o3-deep-research vs Kimi K3
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare o3-deep-research by OpenAI against Kimi K3 by Moonshot AI across real-world coding, reasoning, token economics, and latency.
원하는 AI 모델 선택 및 실시간 비교
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Live comparison: no in-depth editorial review has been published for this pair yet.
o3-deep-research
심층 추론 및 논리 엔진OpenAI의 o3-deep-research 모델은 심층 논리 추론, 고급 수학 문제 해결 및 다단계 과학적 분석에 최적화된 AI 모델로, 200K 컨텍스트 윈도우, 초당 45 토큰의 생성 속도, 종합 지능 지수 55/100점(1M 토큰당 입력 $10 / 출력 $40)을 제공합니다.
$10.00 / 1M
$40.00 / 1M
200K tokens
55%
Kimi K3
오픈 웨이트 추론 모델Moonshot AI의 Kimi K3 모델은 강화학습 기반의 고효율 오픈 웨이트 사고 사슬(Chain-of-Thought) 추론에 최적화된 AI 모델로, 1.0M 컨텍스트 윈도우, 초당 58 토큰의 생성 속도, 종합 지능 지수 93/100점(무료 오픈 웨이트 제공)을 제공합니다.
$0.00 / 1M
$0.00 / 1M
1.05M tokens
74.5%
한눈에 보는 핵심 차이점
- Kimi K3 is 1.0x cheaper for API usage (input + output tokens combined).
- Kimi K3 has a larger context window (1M vs 200K tokens).
- Kimi K3 is faster at 58 tok/s vs 45 tok/s.
- Kimi K3 leads on SWE-bench coding (74.5% vs 55.0%).
| Feature | o3-deep-research | Kimi K3 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 55 | 93 |
Provider | OpenAI | Moonshot AI |
Input Price Cost per million input tokens | $10.00 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $40.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $7.75 / 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 | 45 tok/s | 58 tok/s |
SWE-bench Verified Real-world software engineering | 55% | 74.5% |
GPQA Diamond Graduate-level reasoning | 74.5% | 79% |
Cached Input Price Prompt caching discount per 1M tokens | $2.50 / 1M | $0.00 / 1M |
부문별 우위 모델
At-a-glance decision framework검증된 벤치마크 점수 비교
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.
지연 시간(Latency) 및 응답 속도
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
실시간 API 토큰 비용 시뮬레이터
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
o3-deep-research · Strengths & Key Advantages
- 200K 컨텍스트 윈도우 및 최대 100K 출력 토큰 생성 지원 (SWE-bench Verified 55% · GPQA Diamond 74.5%)
- 초당 45 토큰의 빠른 처리 속도와 3400ms에 불과한 첫 토큰 지연 시간(TTFT)
- 합리적인 API 요금 체계(1M 토큰당 입력 $10 / 출력 $40)와 프롬프트 캐싱, 함수 호출 및 정형화된 JSON 출력 완벽 지원
Considerations & Trade-offs
- 독점 클라우드 전용 모델로 로컬 오프라인 서버에 가중치를 다운로드하여 실행할 수 없습니다
- 대규모 프로덕션 호출 환경에서는 비용 절감을 위해 프롬프트 캐싱과 배치 API(Batch API) 활용이 권장됩니다
Kimi K3 · Strengths & Key Advantages
- 1.0M 컨텍스트 윈도우 및 최대 131K 출력 토큰 생성 지원 (SWE-bench Verified 74.5% · GPQA Diamond 79%)
- 초당 58 토큰의 빠른 처리 속도와 720ms에 불과한 첫 토큰 지연 시간(TTFT)
- 오픈 웨이트 아키텍처 (2.8T total (MoE))를 통해 vLLM, SGLang, Ollama 기반 자체 GPU 서버 구축 및 파인튜닝 가능
Considerations & Trade-offs
- 비양자화 전체 정밀도로 로컬 서버에서 구동하려면 충분한 GPU VRAM 용량이 필요합니다
- 초고난도 학술 연구 및 수학 증명 과제에서는 상위 대형 추론 특화 모델이 더 깊은 분석을 제공할 수 있습니다
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | o3-deep-research | Kimi K3 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $7.28 | $2.00 |
Output Tokens / Task Tokens generated per task | 90K | 35K |
Reasoning Tokens / Task Thinking tokens burned per task | 42K | 18K |
기술 사양 (Specifications)
Architecture, release timeline, capabilities, and modalities.
| Feature | o3-deep-research | Kimi K3 |
|---|---|---|
| Official API Model ID (SDK) | o3-deep-research | kimi-k3 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-06 | 2026-07 |
| Knowledge Cutoff | June 2024 | March 2026 |
| Architecture | Agentic reasoning transformer with web and file tools | 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 ($10/1M in) across full 200K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix caching at $2.5/1M (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 |
o3-deep-research vs Kimi K3: 핵심 요약 문답 (FAQ)
Which is more intelligent, o3-deep-research or Kimi K3?
Kimi K3 is more intelligent. It scores 93 compared with 55 on the composite Intelligence Index.
Which model is faster?
Kimi K3 is faster, generating 58 tokens per second compared with 45 tokens per second.
Which model is cheaper to run?
Kimi K3 is cheaper at $0.00 per 1M tokens blended, compared with $7.75 (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 3.40s.
Which model has a larger context window?
Kimi K3 has a larger context window, supporting 1.05M tokens compared with 200K tokens.
종합 평가
Choose o3-deep-research for 심층 논리 추론, 고급 수학 문제 해결 및 다단계 과학적 분석, 엔터프라이즈 rag 지식베이스 및 대규모 프로덕션 api 연동에 최적., or choose Kimi K3 for 강화학습 기반의 고효율 오픈 웨이트 사고 사슬(chain-of-thought) 추론, 엔터프라이즈 rag 지식베이스 및 대규모 프로덕션 api 연동에 최적..
자주 묻는 질문 (FAQ)
Which is cheaper: o3-deep-research or Kimi K3?+
o3-deep-research costs $10/1M input and $40/1M output tokens, while Kimi K3 costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: o3-deep-research or Kimi K3?+
o3-deep-research 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.
공식 출처: OpenAI & Moonshot AI Official Documentation