GPT-5.5 Pro vs Claude Opus 5.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
GPT-5.5 Pro by OpenAI and Claude Opus 5.5 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 Opus 5.5 holds a notable pricing advantage for token ingestion. On output generation, Claude Opus 5.5 is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, GPT-5.5 Pro provides an expanded memory horizon with 1,050K tokens. In latency-sensitive production environments, Claude Opus 5.5 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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GPT-5.5 Pro
Deep-Reasoning-ModellGPT-5.5 Pro von OpenAI ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 1.1M Token zu API-Preisen von $50/1M Input- und $300/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$50.00 / 1M
$300.00 / 1M
1.05M tokens
82.6%
Claude Opus 5.5
Deep-Reasoning-ModellClaude Opus 5.5 von Anthropic ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 1M Token zu API-Preisen von $4.0/1M Input- und $20.0/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$4.00 / 1M
$20.00 / 1M
1M tokens
84.6%
Wichtigste Unterschiede auf einen Blick
- Claude Opus 5.5 is 14.3x cheaper for API usage (input + output tokens combined).
- GPT-5.5 Pro has a larger context window (1M vs 1M tokens).
- Claude Opus 5.5 is faster at 78 tok/s vs 42 tok/s.
- Claude Opus 5.5 leads on SWE-bench coding (84.6% vs 82.6%).
| Feature | GPT-5.5 Pro | Claude Opus 5.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 87 | 94 |
Provider | OpenAI | Anthropic |
Input Price Cost per million input tokens | $50.00 / 1M | $4.00 / 1M |
Output Price Cost per million output tokens | $300.00 / 1M | $20.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $43.50 / 1M | $3.08 / 1M |
Context Window Maximum input + output tokens | 1.05M tokens~1,575 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 128K | 128K |
Throughput Speed Tokens generated per second | 42 tok/s | 78 tok/s |
SWE-bench Verified Real-world software engineering | 82.6% | 84.6% |
GPQA Diamond Graduate-level reasoning | 81.2% | 82.3% |
Cached Input Price Prompt caching discount per 1M tokens | $5.00 / 1M | $0.40 / 1M |
Wer gewinnt in welcher Disziplin?
At-a-glance decision frameworkGeprüfte Benchmark-Ergebnisse im Vergleich
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.
Latenz & Antwortzeit
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Interaktiver Token-Kosten-Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-5.5 Pro · Strengths & Key Advantages
- Fortschrittliche Maximum-accuracy unified reasoning model-Architektur von OpenAI mit 1.1M Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($50/1M Input, $300/1M Output) bei hoher Durchsatzrate
- Hohe Präzision bei Code-Generierung, logischem Schließen und mehrsprachigen Benchmarks
Considerations & Trade-offs
- Bei sehr großen Kontextmengen ist eine sorgfältige Token-Budgetierung ratsam
- Spezialisierte Fachaufgaben profitieren von gezieltem Prompt-Engineering
Claude Opus 5.5 · Strengths & Key Advantages
- Fortschrittliche Extended Autonomous Hybrid Reasoning Transformer-Architektur von Anthropic mit 1M Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($4.0/1M Input, $20.0/1M Output) bei hoher Durchsatzrate
- Hohe Präzision bei Code-Generierung, logischem Schließen und mehrsprachigen Benchmarks
Considerations & Trade-offs
- Bei sehr großen Kontextmengen ist eine sorgfältige Token-Budgetierung ratsam
- Spezialisierte Fachaufgaben profitieren von gezieltem Prompt-Engineering
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-5.5 Pro | Claude Opus 5.5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $66.50 | $3.85 |
Output Tokens / Task Tokens generated per task | 78K | 68K |
Reasoning Tokens / Task Thinking tokens burned per task | 52K | 42K |
Technische Spezifikationen
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-5.5 Pro | Claude Opus 5.5 |
|---|---|---|
| Official API Model ID (SDK) | gpt-5-5-pro | claude-opus-5-5-20260901 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2026-04 | 2026-09 |
| Knowledge Cutoff | December 2025 | June 2026 |
| Architecture | Maximum-accuracy unified reasoning model | Extended Autonomous Hybrid Reasoning Transformer |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM | 4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($50/1M in) across full 1050K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Automatic prefix caching at $5/1M (no extra cache-write fee) | $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 · 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 |
GPT-5.5 Pro vs Claude Opus 5.5: Schnelle Antworten (FAQ)
Which is more intelligent, GPT-5.5 Pro or Claude Opus 5.5?
Claude Opus 5.5 is more intelligent. It scores 94 compared with 87 on the composite Intelligence Index.
Which model is faster?
Claude Opus 5.5 is faster, generating 78 tokens per second compared with 42 tokens per second.
Which model is cheaper to run?
Claude Opus 5.5 is cheaper at $3.08 per 1M tokens blended, compared with $43.50 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Claude Opus 5.5 has lower latency, with a time to first token of 1.40s compared with 4.80s.
Which model has a larger context window?
GPT-5.5 Pro has a larger context window, supporting 1.05M tokens compared with 1M tokens.
Fazit
Verdict & Model Selection Recommendation: Choose GPT-5.5 Pro if your priority is Final-pass verification on the highest-stakes deliverables. Its performance in demanding tasks justifies its operational token cost. Choose Claude Opus 5.5 if your workflow prioritizes Large-scale software development agents, enterprise codebases, and mission-critical scientific synthesis. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Häufig gestellte Fragen (FAQ)
How does GPT-5.5 Pro compare to Claude Opus 5.5 in API token pricing and cost per token?+
GPT-5.5 Pro costs $50.00 per 1M input tokens and $300.00 per 1M output tokens, whereas Claude Opus 5.5 costs $4.00 input and $20.00 output. Overall, Claude Opus 5.5 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: GPT-5.5 Pro or Claude Opus 5.5?+
GPT-5.5 Pro supports a context window of 1,050K tokens (~788k words), while Claude Opus 5.5 supports 1,000K tokens (~750k words). Therefore, GPT-5.5 Pro can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: GPT-5.5 Pro or Claude Opus 5.5?+
GPT-5.5 Pro scores 82.6% on SWE-bench Verified while Claude Opus 5.5 scores 84.6%. For autonomous software engineering and multi-file debugging, Claude Opus 5.5 holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between GPT-5.5 Pro and Claude Opus 5.5?+
GPT-5.5 Pro features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Claude Opus 5.5 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between GPT-5.5 Pro and Claude Opus 5.5?+
GPT-5.5 Pro generates approximately 42 tokens per second, while Claude Opus 5.5 delivers 78 tokens per second. In latency-sensitive production environments, Claude Opus 5.5 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining GPT-5.5 Pro and Claude Opus 5.5 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Claude Opus 5.5 to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to GPT-5.5 Pro. 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.
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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.
Offizielle Quellen: GetInfoUs Independent AI Benchmarks & Official API Specs