GPT-6 Luna vs Kimi K3
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare GPT-6 Luna by OpenAI against Kimi K3 by Moonshot AI across real-world coding, reasoning, token economics, and latency.
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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%
Kimi K3
开源权重推理大模型Moonshot AI 推出的 Kimi K3 是一款通过大规模强化学习训练、支持私有化部署的开源权重推理大模型，具备 1.0M 上下文窗口、58 Token/秒生成速度与 93/100 综合智能指数（免费开源权重）。
$0.00 / 1M
$0.00 / 1M
1.05M tokens
74.5%
核心差异一览
- Kimi K3 is 1.0x cheaper for API usage (input + output tokens combined).
- Kimi K3 has a larger context window (1M vs 272K tokens).
- GPT-6 Luna is faster at 245 tok/s vs 58 tok/s.
- Kimi K3 leads on SWE-bench coding (74.5% vs 70.5%).
| Feature | GPT-6 Luna | Kimi K3 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 67 | 93 |
Provider | OpenAI | Moonshot AI |
Input Price Cost per million input tokens | $0.10 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $0.50 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.08 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 272K tokens~408 A4 pages of text | 1.05M tokens~1,573 A4 pages of text |
Max Output Tokens Per single request | 128K | 131K |
Throughput Speed Tokens generated per second | 245 tok/s | 58 tok/s |
SWE-bench Verified Real-world software engineering | 70.5% | 74.5% |
GPQA Diamond Graduate-level reasoning | 73.5% | 79% |
Cached Input Price Prompt caching discount per 1M tokens | $0.01 / 1M | $0.00 / 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
Kimi K3 · Strengths & Key Advantages
- 提供 1.0M 上下文窗口与最高 131K 输出容量，核心基准表现优异（SWE-bench Verified 74.5% · GPQA Diamond 79%）
- 高达 58 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 720 毫秒
- 开放模型权重 (2.8T total (MoE))，支持通过 vLLM、SGLang 或 Ollama 在私有 GPU 集群上本地部署与微调
Considerations & Trade-offs
- 在本地以满血精度运行需要较高的 GPU 显存 (VRAM) 与运维基础设施
- 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
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-6 Luna | Kimi K3 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.13 | $2.00 |
Output Tokens / Task Tokens generated per task | 46K | 35K |
Reasoning Tokens / Task Thinking tokens burned per task | 21K | 18K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-6 Luna | Kimi K3 |
|---|---|---|
| Official API Model ID (SDK) | gpt-6-luna | kimi-k3 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-09 | 2026-07 |
| Knowledge Cutoff | April 2026 | March 2026 |
| Architecture | Ultra-budget compact reasoning model | Colossus-Scale Multimodal Sparse Mixture-of-Experts |
| Parameters | Confidential | 2.8T total (MoE) |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Flat rate ($0.1/1M in) across full 272K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.01/1M (no extra cache-write fee) | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| 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 | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| 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) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 1736 GB VRAM (4-bit Quantized) · 6020 GB VRAM (Native BF16) |
| Safety & Frontier Classification | OpenAI Preparedness Framework Audited & System Card Published | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision | text, vision |
| Output Modalities | text | text |
GPT-6 Luna vs Kimi K3: 常见问题解答 (FAQ)
Which is more intelligent, GPT-6 Luna or Kimi K3?
Kimi K3 is more intelligent. It scores 93 compared with 67 on the composite Intelligence Index.
Which model is faster?
GPT-6 Luna is faster, generating 245 tokens per second compared with 58 tokens per second.
Which model is cheaper to run?
Kimi K3 is cheaper at $0.00 per 1M tokens blended, compared with $0.08 (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 0.72s.
Which model has a larger context window?
Kimi K3 has a larger context window, supporting 1.05M tokens compared with 272K tokens.
综合结论
Choose GPT-6 Luna for 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。, or choose Kimi K3 for 通过大规模强化学习训练、支持私有化部署的开源权重推理任务、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。.
常见问题 (FAQ)
Which is cheaper: GPT-6 Luna or Kimi K3?+
GPT-6 Luna costs $0.1/1M input and $0.5/1M output tokens, while Kimi K3 costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: GPT-6 Luna or Kimi K3?+
GPT-6 Luna supports 272K tokens whereas Kimi K3 supports 1.0M tokens.
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 & Moonshot AI Official Documentation