Claude 3.7 Sonnet vs DeepSeek R1
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Claude 3.7 Sonnet by Anthropic and DeepSeek R1 by DeepSeek represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, DeepSeek R1 holds a notable pricing advantage for token ingestion. On output generation, DeepSeek R1 is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Claude 3.7 Sonnet provides an expanded memory horizon with 200K tokens. In latency-sensitive production environments, Claude 3.7 Sonnet leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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Claude 3.7 Sonnet
Модель глубокого рассужденияClaude 3.7 Sonnet от Anthropic — передовая модель искусственного интеллекта с контекстным окном 200K токенов и стоимостью API $3/1M входящих и $15/1M исходящих токенов для программирования, аналитики и автоматизации.
$3.00 / 1M
$15.00 / 1M
200K tokens
70.3%
DeepSeek R1
Открытые веса (Open Weights)DeepSeek R1 от DeepSeek — передовая модель искусственного интеллекта с контекстным окном 128K токенов и стоимостью API $0.55/1M входящих и $2.19/1M исходящих токенов для программирования, аналитики и автоматизации.
$0.55 / 1M
$2.19 / 1M
128K tokens
49.2%
Ключевые отличия кратко
- 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 |
Кто лидирует в каждой категории?
At-a-glance decision frameworkРезультаты проверенных бенчмарков
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.
Задержка и скорость ответа
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Интерактивный калькулятор стоимости токенов
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Claude 3.7 Sonnet · Strengths & Key Advantages
- Современная архитектура Hybrid Reasoning MoE от Anthropic с поддержкой до 200K токенов контекста
- Оптимальная стоимость использования ($3/1M вход, $15/1M выход) и высокая скорость генерации
- Высокие результаты в задачах написания кода, логического мышления и многоязычной обработки
Considerations & Trade-offs
- При работе с максимальным контекстом требуется контроль расхода токенов
- Для узкоспециализированных задач рекомендуется точная настройка системных промптов
DeepSeek R1 · Strengths & Key Advantages
- Современная архитектура Open-Weights Reasoning MoE от DeepSeek с поддержкой до 128K токенов контекста
- Оптимальная стоимость использования ($0.55/1M вход, $2.19/1M выход) и высокая скорость генерации
- Высокие результаты в задачах написания кода, логического мышления и многоязычной обработки
Considerations & Trade-offs
- При работе с максимальным контекстом требуется контроль расхода токенов
- Для узкоспециализированных задач рекомендуется точная настройка системных промптов
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 |
Технические характеристики
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: Быстрые ответы (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.
Вердикт
Verdict & Model Selection Recommendation: Choose Claude 3.7 Sonnet if your priority is Full-stack software architecture, complex refactoring, and agentic workflows. Its performance in demanding tasks justifies its operational token cost. Choose DeepSeek R1 if your workflow prioritizes Competitive coding, mathematical research, and privacy-first on-premise deployments. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Часто задаваемые вопросы (FAQ)
How does Claude 3.7 Sonnet compare to DeepSeek R1 in API token pricing and cost per token?+
Claude 3.7 Sonnet costs $3.00 per 1M input tokens and $15.00 per 1M output tokens, whereas DeepSeek R1 costs $0.55 input and $2.19 output. Overall, DeepSeek R1 is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Claude 3.7 Sonnet or DeepSeek R1?+
Claude 3.7 Sonnet supports a context window of 200K tokens (~150k words), while DeepSeek R1 supports 128K tokens (~96k words). Therefore, Claude 3.7 Sonnet can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Claude 3.7 Sonnet or DeepSeek R1?+
Claude 3.7 Sonnet scores 70.3% on SWE-bench Verified while DeepSeek R1 scores 49.2%. For autonomous software engineering and multi-file debugging, Claude 3.7 Sonnet holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Claude 3.7 Sonnet and DeepSeek R1?+
Claude 3.7 Sonnet features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, DeepSeek R1 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Claude 3.7 Sonnet and DeepSeek R1?+
Claude 3.7 Sonnet generates approximately 72 tokens per second, while DeepSeek R1 delivers 45 tokens per second. In latency-sensitive production environments, Claude 3.7 Sonnet provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Claude 3.7 Sonnet and DeepSeek R1 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to DeepSeek R1 to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Claude 3.7 Sonnet. This tiered routing pattern cuts overall API bills by up to 70%.
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: Benchmarks, Pricing & Speed ComparisonСравнение
- Claude 3.7 Sonnet vs o3-mini: Benchmarks, Pricing & Speed ComparisonСравнение
- Claude Sonnet 4 vs Claude 3.7 Sonnet: Benchmarks, Pricing & Speed ComparisonСравнение
- DeepSeek R1 vs OpenAI o1: Benchmarks, Pricing & Speed ComparisonСравнение
- DeepSeek R1 vs o3-mini: Benchmarks, Pricing & Speed ComparisonСравнение
- Gemini 2.0 Pro vs Claude 3.7 Sonnet: Benchmarks, Pricing & Speed ComparisonСравнение
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.
Официальные источники: GetInfoUs Independent AI Benchmarks & Official API Specs