OpenAI o1 vs Claude 3.7 Sonnet
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
OpenAI o1 by OpenAI and Claude 3.7 Sonnet by Anthropic 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, Claude 3.7 Sonnet holds a notable pricing advantage for token ingestion. On output generation, Claude 3.7 Sonnet is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical 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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OpenAI o1
Modelo de Raciocínio ProfundoO OpenAI o1 da OpenAI é um modelo de inteligência artificial com janela de contexto de 200K tokens e preço de API de $15/1M na entrada e $60/1M na saída, projetado para programação, raciocínio e automação empresarial.
$15.00 / 1M
$60.00 / 1M
200K tokens
48.9%
Claude 3.7 Sonnet
Modelo de Raciocínio ProfundoO Claude 3.7 Sonnet da Anthropic é um modelo de inteligência artificial com janela de contexto de 200K tokens e preço de API de $3/1M na entrada e $15/1M na saída, projetado para programação, raciocínio e automação empresarial.
$3.00 / 1M
$15.00 / 1M
200K tokens
70.3%
Principais Diferenças em Resumo
- Claude 3.7 Sonnet is 4.8x cheaper for API usage (input + output tokens combined).
- Claude 3.7 Sonnet is faster at 72 tok/s vs 60 tok/s.
- Claude 3.7 Sonnet leads on SWE-bench coding (70.3% vs 48.9%).
| Feature | OpenAI o1 | Claude 3.7 Sonnet |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 55 | 48 |
Provider | OpenAI | Anthropic |
Input Price Cost per million input tokens | $15.00 / 1M | $3.00 / 1M |
Output Price Cost per million output tokens | $60.00 / 1M | $15.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $14.25 / 1M | $2.31 / 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 | 100K | 64K |
Throughput Speed Tokens generated per second | 60 tok/s | 72 tok/s |
SWE-bench Verified Real-world software engineering | 48.9% | 70.3% |
GPQA Diamond Graduate-level reasoning | 77.3% | 65% |
Cached Input Price Prompt caching discount per 1M tokens | $7.50 / 1M | $0.30 / 1M |
Quem vence em cada categoria?
At-a-glance decision frameworkPontuações 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.
Latência e Tempo de Resposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interativo de Custo de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
OpenAI o1 · Strengths & Key Advantages
- Arquitetura Chain-of-Thought Reasoning Foundation avançada da OpenAI com contexto de 200K tokens
- Excelente relação custo-benefício ($15/1M entrada, $60/1M saída) e baixa latência
- Forte desempenho em benchmarks de código, análise lógica e tarefas multilíngues
Considerations & Trade-offs
- Cargas de trabalho com contexto máximo exigem monitoramento de custos de tokens
- Requer engenharia de prompt adequada para fluxos de trabalho altamente específicos
Claude 3.7 Sonnet · Strengths & Key Advantages
- Arquitetura Hybrid Reasoning MoE avançada da Anthropic com contexto de 200K tokens
- Excelente relação custo-benefício ($3/1M entrada, $15/1M saída) e baixa latência
- Forte desempenho em benchmarks de código, análise lógica e tarefas multilíngues
Considerations & Trade-offs
- Cargas de trabalho com contexto máximo exigem monitoramento de custos de tokens
- Requer engenharia de prompt adequada para fluxos de trabalho altamente específicos
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | OpenAI o1 | Claude 3.7 Sonnet |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.20 | $1.42 |
Output Tokens / Task Tokens generated per task | 24K | 41K |
Reasoning Tokens / Task Thinking tokens burned per task | 15K | 22K |
Especificações Técnicas
Architecture, release timeline, capabilities, and modalities.
| Feature | OpenAI o1 | Claude 3.7 Sonnet |
|---|---|---|
| Official API Model ID (SDK) | o1 | claude-3-7-sonnet-20250201 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2024-12 | 2025-02 |
| Knowledge Cutoff | October 2023 | November 2024 |
| Architecture | Chain-of-Thought Reasoning Foundation | Hybrid Reasoning MoE |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 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) | Flat rate ($15/1M in) across full 200K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Automatic prefix caching at $7.5/1M (no extra cache-write fee) | $0.3/1M 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 tool + Hosted Code Interpreter & File Search RAG | Native Web Search Citations + Code Execution Tool + MCP Connectors |
| Official Fine-Tuning Support | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | 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 | OpenAI Preparedness Framework Audited & System Card Published | 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 |
OpenAI o1 vs Claude 3.7 Sonnet: Respostas Rápidas (FAQ)
Which is more intelligent, OpenAI o1 or Claude 3.7 Sonnet?
OpenAI o1 is more intelligent. It scores 55 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 60 tokens per second.
Which model is cheaper to run?
Claude 3.7 Sonnet is cheaper at $2.31 per 1M tokens blended, compared with $14.25 (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 3.20s.
Veredito
Verdict & Model Selection Recommendation: Choose OpenAI o1 if your priority is Doctorate-level scientific research, competitive algorithmic coding, and high-stakes mathematical proofs. Its performance in demanding tasks justifies its operational token cost. Choose Claude 3.7 Sonnet if your workflow prioritizes Full-stack software architecture, complex refactoring, and agentic workflows. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Perguntas Frequentes (FAQ)
How does OpenAI o1 compare to Claude 3.7 Sonnet in API token pricing and cost per token?+
OpenAI o1 costs $15.00 per 1M input tokens and $60.00 per 1M output tokens, whereas Claude 3.7 Sonnet costs $3.00 input and $15.00 output. Overall, Claude 3.7 Sonnet 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: OpenAI o1 or Claude 3.7 Sonnet?+
Both OpenAI o1 and Claude 3.7 Sonnet offer an identical context window of 200K tokens (~150k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.
Which AI model is better for software engineering and coding: OpenAI o1 or Claude 3.7 Sonnet?+
OpenAI o1 scores 48.9% on SWE-bench Verified while Claude 3.7 Sonnet scores 70.3%. 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 OpenAI o1 and Claude 3.7 Sonnet?+
OpenAI o1 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Claude 3.7 Sonnet provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between OpenAI o1 and Claude 3.7 Sonnet?+
OpenAI o1 generates approximately 60 tokens per second, while Claude 3.7 Sonnet delivers 72 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 OpenAI o1 and Claude 3.7 Sonnet to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Claude 3.7 Sonnet to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to OpenAI o1. 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
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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.
Fontes oficiais: GetInfoUs Independent AI Benchmarks & Official API Specs