GPT-6 Sol vs Claude Sonnet 5.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
GPT-6 Sol 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, both models offer identical input token rates. On output generation, costs are matched. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical memory horizon with 1,000K tokens. In latency-sensitive production environments, GPT-6 Sol 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.
Выбирайте и сравнивайте любые модели
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
GPT-6 Sol
Модель глубокого рассужденияGPT-6 Sol от OpenAI — передовая модель искусственного интеллекта с контекстным окном 1M токенов и стоимостью API $2.0/1M входящих и $10.0/1M исходящих токенов для программирования, аналитики и автоматизации.
$2.00 / 1M
$10.00 / 1M
1M tokens
82.1%
Claude Sonnet 5.5
Модель глубокого рассужденияClaude Sonnet 5.5 от Anthropic — передовая модель искусственного интеллекта с контекстным окном 1M токенов и стоимостью API $2.0/1M входящих и $10.0/1M исходящих токенов для программирования, аналитики и автоматизации.
$2.00 / 1M
$10.00 / 1M
1M tokens
82.9%
Ключевые отличия кратко
- GPT-6 Sol is 1.0x cheaper for API usage (input + output tokens combined).
- GPT-6 Sol is faster at 116 tok/s vs 105 tok/s.
- Claude Sonnet 5.5 leads on SWE-bench coding (82.9% vs 82.1%).
| Feature | GPT-6 Sol | Claude Sonnet 5.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 93 | 78 |
Provider | OpenAI | Anthropic |
Input Price Cost per million input tokens | $2.00 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $10.00 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.47 / 1M | $1.54 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 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 | 116 tok/s | 105 tok/s |
SWE-bench Verified Real-world software engineering | 82.1% | 82.9% |
GPQA Diamond Graduate-level reasoning | 80.6% | 79.4% |
Cached Input Price Prompt caching discount per 1M tokens | $0.10 / 1M | $0.20 / 1M |
Кто лидирует в каждой категории?
At-a-glance decision frameworkРезультаты проверенных бенчмарков
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.
Задержка и скорость ответа
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Интерактивный калькулятор стоимости токенов
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-6 Sol · Strengths & Key Advantages
- Современная архитектура Cost-efficient frontier unified reasoning model от OpenAI с поддержкой до 1M токенов контекста
- Оптимальная стоимость использования ($2.0/1M вход, $10.0/1M выход) и высокая скорость генерации
- Высокие результаты в задачах написания кода, логического мышления и многоязычной обработки
Considerations & Trade-offs
- При работе с максимальным контекстом требуется контроль расхода токенов
- Для узкоспециализированных задач рекомендуется точная настройка системных промптов
Claude Sonnet 5.5 · Strengths & Key Advantages
- Современная архитектура Adaptive Frontier Multimodal Transformer от Anthropic с поддержкой до 1M токенов контекста
- Оптимальная стоимость использования ($2.0/1M вход, $10.0/1M выход) и высокая скорость генерации
- Высокие результаты в задачах написания кода, логического мышления и многоязычной обработки
Considerations & Trade-offs
- При работе с максимальным контекстом требуется контроль расхода токенов
- Для узкоспециализированных задач рекомендуется точная настройка системных промптов
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-6 Sol | Claude Sonnet 5.5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $2.00 | $3.29 |
Output Tokens / Task Tokens generated per task | 62K | 72K |
Reasoning Tokens / Task Thinking tokens burned per task | 36K | 37K |
Технические характеристики
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-6 Sol | Claude Sonnet 5.5 |
|---|---|---|
| Official API Model ID (SDK) | gpt-6-sol | claude-sonnet-5-5-20260901 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2026-09 | 2026-09 |
| Knowledge Cutoff | April 2026 | June 2026 |
| Architecture | Cost-efficient frontier unified reasoning model | Adaptive Frontier Multimodal 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 ($2/1M in) across full 1000K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.1/1M (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-6 Sol vs Claude Sonnet 5.5: Быстрые ответы (FAQ)
Which is more intelligent, GPT-6 Sol or Claude Sonnet 5.5?
GPT-6 Sol is more intelligent. It scores 93 compared with 78 on the composite Intelligence Index.
Which model is faster?
GPT-6 Sol is faster, generating 116 tokens per second compared with 105 tokens per second.
Which model is cheaper to run?
GPT-6 Sol is cheaper at $1.47 per 1M tokens blended, compared with $1.54 (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 1.16s.
Вердикт
Verdict & Model Selection Recommendation: Choose GPT-6 Sol if your priority is Cost-sensitive frontier work and agentic coding. 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.
Часто задаваемые вопросы (FAQ)
How does GPT-6 Sol compare to Claude Sonnet 5.5 in API token pricing and cost per token?+
Both GPT-6 Sol and Claude Sonnet 5.5 share matched API token pricing at $2.00 per 1M input tokens and $10.00 per 1M output tokens. Because their token economics are identical, your selection should be guided by benchmark latency, memory horizon, and reasoning capabilities rather than operational token fees. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: GPT-6 Sol or Claude Sonnet 5.5?+
Both GPT-6 Sol and Claude Sonnet 5.5 offer an identical context window of 1,000K tokens (~750k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.
Which AI model is better for software engineering and coding: GPT-6 Sol or Claude Sonnet 5.5?+
GPT-6 Sol scores 82.1% 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-6 Sol and Claude Sonnet 5.5?+
GPT-6 Sol 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-6 Sol and Claude Sonnet 5.5?+
GPT-6 Sol generates approximately 116 tokens per second, while Claude Sonnet 5.5 delivers 105 tokens per second. In latency-sensitive production environments, GPT-6 Sol provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining GPT-6 Sol and Claude Sonnet 5.5 to optimize costs?+
Yes! Tiered multi-model routing can optimize for latency and accuracy: you can route high-frequency conversational turns and latency-sensitive queries to GPT-6 Sol, and route deep architectural or complex reasoning queries to GPT-6 Sol. This balanced routing strategy maximizes system throughput and answer quality.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
- Claude Sonnet 5.5 vs DeepSeek V4 Pro: Benchmarks, Pricing & Speed ComparisonСравнение
- Claude Sonnet 5.5 vs Gemini 4 Argon: Benchmarks, Pricing & Speed ComparisonСравнение
- Claude Sonnet 5 vs Claude Sonnet 5.5: Benchmarks, Pricing & Speed ComparisonСравнение
- Devstral 2 vs Claude Sonnet 5.5: Benchmarks, Pricing & Speed ComparisonСравнение
- GPT-5.1 Codex Max vs Claude Sonnet 5.5: Benchmarks, Pricing & Speed ComparisonСравнение
- GPT-6 Sol vs Claude Opus 5.5: Benchmarks, Pricing & Speed ComparisonСравнение
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.
Официальные источники: GetInfoUs Independent AI Benchmarks & Official API Specs