra di contesto da 200K, velocità di 72 token/s, indice di intelligenza di 48/100 e
$3.00 / 1M
$15.00 / 1M
200K tokens
70.3%
DeepSeek R1
Ragionamento a Pesi ApertiDeepSeek R1 di DeepSeek è un modello di IA progettato per il ragionamento Chain-of-Thought a pesi aperti ad alta efficienza algoritmica, con finestra di contesto da 128K, velocità di 45 token/s, indice di intelligenza di 49/100 e tariffa di
$0.55 / 1M
$2.19 / 1M
128K tokens
49.2%
Differenze Chiave a Colpo d’Occhio
- DeepSeek R1 is 6.1x cheaper for API usage (input + output tokens combined).
- Claude 3.7 Sonnet has a larger context window (200K vs 128K tokens).
- Claude 3.7 Sonnet is faster at 72 tok/s vs 45 tok/s.
- Claude 3.7 Sonnet leads on SWE-bench coding (70.3% vs 49.2%).
| Feature | Claude 3.7 Sonnet | DeepSeek R1 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 48 | 49 |
Provider | Anthropic | DeepSeek |
Input Price Cost per million input tokens | $3.00 / 1M | $0.55 / 1M |
Output Price Cost per million output tokens | $15.00 / 1M | $2.19 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $2.31 / 1M | $0.43 / 1M |
Context Window Maximum input + output tokens | 200K tokens~300 A4 pages of text | 128K tokens~192 A4 pages of text |
Max Output Tokens Per single request | 64K | 8K |
Throughput Speed Tokens generated per second | 72 tok/s | 45 tok/s |
SWE-bench Verified Real-world software engineering | 70.3% | 49.2% |
GPQA Diamond Graduate-level reasoning | 65% | 71.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.30 / 1M | $0.14 / 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 3.7 Sonnet · Strengths & Key Advantages
- Finestra di contesto da 200K con capacità massima di output fino a 64K token (SWE-bench Verified 70.3% · GPQA Diamond 65%)
- Throughput elevato di 72 token/secondo con latenza al primo token (TTFT) di soli 520 ms
- Economia API competitiva ($3/$15 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
- Per volumi di traffico elevati in produzione si consiglia di attivare il prompt caching e la Batch API
DeepSeek R1 · Strengths & Key Advantages
- Finestra di contesto da 128K con capacità massima di output fino a 8K token (SWE-bench Verified 49.2% · GPQA Diamond 71.5%)
- Throughput elevato di 45 token/secondo con latenza al primo token (TTFT) di soli 950 ms
- Pesi aperti (671B Total (37B )) 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 3.7 Sonnet | DeepSeek R1 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $1.42 | $0.65 |
Output Tokens / Task Tokens generated per task | 41K | 45K |
Reasoning Tokens / Task Thinking tokens burned per task | 22K | 25K |
Specifiche Tecniche
Architecture, release timeline, capabilities, and modalities.
| Feature | Claude 3.7 Sonnet | DeepSeek R1 |
|---|---|---|
| Official API Model ID (SDK) | claude-3-7-sonnet-20250201 | deepseek-reasoner |
| API Protocol | Anthropic Messages API + AWS Bedrock + Vertex AI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-02 | 2025-01 |
| Knowledge Cutoff | November 2024 | July 2024 |
| Architecture | Hybrid Reasoning MoE | Open-Weights Reasoning MoE |
| Parameters | Confidential | 671B Total (37B 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 | Flat rate ($0.55/1M in) across full 128K window |
| Prompt Cache Policy & TTL | $0.3/1M cached read (90% off); 5-min default & 1-hour extended TTL | Automatic prefix caching at $0.14/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) |
| 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) |
| 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) · 416 GB VRAM (4-bit Quantized) · 1443 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 |
| Output Modalities | text | text |
Claude 3.7 Sonnet vs DeepSeek R1: Risposte Rapide (FAQ)
Which is more intelligent, Claude 3.7 Sonnet or DeepSeek R1?
DeepSeek R1 is more intelligent. It scores 49 compared with 48 on the composite Intelligence Index.
Which model is faster?
Claude 3.7 Sonnet is faster, generating 72 tokens per second compared with 45 tokens per second.
Which model is cheaper to run?
DeepSeek R1 is cheaper at $0.43 per 1M tokens blended, compared with $2.31 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Claude 3.7 Sonnet has lower latency, with a time to first token of 0.52s compared with 0.95s.
Which model has a larger context window?
Claude 3.7 Sonnet has a larger context window, supporting 200K tokens compared with 128K tokens.
Verdetto
Sia Claude 3.7 Sonnet (Ideale per l’ingegneria del software autonoma, il refactoring multi-file e il debug avanzato, sistemi RAG aziendali e integrazioni API scalabili.) sia DeepSeek R1 (Ideale per il ragionamento Chain-of-Thought a pesi aperti ad alta efficienza algoritmica, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: DeepSeek R1 guida nei benchmark di ragionamento e programmazione, Claude 3.7 Sonnet garantisce una generazione più rapida e DeepSeek R1 offre il miglior rapporto qualità-prezzo per carichi API elevati.
Domande Frequenti (FAQ)
Quale modello è migliore per coding e ragionamento, Claude 3.7 Sonnet o DeepSeek R1?+
Nell’indice di intelligenza composito e nei benchmark di programmazione prevale DeepSeek R1. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.
Qual è la differenza di prezzo API tra Claude 3.7 Sonnet e DeepSeek R1?+
Claude 3.7 Sonnet costa $3.00 / $15.00 per milione di token di input/output, mentre DeepSeek R1 costa $0.55 / $2.19. Nel costo combinato, DeepSeek R1 risulta più economico.
Quale modello ha la finestra di contesto più ampia e la velocità maggiore?+
Claude 3.7 Sonnet supporta 200K token di contesto contro 128K token di DeepSeek R1, mentre Claude 3.7 Sonnet 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
- Claude 3.7 Sonnet vs GPT-4o: Confronto Benchmark, Prezzi API e Velocità (2026)Confronta
- Claude 3.7 Sonnet vs o3-mini: Confronto Benchmark, Prezzi API e Velocità (2026)Confronta
- Claude Sonnet 4 vs Claude 3.7 Sonnet: Confronto Benchmark, Prezzi API e Velocità (2026)Confronta
- DeepSeek R1 vs OpenAI o1: Confronto Benchmark, Prezzi API e Velocità (2026)Confronta
- DeepSeek R1 vs o3-mini: Confronto Benchmark, Prezzi API e Velocità (2026)Confronta
- Gemini 2.0 Pro vs Claude 3.7 Sonnet: Confronto Benchmark, Prezzi API e Velocità (2026)Confronta
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 DeepSeek, schede di sistema e benchmark indipendenti (2026).