GPT Realtime Whisper vs Kimi K3
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare GPT Realtime Whisper by OpenAI against Kimi K3 by Moonshot AI across real-world coding, reasoning, token economics, and latency.
원하는 AI 모델 선택 및 실시간 비교
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Live comparison: no in-depth editorial review has been published for this pair yet.
GPT Realtime Whisper
고성능 AI 모델OpenAI의 GPT Realtime Whisper 모델은 448 토큰 컨텍스트 윈도우와 입력 $0.006/1M 토큰, 출력 $0/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$0.01 / 1M
$0.00 / 1M
448 tokens
36%
Kimi K3
오픈 웨이트 모델Moonshot AI의 Kimi K3 모델은 1.0M 토큰 컨텍스트 윈도우와 입력 $0/1M 토큰, 출력 $0/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$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 0K tokens).
- GPT Realtime Whisper is faster at 140 tok/s vs 58 tok/s.
- Kimi K3 leads on SWE-bench coding (74.5% vs 36.0%).
| Feature | GPT Realtime Whisper | Kimi K3 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 44 | 93 |
Provider | OpenAI | Moonshot AI |
Input Price Cost per million input tokens | $0.01 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 448 tokens~1 A4 pages of text | 1.05M tokens~1,573 A4 pages of text |
Max Output Tokens Per single request | 0K | 131K |
Throughput Speed Tokens generated per second | 140 tok/s | 58 tok/s |
SWE-bench Verified Real-world software engineering | 36% | 74.5% |
GPQA Diamond Graduate-level reasoning | 46% | 79% |
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.
GPT Realtime Whisper · Strengths & Key Advantages
- OpenAI의 첨단 Streaming speech-to-text transformer 아키텍처와 최대 448 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0.006/1M, 출력 $0/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
Kimi K3 · Strengths & Key Advantages
- Moonshot AI의 첨단 Colossus-Scale Multimodal Sparse Mixture-of-Experts 아키텍처와 최대 1.0M 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0/1M, 출력 $0/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT Realtime Whisper | Kimi K3 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.04 | $2.00 |
Output Tokens / Task Tokens generated per task | 3K | 35K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 18K |
기술 사양 (Specifications)
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT Realtime Whisper | Kimi K3 |
|---|---|---|
| Official API Model ID (SDK) | gpt-realtime-whisper | kimi-k3 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-05 | 2026-07 |
| Knowledge Cutoff | February 2026 | March 2026 |
| Architecture | Streaming speech-to-text transformer | 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 ($0.006/1M in) across full 0K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0/1M (no extra cache-write fee) | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| Computer Use / GUI Agent | Text-only completion (No GUI action loop) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| MCP & Parallel Tool Calling | Prompt-based JSON extraction only | 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) · Audio (~25–32 tokens/sec) | 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 | audio | text, vision |
| Output Modalities | text | text |
GPT Realtime Whisper vs Kimi K3: 핵심 요약 문답 (FAQ)
Which is more intelligent, GPT Realtime Whisper or Kimi K3?
Kimi K3 is more intelligent. It scores 93 compared with 44 on the composite Intelligence Index.
Which model is faster?
GPT Realtime Whisper is faster, generating 140 tokens per second compared with 58 tokens per second.
Which model has lower latency?
GPT Realtime Whisper has lower latency, with a time to first token of 0.15s compared with 0.72s.
Which model has a larger context window?
Kimi K3 has a larger context window, supporting 1.05M tokens compared with 448 tokens.
종합 평가
Choose GPT Realtime Whisper for 기업 업무 자동화, 소프트웨어 개발, 자율형 ai 에이전트 및 openai 기반 프로덕션 파이프라인, or choose Kimi K3 for 기업 업무 자동화, 소프트웨어 개발, 자율형 ai 에이전트 및 moonshot ai 기반 프로덕션 파이프라인.
자주 묻는 질문 (FAQ)
Which is cheaper: GPT Realtime Whisper or Kimi K3?+
GPT Realtime Whisper costs $0.006/1M input and $0/1M output tokens, while Kimi K3 costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: GPT Realtime Whisper or Kimi K3?+
GPT Realtime Whisper supports 0K 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