DeepSeek R1 vs o3-mini
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
DeepSeek 的 DeepSeek R1 与 OpenAI 的 o3-mini 是当前备受关注的两款主力 AI 模型。DeepSeek R1 提供 128K Token 上下文窗口，API 定价为每百万 Token 输入 $0.55 / 输出 $2.19；而 o3-mini 提供 200K Token 上下文窗口，定价为每百万 Token 输入 $1.10 / 输出 $4.40。以下为两者的基准测试、延迟与成本全面对比。
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DeepSeek R1
开源权重推理大模型DeepSeek 推出的 DeepSeek R1 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 128K 上下文窗口、45 Token/秒生成速度与 49/100 综合智能指数（每百万 Token $0.55 输入 / $2.19 输出）。
$0.55 / 1M
$2.19 / 1M
128K tokens
49.2%
o3-mini
深度逻辑推理引擎OpenAI 推出的 o3-mini 是一款专为博士级数理逻辑、科学推导与多步思维链 (CoT) 设计的深度推理大模型，具备 200K 上下文窗口、85 Token/秒生成速度与 51/100 综合智能指数（每百万 Token $1.1 输入 / $4.4 输出）。
$1.10 / 1M
$4.40 / 1M
200K tokens
49%
核心差异一览
- DeepSeek R1 is 2.1x cheaper for API usage (input + output tokens combined).
- o3-mini has a larger context window (200K vs 128K tokens).
- o3-mini is faster at 85 tok/s vs 45 tok/s.
- DeepSeek R1 leads on SWE-bench coding (49.2% vs 49.0%).
| Feature | DeepSeek R1 | o3-mini |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 49 | 51 |
Provider | DeepSeek | OpenAI |
Input Price Cost per million input tokens | $0.55 / 1M | $1.10 / 1M |
Output Price Cost per million output tokens | $2.19 / 1M | $4.40 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.43 / 1M | $1.05 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 200K tokens~300 A4 pages of text |
Max Output Tokens Per single request | 8K | 100K |
Throughput Speed Tokens generated per second | 45 tok/s | 85 tok/s |
SWE-bench Verified Real-world software engineering | 49.2% | 49% |
GPQA Diamond Graduate-level reasoning | 71.5% | 79.7% |
Cached Input Price Prompt caching discount per 1M tokens | $0.14 / 1M | $0.55 / 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.
DeepSeek R1 · Strengths & Key Advantages
- 提供 128K 上下文窗口与最高 8K 输出容量，核心基准表现优异（SWE-bench Verified 49.2% · GPQA Diamond 71.5%）
- 高达 45 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 950 毫秒
- 开放模型权重 (671B Total (37B ))，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
o3-mini · Strengths & Key Advantages
- 提供 200K 上下文窗口与最高 100K 输出容量，核心基准表现优异（SWE-bench Verified 49% · GPQA Diamond 79.7%）
- 高达 85 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 1400 毫秒
- 极具竞争力的 API 定价（每百万 Token $1.1 输入 / $4.4 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
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | DeepSeek R1 | o3-mini |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.65 | $1.30 |
Output Tokens / Task Tokens generated per task | 45K | 45K |
Reasoning Tokens / Task Thinking tokens burned per task | 25K | 25K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | DeepSeek R1 | o3-mini |
|---|---|---|
| Official API Model ID (SDK) | deepseek-reasoner | o3-mini |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-01 | 2025-01 |
| Knowledge Cutoff | July 2024 | October 2023 |
| Architecture | Open-Weights Reasoning MoE | Chain-of-Thought Reasoning Dense |
| Parameters | 671B Total (37B Active) | Confidential |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.55/1M in) across full 128K window | Flat rate ($1.1/1M in) across full 200K window |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.14/1M (no extra cache-write fee) | Automatic prefix caching at $0.55/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) |
| 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 | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 416 GB VRAM (4-bit Quantized) · 1443 GB VRAM (Native BF16) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | OpenAI Preparedness Framework Audited & System Card Published |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text | text |
| Output Modalities | text | text |
DeepSeek R1 vs o3-mini: 常见问题解答 (FAQ)
Which is more intelligent, DeepSeek R1 or o3-mini?
o3-mini is more intelligent. It scores 51 compared with 49 on the composite Intelligence Index.
Which model is faster?
o3-mini is faster, generating 85 tokens per second compared with 45 tokens per second.
Which model is cheaper to run?
DeepSeek R1 is cheaper at $0.43 per 1M tokens blended, compared with $1.05 (7:2:1 cache:input:output ratio).
Which model has lower latency?
DeepSeek R1 has lower latency, with a time to first token of 0.95s compared with 1.40s.
Which model has a larger context window?
o3-mini has a larger context window, supporting 200K tokens compared with 128K tokens.
综合结论
综合来看，DeepSeek R1（适合：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。）与 o3-mini（适合：专为博士级数理逻辑、科学推导与多步思维链 (CoT) 设计的深度推理任务、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。）各具优势：o3-mini 在综合智能与编程基准测试中表现更强，o3-mini 拥有更快的 Token 生成速度，而 DeepSeek R1 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，DeepSeek R1 和 o3-mini 哪个更强？+
在综合智能指数与编程基准评测中，o3-mini 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。
DeepSeek R1 与 o3-mini 的 API 价格哪个更便宜？+
DeepSeek R1 的 API 价格为输入 $0.55 / 输出 $2.19（每百万 Token），o3-mini 为输入 $1.10 / 输出 $4.40。综合混合成本来看，DeepSeek R1 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？+
DeepSeek R1 支持 128K Token 上下文窗口，而 o3-mini 支持 200K Token。在流式生成速度方面，o3-mini 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.
Related reading
- Claude 3.7 Sonnet vs DeepSeek R1：基准测试、API 价格与速度全面对比 (2026)对比
- Claude 3.7 Sonnet vs o3-mini：基准测试、API 价格与速度全面对比 (2026)对比
- DeepSeek R1 vs OpenAI o1：基准测试、API 价格与速度全面对比 (2026)对比
- Grok 4.20 (Reasoning) vs DeepSeek R1：基准测试、API 价格与速度全面对比 (2026)对比
- o1-pro vs o3-mini：基准测试、API 价格与速度全面对比 (2026)对比
- o3-pro vs DeepSeek R1：基准测试、API 价格与速度全面对比 (2026)对比
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: DeepSeek 与 OpenAI 官方 API 文档、模型发布说明及独立基准测试报告 (2026)。