Whisper Large v3 Turbo vs Whisper Large v3
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Whisper Large v3 Turbo von OpenAI und Whisper Large v3 von OpenAI gehören zu den führenden KI-Modellen. Whisper Large v3 Turbo bietet ein Kontextfenster von 448 Tokens bei API-Preisen von $0.00/$0.00 pro 1M Tokens, während Whisper Large v3 448 Tokens Kontext zu $0.01/$0.00 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.
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Whisper Large v3 Turbo
Natives Sprach- & AudiomodellWhisper Large v3 Turbo von OpenAI ist ein KI-Modell für natürliche Sprachsynthese, Echtzeit-Audioverarbeitung und präzise Transkription mit 0K Kontextfenster, 180 Token/s Durchsatz, einem Intelligenz-Index von 42/100 und $0.0006/$0 pro 1M T
$0.00 / 1M
$0.00 / 1M
448 tokens
36%
Whisper Large v3
Natives Sprach- & AudiomodellWhisper Large v3 von OpenAI ist ein KI-Modell für natürliche Sprachsynthese, Echtzeit-Audioverarbeitung und präzise Transkription mit 0K Kontextfenster, 55 Token/s Durchsatz, einem Intelligenz-Index von 45/100 und $0.006/$0 pro 1M Token.
$0.01 / 1M
$0.00 / 1M
448 tokens
36%
Wichtigste Unterschiede auf einen Blick
- Whisper Large v3 Turbo is 10.0x cheaper for API usage (input + output tokens combined).
- Whisper Large v3 Turbo is faster at 180 tok/s vs 55 tok/s.
- Whisper Large v3 leads on SWE-bench coding (36.0% vs 36.0%).
| Feature | Whisper Large v3 Turbo | Whisper Large v3 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 42 | 45 |
Provider | OpenAI | OpenAI |
Input Price Cost per million input tokens | $0.00 / 1M | $0.01 / 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 | 448 tokens~1 A4 pages of text |
Max Output Tokens Per single request | 0K | 0K |
Throughput Speed Tokens generated per second | 180 tok/s | 55 tok/s |
SWE-bench Verified Real-world software engineering | 36% | 36% |
GPQA Diamond Graduate-level reasoning | 46% | 46% |
Wer gewinnt in welcher Disziplin?
At-a-glance decision frameworkGeprüfte Benchmark-Ergebnisse im Vergleich
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.
Latenz & Antwortzeit
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Interaktiver Token-Kosten-Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Whisper Large v3 Turbo · Strengths & Key Advantages
- 0K Kontextfenster und bis zu 0K maximale Output-Token (SWE-bench Verified 36% · GPQA Diamond 46%)
- Hohe Generierungsgeschwindigkeit von 180 Token/Sekunde bei nur 120 ms Erstlatenz (TTFT)
- Offene Modellgewichte (809M) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama
Considerations & Trade-offs
- Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
- Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen
Whisper Large v3 · Strengths & Key Advantages
- 0K Kontextfenster und bis zu 0K maximale Output-Token (SWE-bench Verified 36% · GPQA Diamond 46%)
- Hohe Generierungsgeschwindigkeit von 55 Token/Sekunde bei nur 350 ms Erstlatenz (TTFT)
- Offene Modellgewichte (1.55B) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama
Considerations & Trade-offs
- Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
- Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Whisper Large v3 Turbo | Whisper Large v3 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.03 | $0.09 |
Output Tokens / Task Tokens generated per task | 3K | 3K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Technische Spezifikationen
Architecture, release timeline, capabilities, and modalities.
| Feature | Whisper Large v3 Turbo | Whisper Large v3 |
|---|---|---|
| Official API Model ID (SDK) | whisper-large-v3-turbo | whisper-large-v3 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2024-10 | 2023-11 |
| Knowledge Cutoff | 2024 (audio corpus) | 2023 (audio corpus) |
| Architecture | Distilled encoder-decoder speech transformer | Encoder-decoder speech transformer |
| Parameters | 809M | 1.55B |
| 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 | Flat rate ($0.006/1M in) across full 0K window |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0/1M (no extra cache-write fee) | Automatic prefix caching at $0/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | Text-only completion (No GUI action loop) | Text-only completion (No GUI action loop) |
| MCP & Parallel Tool Calling | Prompt-based JSON extraction only | Prompt-based JSON extraction only |
| Web Search Grounding & Sandbox | Native Web Search tool + Hosted Code Interpreter & File Search RAG | 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) | 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) · Audio (~25–32 tokens/sec) |
| 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) · 2 GB VRAM (4-bit Quantized) · 4 GB VRAM (Native BF16) |
| Safety & Frontier Classification | OpenAI Preparedness Framework Audited & System Card Published | OpenAI Preparedness Framework Audited & System Card Published |
| 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 | audio |
| Output Modalities | text | text |
Whisper Large v3 Turbo vs Whisper Large v3: Schnelle Antworten (FAQ)
Which is more intelligent, Whisper Large v3 Turbo or Whisper Large v3?
Whisper Large v3 is more intelligent. It scores 45 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 55 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 0.35s.
Fazit
Sowohl Whisper Large v3 Turbo (Optimal für natürliche Sprachsynthese, Echtzeit-Audioverarbeitung und präzise Transkription, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Whisper Large v3 (Optimal für natürliche Sprachsynthese, Echtzeit-Audioverarbeitung und präzise Transkription, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Whisper Large v3 führt bei komplexem Reasoning und Coding, Whisper Large v3 Turbo liefert den höheren Token-Durchsatz und Whisper Large v3 Turbo ist die kosteneffizienteste Wahl für große API-Workloads.
Häufig gestellte Fragen (FAQ)
Welches Modell ist besser für Coding und Reasoning – Whisper Large v3 Turbo oder Whisper Large v3?+
Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Whisper Large v3 vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.
Wie unterscheiden sich die API-Preise von Whisper Large v3 Turbo und Whisper Large v3?+
Whisper Large v3 Turbo kostet $0.00 / $0.00 pro 1M Input-/Output-Tokens, Whisper Large v3 liegt bei $0.01 / $0.00. Beim gewichteten Gesamtpreis ist Whisper Large v3 Turbo günstiger.
Welches Modell hat das größere Kontextfenster und die höhere Geschwindigkeit?+
Whisper Large v3 Turbo bietet 448 Tokens Kontext gegenüber 448 Tokens bei Whisper Large v3. Bei der Generierungsgeschwindigkeit liegt Whisper Large v3 Turbo vorn.
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.
Offizielle Quellen: Offizielle API-Dokumentation von OpenAI und OpenAI, Systemkarten und unabhängige Benchmark-Auswertungen (2026).