o3-mini vs Kimi K3
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare o3-mini by OpenAI against Kimi K3 by Moonshot AI across real-world coding, reasoning, token economics, and latency.
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o3-mini
深度推理模型OpenAI 推出的 o3-mini 人工智能模型，具备 200K 上下文窗口，输入价格为 $1.1/百万 Token，输出价格为 $4.4/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$1.10 / 1M
$4.40 / 1M
200K tokens
49%
Kimi K3
开源权重模型Moonshot AI 推出的 Kimi K3 人工智能模型，具备 1.0M 上下文窗口，输入价格为 $0/百万 Token，输出价格为 $0/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$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 200K tokens).
- o3-mini is faster at 85 tok/s vs 58 tok/s.
- Kimi K3 leads on SWE-bench coding (74.5% vs 49.0%).
| Feature | o3-mini | Kimi K3 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 51 | 93 |
Provider | OpenAI | Moonshot AI |
Input Price Cost per million input tokens | $1.10 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $4.40 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.05 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 200K tokens~300 A4 pages of text | 1.05M tokens~1,573 A4 pages of text |
Max Output Tokens Per single request | 100K | 131K |
Throughput Speed Tokens generated per second | 85 tok/s | 58 tok/s |
SWE-bench Verified Real-world software engineering | 49% | 74.5% |
GPQA Diamond Graduate-level reasoning | 79.7% | 79% |
Cached Input Price Prompt caching discount per 1M tokens | $0.55 / 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.
o3-mini · Strengths & Key Advantages
- 由 OpenAI 研发的高精度 Chain-of-Thought Reasoning Dense 架构，支持 200K 上下文窗口
- 极具竞争力的 API 定价（输入 $1.1/1M，输出 $4.4/1M），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
Kimi K3 · Strengths & Key Advantages
- 由 Moonshot AI 研发的高精度 Colossus-Scale Multimodal Sparse Mixture-of-Experts 架构，支持 1.0M 上下文窗口
- 极具竞争力的 API 定价（输入 $0/1M，输出 $0/1M），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 | o3-mini | Kimi K3 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $1.30 | $2.00 |
Output Tokens / Task Tokens generated per task | 45K | 35K |
Reasoning Tokens / Task Thinking tokens burned per task | 25K | 18K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | o3-mini | Kimi K3 |
|---|---|---|
| Official API Model ID (SDK) | o3-mini | kimi-k3 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-01 | 2026-07 |
| Knowledge Cutoff | October 2023 | March 2026 |
| Architecture | Chain-of-Thought Reasoning Dense | 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 ($1.1/1M in) across full 200K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.55/1M (no extra cache-write fee) | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | 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) | 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 | text, vision |
| Output Modalities | text | text |
o3-mini vs Kimi K3: 常见问题解答 (FAQ)
Which is more intelligent, o3-mini or Kimi K3?
Kimi K3 is more intelligent. It scores 93 compared with 51 on the composite Intelligence Index.
Which model is faster?
o3-mini is faster, generating 85 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 $1.05 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Kimi K3 has lower latency, with a time to first token of 0.72s compared with 1.40s.
Which model has a larger context window?
Kimi K3 has a larger context window, supporting 1.05M tokens compared with 200K tokens.
综合结论
Choose o3-mini for 企业级 ai 自动化、代码研发、智能体（agent）工作流以及 openai 生态深度集成, or choose Kimi K3 for 企业级 ai 自动化、代码研发、智能体（agent）工作流以及 moonshot ai 生态深度集成.
常见问题 (FAQ)
Which is cheaper: o3-mini or Kimi K3?+
o3-mini costs $1.1/1M input and $4.4/1M output tokens, while Kimi K3 costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: o3-mini or Kimi K3?+
o3-mini supports 200K 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