Claude 3.7 Sonnet vs GPT-4o
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Claude 3.7 Sonnet by Anthropic and GPT-4o by OpenAI 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, GPT-4o holds a notable pricing advantage for token ingestion. On output generation, GPT-4o 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, GPT-4o 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.
Changez et Comparez les Modèles
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Claude 3.7 Sonnet
Codage et raisonnement de pointeLe modèle de pointe hybride d'Anthropic fournissant des réponses instantanées ou un raisonnement étendu par chaîne de pensée, établissant des records en ingénierie logicielle.
$3.00 / 1M
$15.00 / 1M
200K tokens
70.3%
GPT-4o
Bourreau de travail omnimodalLe modèle de fondation omnimodal phare d'OpenAI intégrant nativement la voix, la vision et le texte avec une vitesse élevée et une fidélité d'appel d'outils exceptionnelle.
$2.50 / 1M
$10.00 / 1M
128K tokens
38.8%
Différences Clés en un Coup d’Œil
- GPT-4o is 1.3x cheaper for API usage (input + output tokens combined).
- Claude 3.7 Sonnet has a larger context window (200K vs 128K tokens).
- GPT-4o is faster at 105 tok/s vs 72 tok/s.
- Claude 3.7 Sonnet leads on SWE-bench coding (70.3% vs 38.8%).
| Caractéristique | Claude 3.7 Sonnet | GPT-4o |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 48 | 41 |
Provider | Anthropic | OpenAI |
Input Price Cost per million input tokens | $3.00 / 1M | $2.50 / 1M |
Output Price Cost per million output tokens | $15.00 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $2.31 / 1M | $2.38 / 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 | 16K |
Throughput Speed Tokens generated per second | 72 tok/s | 105 tok/s |
SWE-bench Verified Real-world software engineering | 70.3% | 38.8% |
GPQA Diamond Graduate-level reasoning | 65% | 53.6% |
Cached Input Price Prompt caching discount per 1M tokens | $0.30 / 1M | $1.25 / 1M |
Qui gagne où ?
At-a-glance decision frameworkScores de Benchmarks Vérifiés
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.
Latence et Temps de Réponse
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulateur Interactif de Coût de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Claude 3.7 Sonnet · Strengths & Key Advantages
- monde-leading SWE-bench Verified codage capacité (70.3%)
- Dynamic raisonnement économique slider (standard vs pensée mode)
- Extremely grand sortie génération (up à 64K tokens par turn)
- 90% discount sur cached entrée prompts ($0.30 par 1M tokens)
Considerations & Trade-offs
- supérieur sortie prix ($15/1M) par rapport à léger utility modèles
- No natif audio entrée (nécessite audio-à-texte preprocessing)
GPT-4o · Strengths & Key Advantages
- natif multimodal audio, vision, et en temps réel voix intégration
- rapide inférence vitesse (105 tokens/sec) et faible latence
- fiable structuré JSON sortie et function calling
- Wide d'entreprise outil écosystème et SDK support
Considerations & Trade-offs
- fenêtre de contexte capped à 128K tokens
- SWE-bench ingénierie logicielle score inférieur que spécialisé raisonnement modèles
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Claude 3.7 Sonnet | GPT-4o |
|---|---|---|
Cost per Task Estimated USD to finish one task | $1.42 | $0.68 |
Output Tokens / Task Tokens generated per task | 41K | 24K |
Reasoning Tokens / Task Thinking tokens burned per task | 22K | 0K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | Claude 3.7 Sonnet | GPT-4o |
|---|---|---|
| Official API Model ID (SDK) | claude-3-7-sonnet-20250201 | gpt-4o |
| API Protocol | Anthropic Messages API + AWS Bedrock + Vertex AI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-02 | 2024-05 |
| Knowledge Cutoff | November 2024 | October 2023 |
| Architecture | Hybrid Reasoning MoE | Omni-Multimodal Dense |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 800,000 – 2,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens | Flat rate ($2.5/1M 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 $1.25/1M (no extra cache-write fee) |
| 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 tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Custom Model Tuning via Amazon Bedrock & Prompt Distillation | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) |
| 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) · Audio (~25–32 tokens/sec) |
| 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 | OpenAI Preparedness Framework Audited & System Card Published |
| 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, audio |
| Output Modalities | text | text, audio |
Claude 3.7 Sonnet vs GPT-4o: Réponses Rapides (FAQ)
Which is more intelligent, Claude 3.7 Sonnet or GPT-4o?
Claude 3.7 Sonnet is more intelligent. It scores 48 compared with 41 on the composite Intelligence Index.
Which model is faster?
GPT-4o is faster, generating 105 tokens per second compared with 72 tokens per second.
Which model is cheaper to run?
Claude 3.7 Sonnet is cheaper at $2.31 per 1M tokens blended, compared with $2.38 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GPT-4o has lower latency, with a time to first token of 0.41s compared with 0.52s.
Which model has a larger context window?
Claude 3.7 Sonnet has a larger context window, supporting 200K tokens compared with 128K tokens.
Verdict
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 GPT-4o if your workflow prioritizes Omni-modal voice applications, conversational agents, and structured enterprise APIs. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Questions fréquentes
How does Claude 3.7 Sonnet compare to GPT-4o 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 GPT-4o costs $2.50 input and $10.00 output. Overall, GPT-4o 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 GPT-4o?+
Claude 3.7 Sonnet supports a context window of 200K tokens (~150k words), while GPT-4o 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 GPT-4o?+
Claude 3.7 Sonnet scores 70.3% on SWE-bench Verified while GPT-4o scores 38.8%. 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 GPT-4o?+
Claude 3.7 Sonnet features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, GPT-4o focuses on standard fast inference.
How does inference speed (tokens per second) compare between Claude 3.7 Sonnet and GPT-4o?+
Claude 3.7 Sonnet generates approximately 72 tokens per second, while GPT-4o delivers 105 tokens per second. In latency-sensitive production environments, GPT-4o 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 GPT-4o to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to GPT-4o 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
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Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: GetInfoUs Independent AI Benchmarks & Official API Specs