GPT-6 Luna vs GPT-5.6 Sol
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
OpenAI 的 GPT-6 Luna 与 OpenAI 的 GPT-5.6 Sol 是当前备受关注的两款主力 AI 模型。GPT-6 Luna 提供 272K Token 上下文窗口，API 定价为每百万 Token 输入 $0.10 / 输出 $0.50；而 GPT-5.6 Sol 提供 1.05M Token 上下文窗口，定价为每百万 Token 输入 $4.00 / 输出 $20.00。以下为两者的基准测试、延迟与成本全面对比。
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GPT-6 Luna
轻量级高性价比模型OpenAI 推出的 GPT-6 Luna 是一款兼顾极低 API 调用成本与高吞吐效率的轻量级大模型，具备 272K 上下文窗口、245 Token/秒生成速度与 67/100 综合智能指数（每百万 Token $0.1 输入 / $0.5 输出）。
$0.10 / 1M
$0.50 / 1M
272K tokens
70.5%
GPT-5.6 Sol
旗舰前沿大模型OpenAI 推出的 GPT-5.6 Sol 是一款面向复杂企业级推理、软件开发与全场景生产工作流的旗舰前沿大模型，具备 1.1M 上下文窗口、108 Token/秒生成速度与 89/100 综合智能指数（每百万 Token $4 输入 / $20 输出）。
$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
- 提供 272K 上下文窗口与最高 128K 输出容量，核心基准表现优异（SWE-bench Verified 70.5% · GPQA Diamond 73.5%）
- 高达 245 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 380 毫秒
- 极具竞争力的 API 定价（每百万 Token $0.1 输入 / $0.5 输出），原生支持提示词缓存、函数调用与结构化 JSON 输出
Considerations & Trade-offs
- 闭源专有云端模型，不支持下载权重进行离线本地物理隔离部署
- 在超长链路的前沿科研推导任务上，深度略逊于顶配重型旗舰推理模型
GPT-5.6 Sol · Strengths & Key Advantages
- 提供 1.1M 上下文窗口与最高 128K 输出容量，核心基准表现优异（SWE-bench Verified 83.8% · GPQA Diamond 82%）
- 高达 108 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 1210 毫秒
- 极具竞争力的 API 定价（每百万 Token $4 输入 / $20 输出），原生支持提示词缓存、函数调用与结构化 JSON 输出
Considerations & Trade-offs
- 闭源专有云端模型，不支持下载权重进行离线本地物理隔离部署
- 在大规模高频调用场景下，建议结合提示词缓存 (Prompt Caching) 与批量 API 以控制成本
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
综合来看，GPT-6 Luna（适合：兼顾极低 API 调用成本与高吞吐效率的轻量级任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。）与 GPT-5.6 Sol（适合：面向复杂企业级推理、软件开发与全场景生产工作流的旗舰前沿任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。）各具优势：GPT-5.6 Sol 在综合智能与编程基准测试中表现更强，GPT-6 Luna 拥有更快的 Token 生成速度，而 GPT-6 Luna 则在规模化 API 部署中具备更高的成本效益。
常见问题 (FAQ)
在编程和深度推理方面，GPT-6 Luna 和 GPT-5.6 Sol 哪个更强？+
在综合智能指数与编程基准评测中，GPT-5.6 Sol 表现占优。建议结合上方的 SWE-bench Verified 与 GPQA Diamond 跑分根据具体场景选择。
GPT-6 Luna 与 GPT-5.6 Sol 的 API 价格哪个更便宜？+
GPT-6 Luna 的 API 价格为输入 $0.10 / 输出 $0.50（每百万 Token），GPT-5.6 Sol 为输入 $4.00 / 输出 $20.00。综合混合成本来看，GPT-6 Luna 更具性价比。
哪款模型拥有更大的上下文窗口和更快的响应速度？+
GPT-6 Luna 支持 272K Token 上下文窗口，而 GPT-5.6 Sol 支持 1.05M Token。在流式生成速度方面，GPT-6 Luna 的吞吐表现更快。
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: OpenAI 与 OpenAI 官方 API 文档、模型发布说明及独立基准测试报告 (2026)。