Whisper Large v3 Turbo vs Kimi K3
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Whisper Large v3 Turbo by OpenAI against Kimi K3 by Moonshot AI across real-world coding, reasoning, token economics, and latency.
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Whisper Large v3 Turbo
Yerel Ses ve Konuşma ModeliOpenAI tarafından geliştirilen Whisper Large v3 Turbo; doğal ses sentezi, gerçek zamanlı ses işleme ve konuşma metne dönüştürme için tasarlanmış olup 0K bağlam penceresi, 180 token/sn hız, 42/100 zeka indeksi ve 1M token başına $0.0006/$0 f
$0.00 / 1M
$0.00 / 1M
448 tokens
36%
Kimi K3
Açık Ağırlıklı Akıl YürütmeMoonshot AI tarafından geliştirilen Kimi K3; yüksek verimli açık ağırlıklı düşünce zinciri (Chain-of-Thought) akıl yürütme için tasarlanmış olup 1.0M bağlam penceresi, 58 token/sn hız, 93/100 zeka indeksi ve ücretsiz açık ağırlık erişimi su
$0.00 / 1M
$0.00 / 1M
1.05M tokens
74.5%
Bir Bakışta Temel Farklar
- Both models cost the same under this workload — choose on capability, not price.
- Kimi K3 has a larger context window (1M vs 0K tokens).
- Whisper Large v3 Turbo is faster at 180 tok/s vs 58 tok/s.
- Kimi K3 leads on SWE-bench coding (74.5% vs 36.0%).
| Feature | Whisper Large v3 Turbo | Kimi K3 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 42 | 93 |
Provider | OpenAI | Moonshot AI |
Input Price Cost per million input tokens | $0.00 / 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 | 180 tok/s | 58 tok/s |
SWE-bench Verified Real-world software engineering | 36% | 74.5% |
GPQA Diamond Graduate-level reasoning | 46% | 79% |
Hangi Alanda Kim Kazanıyor?
At-a-glance decision frameworkDoğrulanmış Benchmark Puanları
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.
Gecikme Süresi ve Yanıt Hızı
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Gerçek Zamanlı Token Maliyet Simülatörü
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Whisper Large v3 Turbo · Strengths & Key Advantages
- 0K bağlam penceresi ve yanıt başına maksimum 0K çıktı tokenı kapasitesi (SWE-bench Verified 36% · GPQA Diamond 46%)
- Sadece 120 ms ilk token gecikmesi (TTFT) ile saniyede 180 token yüksek üretim hızı
- vLLM, SGLang veya Ollama üzerinden yerel GPU sunucularında barındırılabilen açık ağırlıklı mimari (809M)
Considerations & Trade-offs
- Tam hassasiyette yerel GPU çıkarımı için yeterli VRAM kapasitesine sahip donanım altyapısı gerektirir
- Çok karmaşık akademik ve bilimsel çıkarımlarda üst düzey ağır akıl yürütme modelleri daha derin sonuçlar verebilir
Kimi K3 · Strengths & Key Advantages
- 1.0M bağlam penceresi ve yanıt başına maksimum 131K çıktı tokenı kapasitesi (SWE-bench Verified 74.5% · GPQA Diamond 79%)
- Sadece 720 ms ilk token gecikmesi (TTFT) ile saniyede 58 token yüksek üretim hızı
- vLLM, SGLang veya Ollama üzerinden yerel GPU sunucularında barındırılabilen açık ağırlıklı mimari (2.8T total (MoE))
Considerations & Trade-offs
- Tam hassasiyette yerel GPU çıkarımı için yeterli VRAM kapasitesine sahip donanım altyapısı gerektirir
- Çok karmaşık akademik ve bilimsel çıkarımlarda üst düzey ağır akıl yürütme modelleri daha derin sonuçlar verebilir
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Whisper Large v3 Turbo | Kimi K3 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.03 | $2.00 |
Output Tokens / Task Tokens generated per task | 3K | 35K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 18K |
Teknik Özellikler
Architecture, release timeline, capabilities, and modalities.
| Feature | Whisper Large v3 Turbo | Kimi K3 |
|---|---|---|
| Official API Model ID (SDK) | whisper-large-v3-turbo | kimi-k3 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2024-10 | 2026-07 |
| Knowledge Cutoff | 2024 (audio corpus) | March 2026 |
| Architecture | Distilled encoder-decoder speech transformer | Colossus-Scale Multimodal Sparse Mixture-of-Experts |
| Parameters | 809M | 2.8T total (MoE) |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | 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 ($0.0006/1M 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 | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | 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 | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 2 GB VRAM (4-bit Quantized) · 4 GB VRAM (Native BF16) | 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) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | audio | text, vision |
| Output Modalities | text | text |
Whisper Large v3 Turbo vs Kimi K3: Hızlı Yanıtlar (SSS)
Which is more intelligent, Whisper Large v3 Turbo or Kimi K3?
Kimi K3 is more intelligent. It scores 93 compared with 42 on the composite Intelligence Index.
Which model is faster?
Whisper Large v3 Turbo is faster, generating 180 tokens per second compared with 58 tokens per second.
Which model has lower latency?
Whisper Large v3 Turbo has lower latency, with a time to first token of 0.12s 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.
Sonuç
Choose Whisper Large v3 Turbo for doğal ses sentezi, gerçek zamanlı ses işleme ve konuşma metne dönüştürme, kurumsal rag mimarileri ve ölçeklenebilir üretim api entegrasyonları için idealdir., or choose Kimi K3 for yüksek verimli açık ağırlıklı düşünce zinciri (chain-of-thought) akıl yürütme, kurumsal rag mimarileri ve ölçeklenebilir üretim api entegrasyonları için idealdir..
Sıkça Sorulan Sorular (SSS)
Which is cheaper: Whisper Large v3 Turbo or Kimi K3?+
Whisper Large v3 Turbo costs $0.0006/1M 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: Whisper Large v3 Turbo or Kimi K3?+
Whisper Large v3 Turbo 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.
Resmi kaynaklar: OpenAI & Moonshot AI Official Documentation