GPT-6 Luna vs GPT-5.6 Sol
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
GPT-6 Luna by OpenAI and GPT-5.6 Sol by OpenAI 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-6 Luna holds a notable pricing advantage for token ingestion. On output generation, GPT-6 Luna is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, GPT-5.6 Sol provides an expanded memory horizon with 1,050K tokens. In latency-sensitive production environments, GPT-6 Luna 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-6 Luna
深度推理模型OpenAI 推出的 GPT-6 Luna 人工智能模型，具备 272K 上下文窗口，输入价格为 $0.1/百万 Token，输出价格为 $0.5/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$0.10 / 1M
$0.50 / 1M
272K tokens
70.5%
GPT-5.6 Sol
深度推理模型OpenAI 推出的 GPT-5.6 Sol 人工智能模型，具备 1.1M 上下文窗口，输入价格为 $4/百万 Token，输出价格为 $20/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$4.00 / 1M
$20.00 / 1M
1.05M tokens
83.8%
核心差异一览
- GPT-6 Luna is 40.0x cheaper for API usage (input + output tokens combined).
- GPT-5.6 Sol has a larger context window (1M vs 272K tokens).
- GPT-6 Luna is faster at 245 tok/s vs 108 tok/s.
- GPT-5.6 Sol leads on SWE-bench coding (83.8% vs 70.5%).
| Feature | GPT-6 Luna | GPT-5.6 Sol |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 67 | 89 |
Provider | OpenAI | OpenAI |
Input Price Cost per million input tokens | $0.10 / 1M | $4.00 / 1M |
Output Price Cost per million output tokens | $0.50 / 1M | $20.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.08 / 1M | $3.08 / 1M |
Context Window Maximum input + output tokens | 272K tokens~408 A4 pages of text | 1.05M tokens~1,575 A4 pages of text |
Max Output Tokens Per single request | 128K | 128K |
Throughput Speed Tokens generated per second | 245 tok/s | 108 tok/s |
SWE-bench Verified Real-world software engineering | 70.5% | 83.8% |
GPQA Diamond Graduate-level reasoning | 73.5% | 82% |
Cached Input Price Prompt caching discount per 1M tokens | $0.01 / 1M | $0.40 / 1M |
各项指标谁更胜一筹？
At-a-glance decision framework权威基准测试得分对比
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.
延迟与响应速度
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
实时 Token 成本模拟器
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-6 Luna · Strengths & Key Advantages
- 由 OpenAI 研发的高精度 Ultra-budget compact reasoning model 架构，支持 272K 上下文窗口
- 极具竞争力的 API 定价（输入 $0.1/1M，输出 $0.5/1M），支持高速推理与工具调用
- 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
- 超长上下文请求需要规划 Token 成本与并发配额
- 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果
GPT-5.6 Sol · Strengths & Key Advantages
- 由 OpenAI 研发的高精度 Frontier unified reasoning model 架构，支持 1.1M 上下文窗口
- 极具竞争力的 API 定价（输入 $4/1M，输出 $20/1M），支持高速推理与工具调用
- 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
- 超长上下文请求需要规划 Token 成本与并发配额
- 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-6 Luna | GPT-5.6 Sol |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.13 | $3.30 |
Output Tokens / Task Tokens generated per task | 46K | 64K |
Reasoning Tokens / Task Thinking tokens burned per task | 21K | 38K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-6 Luna | GPT-5.6 Sol |
|---|---|---|
| Official API Model ID (SDK) | gpt-6-luna | gpt-5-6-sol |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2026-09 | 2026-07 |
| Knowledge Cutoff | April 2026 | February 2026 |
| Architecture | Ultra-budget compact reasoning model | Frontier unified reasoning model |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.1/1M in) across full 272K window | Flat rate ($4/1M in) across full 1050K window |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.01/1M (no extra cache-write fee) | Automatic prefix caching at $0.4/1M (no extra cache-write fee) |
| 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 tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) |
| 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 | OpenAI Preparedness Framework Audited & System Card Published |
| 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 Luna vs GPT-5.6 Sol: 常见问题解答 (FAQ)
Which is more intelligent, GPT-6 Luna or GPT-5.6 Sol?
GPT-5.6 Sol is more intelligent. It scores 89 compared with 67 on the composite Intelligence Index.
Which model is faster?
GPT-6 Luna is faster, generating 245 tokens per second compared with 108 tokens per second.
Which model is cheaper to run?
GPT-6 Luna is cheaper at $0.08 per 1M tokens blended, compared with $3.08 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GPT-6 Luna has lower latency, with a time to first token of 0.38s compared with 1.21s.
Which model has a larger context window?
GPT-5.6 Sol has a larger context window, supporting 1.05M tokens compared with 272K tokens.
综合结论
Verdict & Model Selection Recommendation: Choose GPT-6 Luna if your priority is Massive-volume extraction, tagging and simple automation. Its performance in demanding tasks justifies its operational token cost. Choose GPT-5.6 Sol if your workflow prioritizes Complex professional work and agentic coding pipelines. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
常见问题 (FAQ)
How does GPT-6 Luna compare to GPT-5.6 Sol in API token pricing and cost per token?+
GPT-6 Luna costs $0.10 per 1M input tokens and $0.50 per 1M output tokens, whereas GPT-5.6 Sol costs $4.00 input and $20.00 output. Overall, GPT-6 Luna 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-6 Luna or GPT-5.6 Sol?+
GPT-6 Luna supports a context window of 272K tokens (~204k words), while GPT-5.6 Sol supports 1,050K tokens (~788k words). Therefore, GPT-5.6 Sol can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: GPT-6 Luna or GPT-5.6 Sol?+
GPT-6 Luna scores 70.5% on SWE-bench Verified while GPT-5.6 Sol scores 83.8%. For autonomous software engineering and multi-file debugging, GPT-5.6 Sol holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between GPT-6 Luna and GPT-5.6 Sol?+
GPT-6 Luna features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, GPT-5.6 Sol provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between GPT-6 Luna and GPT-5.6 Sol?+
GPT-6 Luna generates approximately 245 tokens per second, while GPT-5.6 Sol delivers 108 tokens per second. In latency-sensitive production environments, GPT-6 Luna provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining GPT-6 Luna and GPT-5.6 Sol to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to GPT-6 Luna to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to GPT-5.6 Sol. 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.
数据来源: GetInfoUs Independent AI Benchmarks & Official API Specs