GPT-5.3 Chat vs Claude Opus 5.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare GPT-5.3 Chat by OpenAI against Claude Opus 5.5 by Anthropic across real-world coding, reasoning, token economics, and latency.
Changez et Comparez les Modèles
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Live comparison: no in-depth editorial review has been published for this pair yet.
GPT-5.3 Chat
Conversationnel 5.3un chat-tuned GPT-5.3 covering conversation, writing et outil flux de travail.
$0.00 / 1M
$0.00 / 1M
128K tokens
77%
Claude Opus 5.5
Titan d'intelligence de pointed'Anthropic pinnacle modèle de pointe conçu pour à long terme agentic ingénierie logicielle, dense multimodal document synthèse, et autonome recherche flux de travail.
$4.00 / 1M
$20.00 / 1M
1M tokens
84.6%
Différences Clés en un Coup d’Œil
- GPT-5.3 Chat is 1.0x cheaper for API usage (input + output tokens combined).
- Claude Opus 5.5 has a larger context window (1M vs 128K tokens).
- GPT-5.3 Chat is faster at 138 tok/s vs 78 tok/s.
- Claude Opus 5.5 leads on SWE-bench coding (84.6% vs 77.0%).
| Caractéristique | GPT-5.3 Chat | Claude Opus 5.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 69 | 94 |
Provider | OpenAI | Anthropic |
Input Price Cost per million input tokens | $0.00 / 1M | $4.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $20.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $3.08 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 16K | 128K |
Throughput Speed Tokens generated per second | 138 tok/s | 78 tok/s |
SWE-bench Verified Real-world software engineering | 77% | 84.6% |
GPQA Diamond Graduate-level reasoning | 78% | 82.3% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.40 / 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.
GPT-5.3 Chat · Strengths & Key Advantages
- Refined conversationnel ton et instruction following
- bon à au format long writing et editing
- très bon à structuré au format long writing
Considerations & Trade-offs
- Subscription-seulement, no public API tarification
- fenêtre de contexte well below le développeur palier
Claude Opus 5.5 · Strengths & Key Advantages
- inégalé autonome multi-étapes codage agentique et refactorisation
- Flawless multidocument raisonnement sur 1M token contexte avec no long-prompt surcharge
- le plus élevé Artificial analyse intelligence Index ever measured (58/100), leading SWE-bench Pro à 89.9%
- Precise outil exécution avec robust error-recovery boucles
Considerations & Trade-offs
- Premium sortie tarification ($20.00/M) plus heavy raisonnement-token usage (~119K sortie tokens par index tâche) keeps long agentic fonctionne expensive
- long pensée times à maximum effort peut push un unique hard agentic tâche toward ~$13
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-5.3 Chat | Claude Opus 5.5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $2.20 | $3.85 |
Output Tokens / Task Tokens generated per task | 56K | 68K |
Reasoning Tokens / Task Thinking tokens burned per task | 31K | 42K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | GPT-5.3 Chat | Claude Opus 5.5 |
|---|---|---|
| Official API Model ID (SDK) | gpt-5-3-chat | claude-opus-5-5-20260901 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2026-03 | 2026-09 |
| Knowledge Cutoff | November 2025 | June 2026 |
| Architecture | Chat-tuned unified reasoning model | Extended Autonomous Hybrid Reasoning Transformer |
| 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) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Free across full context window (only KV-cache VRAM scales) | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | $0.4/1M 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 | 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 |
GPT-5.3 Chat vs Claude Opus 5.5: Réponses Rapides (FAQ)
Which is more intelligent, GPT-5.3 Chat or Claude Opus 5.5?
Claude Opus 5.5 is more intelligent. It scores 94 compared with 69 on the composite Intelligence Index.
Which model is faster?
GPT-5.3 Chat is faster, generating 138 tokens per second compared with 78 tokens per second.
Which model is cheaper to run?
GPT-5.3 Chat is cheaper at $0.00 per 1M tokens blended, compared with $3.08 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GPT-5.3 Chat has lower latency, with a time to first token of 1.06s compared with 1.40s.
Which model has a larger context window?
Claude Opus 5.5 has a larger context window, supporting 1M tokens compared with 128K tokens.
Verdict
Choose GPT-5.3 Chat for conversationnel et writing travail dans chatgpt, or choose Claude Opus 5.5 for grand-échelle logiciel development agents, d'entreprise codebases, et mission critique scientifique synthèse.
Questions fréquentes
Which is cheaper: GPT-5.3 Chat or Claude Opus 5.5?+
GPT-5.3 Chat costs $0/1M input and $0/1M output tokens, while Claude Opus 5.5 costs $4/1M input and $20/1M output tokens.
Which model has a larger context window: GPT-5.3 Chat or Claude Opus 5.5?+
GPT-5.3 Chat supports 128K tokens whereas Claude Opus 5.5 supports 1M tokens.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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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: OpenAI & Anthropic Official Documentation