Head-to-Head Model Comparison

OpenAI o1 vs Claude 3.7 Sonnet

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Verificato dal team di ricerca di GetInfoUsUltima verifica: October 2026Politica editoriale

Cambia e Confronta Qualsiasi Modello

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

OpenAI o1Profile

Claude 3.7 SonnetProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

OpenAI

OpenAI o1

Motore di Ragionamento Profondo

OpenAI o1 di OpenAI è un modello di IA progettato per il ragionamento logico profondo, la matematica avanzata e l’analisi scientifica, con finestra di contesto da 200K, velocità di 60 token/s, indice di intelligenza di 55/100 e tariffa di $

Intelligence Index55 / 100

Input Token:

$15.00 / 1M

Output Token:

$60.00 / 1M

Context Window:

200K tokens

SWE-bench Verified:

48.9%

Anthropic

Claude 3.7 Sonnet

Leader in Programmazione e Codice

Claude 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

Intelligence Index48 / 100

Input Token:

$3.00 / 1M

Output Token:

$15.00 / 1M

Context Window:

200K tokens

SWE-bench Verified:

70.3%

Differenze Chiave a Colpo d’Occhio

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%).

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

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

Chi vince in ogni categoria?

At-a-glance decision framework

Coding

Claude 3.7 Sonnet

+21.4% SWE-bench lead

Cost

Claude 3.7 Sonnet

4.8x cheaper monthly runtime

Speed

Claude 3.7 Sonnet

+20% faster generation

Context

Tied

200K tokens window

Punteggi Benchmark Verificati

Official evaluation metrics verified by independent third-party research labs.

OpenAI o1Claude 3.7 Sonnet

SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

48.9%vs70.3%

GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

77.3%vs65%

MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

89.6%vs78.2%

LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1355ptsvs1335pts

Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

10.8%vs8.9%

Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

43.5%vs35.2%

SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

44.2%vs40.1%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

72.1%vs64.8%

AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

83.3%vs61.3%

Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

75%vs71.5%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

60 tok/s vs72 tok/s

Latenza e Tempo di Risposta

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better

OpenAI o13.20s

Claude 3.7 Sonnet0.52s

Est. End-to-End Response (500 tokens)TTFT + generation time

OpenAI o111.53s

Claude 3.7 Sonnet7.46s

Simulatore Interattivo dei Costi Token

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% cached

0% (Cold)50%90% (Warm)

OpenAI o1 Monthly API Bill

$87.75 / month

$15/1M in · $60/1M out

Claude 3.7 Sonnet Monthly API Bill

$18.45 / month

$3/1M in · $15/1M out

Claude 3.7 Sonnet saves approximately $69.30 / month (79%) under this workload.4.8x Savings

OpenAI o1 · Strengths & Key Advantages

Finestra di contesto da 200K con capacità massima di output fino a 100K token (SWE-bench Verified 48.9% · GPQA Diamond 77.3%)
Throughput elevato di 60 token/secondo con latenza al primo token (TTFT) di soli 3200 ms
Economia API competitiva ($15/$60 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

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

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

Specifiche Tecniche

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: Risposte Rapide (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.

Verdetto

Sia OpenAI o1 (Ideale per il ragionamento logico profondo, la matematica avanzata e l’analisi scientifica, sistemi RAG aziendali e integrazioni API scalabili.) 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.) eccellono in produzione: OpenAI o1 guida nei benchmark di ragionamento e programmazione, Claude 3.7 Sonnet garantisce una generazione più rapida e Claude 3.7 Sonnet offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, OpenAI o1 o Claude 3.7 Sonnet?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale OpenAI o1. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra OpenAI o1 e Claude 3.7 Sonnet?+

OpenAI o1 costa $15.00 / $60.00 per milione di token di input/output, mentre Claude 3.7 Sonnet costa $3.00 / $15.00. Nel costo combinato, Claude 3.7 Sonnet risulta più economico.

Quale modello ha la finestra di contesto più ampia e la velocità maggiore?+

OpenAI o1 supporta 200K token di contesto contro 200K token di Claude 3.7 Sonnet, mentre Claude 3.7 Sonnet offre una velocità di generazione superiore.

AI Terminology & Guide

Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

OpenAI o1 vs Claude 3.7 Sonnet

Cambia e Confronta Qualsiasi Modello

OpenAI o1

Claude 3.7 Sonnet

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

OpenAI o1 · Strengths & Key Advantages

Considerations & Trade-offs

Claude 3.7 Sonnet · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

OpenAI o1 vs Claude 3.7 Sonnet: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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