GPT-5.1 vs Claude Opus 4.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
GPT-5.1 by OpenAI and Claude Opus 4.5 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, GPT-5.1 holds a notable pricing advantage for token ingestion. On output generation, GPT-5.1 is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, GPT-5.1 provides an expanded memory horizon with 400K tokens. In latency-sensitive production environments, GPT-5.1 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-5.1
Deep-Reasoning-ModellGPT-5.1 von OpenAI ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 400K Token zu API-Preisen von $1.25/1M Input- und $10/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$1.25 / 1M
$10.00 / 1M
400K tokens
74.9%
Claude Opus 4.5
Deep-Reasoning-ModellClaude Opus 4.5 von Anthropic ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 200K Token zu API-Preisen von $5/1M Input- und $25/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$5.00 / 1M
$25.00 / 1M
200K tokens
77.2%
Wichtigste Unterschiede auf einen Blick
- GPT-5.1 is 2.8x cheaper for API usage (input + output tokens combined).
- GPT-5.1 has a larger context window (400K vs 200K tokens).
- GPT-5.1 is faster at 130 tok/s vs 62 tok/s.
- Claude Opus 4.5 leads on SWE-bench coding (77.2% vs 74.9%).
| Feature | GPT-5.1 | Claude Opus 4.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 60 | 63 |
Provider | OpenAI | Anthropic |
Input Price Cost per million input tokens | $1.25 / 1M | $5.00 / 1M |
Output Price Cost per million output tokens | $10.00 / 1M | $25.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.34 / 1M | $3.85 / 1M |
Context Window Maximum input + output tokens | 400K tokens~600 A4 pages of text | 200K tokens~300 A4 pages of text |
Max Output Tokens Per single request | 128K | 64K |
Throughput Speed Tokens generated per second | 130 tok/s | 62 tok/s |
SWE-bench Verified Real-world software engineering | 74.9% | 77.2% |
GPQA Diamond Graduate-level reasoning | 72.1% | 74.8% |
Cached Input Price Prompt caching discount per 1M tokens | $0.13 / 1M | $0.50 / 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-5.1 · Strengths & Key Advantages
- Fortschrittliche Unified Reasoning (Instant & Thinking)-Architektur von OpenAI mit 400K Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($1.25/1M Input, $10/1M 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 4.5 · Strengths & Key Advantages
- Fortschrittliche Hybrid Reasoning-Architektur von Anthropic mit 200K Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($5/1M Input, $25/1M 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-5.1 | Claude Opus 4.5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $1.45 | $5.86 |
Output Tokens / Task Tokens generated per task | 52K | 73K |
Reasoning Tokens / Task Thinking tokens burned per task | 28K | 43K |
Technische Spezifikationen
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-5.1 | Claude Opus 4.5 |
|---|---|---|
| Official API Model ID (SDK) | gpt-5-1 | claude-opus-4-5-20251101 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2025-11 | 2025-11 |
| Knowledge Cutoff | October 2024 | March 2025 |
| Architecture | Unified Reasoning (Instant & Thinking) | Hybrid Reasoning |
| 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 ($1.25/1M in) across full 400K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.125/1M (no extra cache-write fee) | $0.5/1M 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-5.1 vs Claude Opus 4.5: Schnelle Antworten (FAQ)
Which is more intelligent, GPT-5.1 or Claude Opus 4.5?
Claude Opus 4.5 is more intelligent. It scores 63 compared with 60 on the composite Intelligence Index.
Which model is faster?
GPT-5.1 is faster, generating 130 tokens per second compared with 62 tokens per second.
Which model is cheaper to run?
GPT-5.1 is cheaper at $1.34 per 1M tokens blended, compared with $3.85 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GPT-5.1 has lower latency, with a time to first token of 1.20s compared with 2.10s.
Which model has a larger context window?
GPT-5.1 has a larger context window, supporting 400K tokens compared with 200K tokens.
Fazit
Verdict & Model Selection Recommendation: Choose GPT-5.1 if your priority is General-purpose production APIs and teams needing balanced quality, speed, and cost. Its performance in demanding tasks justifies its operational token cost. Choose Claude Opus 4.5 if your workflow prioritizes Complex reasoning, creative work, and high-stakes decision making where quality outweighs cost. 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-5.1 compare to Claude Opus 4.5 in API token pricing and cost per token?+
GPT-5.1 costs $1.25 per 1M input tokens and $10.00 per 1M output tokens, whereas Claude Opus 4.5 costs $5.00 input and $25.00 output. Overall, GPT-5.1 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-5.1 or Claude Opus 4.5?+
GPT-5.1 supports a context window of 400K tokens (~300k words), while Claude Opus 4.5 supports 200K tokens (~150k words). Therefore, GPT-5.1 can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: GPT-5.1 or Claude Opus 4.5?+
GPT-5.1 scores 74.9% on SWE-bench Verified while Claude Opus 4.5 scores 77.2%. For autonomous software engineering and multi-file debugging, Claude Opus 4.5 holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between GPT-5.1 and Claude Opus 4.5?+
GPT-5.1 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Claude Opus 4.5 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between GPT-5.1 and Claude Opus 4.5?+
GPT-5.1 generates approximately 130 tokens per second, while Claude Opus 4.5 delivers 62 tokens per second. In latency-sensitive production environments, GPT-5.1 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining GPT-5.1 and Claude Opus 4.5 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to GPT-5.1 to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Claude Opus 4.5. 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
- Claude Sonnet 4.6 vs Claude Opus 4.5: Benchmarks, Pricing & Speed ComparisonVergleich
- Gemini 3 Pro vs GPT-5.1: Benchmarks, Pricing & Speed ComparisonVergleich
- GPT-5.2 Chat vs Claude Opus 4.5: Benchmarks, Pricing & Speed ComparisonVergleich
- GPT-5.2 Chat vs GPT-5.1: Benchmarks, Pricing & Speed ComparisonVergleich
- GPT-5.2 vs GPT-5.1: Benchmarks, Pricing & Speed ComparisonVergleich
- GPT-5 vs GPT-5.1: Benchmarks, Pricing & Speed ComparisonVergleich
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