GPT-5.1 Codex Max vs Claude Sonnet 5.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
GPT-5.1 Codex Max by OpenAI and Claude Sonnet 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, GPT-5.1 Codex Max holds a notable pricing advantage for token ingestion. On output generation, Claude Sonnet 5.5 is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Claude Sonnet 5.5 provides an expanded memory horizon with 1,000K tokens. In latency-sensitive production environments, Claude Sonnet 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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Select any AI models from our verified benchmark database to generate live side-by-side matrices.
GPT-5.1 Codex Max
Derin Akıl Yürütme ModeliOpenAI tarafından geliştirilen GPT-5.1 Codex Max, 400K token bağlam penceresi ve 1M girdi başına $1.75, 1M çıktı başına $14 API fiyatlandırmasıyla yazılım geliştirme, analiz ve kurumsal otomasyon için tasarlanmıştır.
$1.75 / 1M
$14.00 / 1M
400K tokens
78.2%
Claude Sonnet 5.5
Derin Akıl Yürütme ModeliAnthropic tarafından geliştirilen Claude Sonnet 5.5, 1M token bağlam penceresi ve 1M girdi başına $2.0, 1M çıktı başına $10.0 API fiyatlandırmasıyla yazılım geliştirme, analiz ve kurumsal otomasyon için tasarlanmıştır.
$2.00 / 1M
$10.00 / 1M
1M tokens
82.9%
Bir Bakışta Temel Farklar
- Claude Sonnet 5.5 is 1.3x cheaper for API usage (input + output tokens combined).
- Claude Sonnet 5.5 has a larger context window (1M vs 400K tokens).
- Claude Sonnet 5.5 is faster at 105 tok/s vs 95 tok/s.
- Claude Sonnet 5.5 leads on SWE-bench coding (82.9% vs 78.2%).
| Feature | GPT-5.1 Codex Max | Claude Sonnet 5.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 62 | 78 |
Provider | OpenAI | Anthropic |
Input Price Cost per million input tokens | $1.75 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $14.00 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.87 / 1M | $1.54 / 1M |
Context Window Maximum input + output tokens | 400K tokens~600 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 | 95 tok/s | 105 tok/s |
SWE-bench Verified Real-world software engineering | 78.2% | 82.9% |
GPQA Diamond Graduate-level reasoning | 80% | 79.4% |
Cached Input Price Prompt caching discount per 1M tokens | $0.17 / 1M | $0.20 / 1M |
Hangi Alanda Kim Kazanıyor?
At-a-glance decision frameworkDoğrulanmış Benchmark Puanları
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.
Gecikme Süresi ve Yanıt Hızı
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Gerçek Zamanlı Token Maliyet Simülatörü
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-5.1 Codex Max · Strengths & Key Advantages
- OpenAI imzalı gelişmiş Maximum-capability code-specialised reasoning model mimarisi ve 400K token bağlam kapasitesi
- Dengeli API maliyeti ($1.75/1M girdi, $14/1M çıktı) ve yüksek yanıt hızı
- Kod üretimi, mantıksal çıkarım ve çok dilli görevlerde güçlü kıyaslama (benchmark) sonuçları
Considerations & Trade-offs
- Çok uzun bağlamlı sorgularda token maliyetlerinin dikkatli yönetilmesi gerekir
- Alan bazlı özel iş akışlarında en yüksek verim için doğru istem (prompt) tasarımı önerilir
Claude Sonnet 5.5 · Strengths & Key Advantages
- Anthropic imzalı gelişmiş Adaptive Frontier Multimodal Transformer mimarisi ve 1M token bağlam kapasitesi
- Dengeli API maliyeti ($2.0/1M girdi, $10.0/1M çıktı) ve yüksek yanıt hızı
- Kod üretimi, mantıksal çıkarım ve çok dilli görevlerde güçlü kıyaslama (benchmark) sonuçları
Considerations & Trade-offs
- Çok uzun bağlamlı sorgularda token maliyetlerinin dikkatli yönetilmesi gerekir
- Alan bazlı özel iş akışlarında en yüksek verim için doğru istem (prompt) tasarımı önerilir
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-5.1 Codex Max | Claude Sonnet 5.5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $2.40 | $3.29 |
Output Tokens / Task Tokens generated per task | 62K | 72K |
Reasoning Tokens / Task Thinking tokens burned per task | 36K | 37K |
Teknik Özellikler
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-5.1 Codex Max | Claude Sonnet 5.5 |
|---|---|---|
| Official API Model ID (SDK) | gpt-5-1-codex-max | claude-sonnet-5-5-20260901 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2025-11 | 2026-09 |
| Knowledge Cutoff | October 2024 | June 2026 |
| Architecture | Maximum-capability code-specialised reasoning model | Adaptive Frontier Multimodal 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) | Flat rate ($1.75/1M 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 $0.175/1M (no extra cache-write fee) | $0.2/1M 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 Codex Max vs Claude Sonnet 5.5: Hızlı Yanıtlar (SSS)
Which is more intelligent, GPT-5.1 Codex Max or Claude Sonnet 5.5?
Claude Sonnet 5.5 is more intelligent. It scores 78 compared with 62 on the composite Intelligence Index.
Which model is faster?
Claude Sonnet 5.5 is faster, generating 105 tokens per second compared with 95 tokens per second.
Which model is cheaper to run?
Claude Sonnet 5.5 is cheaper at $1.54 per 1M tokens blended, compared with $1.87 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Claude Sonnet 5.5 has lower latency, with a time to first token of 0.55s compared with 1.70s.
Which model has a larger context window?
Claude Sonnet 5.5 has a larger context window, supporting 1M tokens compared with 400K tokens.
Sonuç
Verdict & Model Selection Recommendation: Choose GPT-5.1 Codex Max if your priority is Enterprise-scale code agents and monorepo work. Its performance in demanding tasks justifies its operational token cost. Choose Claude Sonnet 5.5 if your workflow prioritizes Daily production development, agentic workflows, IDE integrations, and automated code review. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Sıkça Sorulan Sorular (SSS)
How does GPT-5.1 Codex Max compare to Claude Sonnet 5.5 in API token pricing and cost per token?+
GPT-5.1 Codex Max costs $1.75 per 1M input tokens and $14.00 per 1M output tokens, whereas Claude Sonnet 5.5 costs $2.00 input and $10.00 output. Overall, GPT-5.1 Codex Max 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 Codex Max or Claude Sonnet 5.5?+
GPT-5.1 Codex Max supports a context window of 400K tokens (~300k words), while Claude Sonnet 5.5 supports 1,000K tokens (~750k words). Therefore, Claude Sonnet 5.5 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 Codex Max or Claude Sonnet 5.5?+
GPT-5.1 Codex Max scores 78.2% on SWE-bench Verified while Claude Sonnet 5.5 scores 82.9%. For autonomous software engineering and multi-file debugging, Claude Sonnet 5.5 holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between GPT-5.1 Codex Max and Claude Sonnet 5.5?+
GPT-5.1 Codex Max features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Claude Sonnet 5.5 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between GPT-5.1 Codex Max and Claude Sonnet 5.5?+
GPT-5.1 Codex Max generates approximately 95 tokens per second, while Claude Sonnet 5.5 delivers 105 tokens per second. In latency-sensitive production environments, Claude Sonnet 5.5 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining GPT-5.1 Codex Max and Claude Sonnet 5.5 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to GPT-5.1 Codex Max to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Claude Sonnet 5.5. 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.
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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.
Resmi kaynaklar: GetInfoUs Independent AI Benchmarks & Official API Specs