Whisper Large v3 Turbo vs Whisper Large v3
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Whisper Large v3 Turbo di OpenAI e Whisper Large v3 di OpenAI sono due modelli di IA di riferimento. Whisper Large v3 Turbo offre una finestra di contesto di 448 token a $0.00/$0.00 per milione di token, mentre Whisper Large v3 mette a disposizione 448 token di contesto a $0.01/$0.00 per milione di token. Di seguito analizziamo benchmark, latenza e costi.
Cambia e Confronta Qualsiasi Modello
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Whisper Large v3 Turbo
Modello Vocale e Audio NativoWhisper Large v3 Turbo di OpenAI è un modello di IA progettato per la sintesi vocale naturale, l’elaborazione audio in tempo reale e la trascrizione, con finestra di contesto da 0K, velocità di 180 token/s, indice di intelligenza di 42/100
$0.00 / 1M
$0.00 / 1M
448 tokens
36%
Whisper Large v3
Modello Vocale e Audio NativoWhisper Large v3 di OpenAI è un modello di IA progettato per la sintesi vocale naturale, l’elaborazione audio in tempo reale e la trascrizione, con finestra di contesto da 0K, velocità di 55 token/s, indice di intelligenza di 45/100 e tarif
$0.01 / 1M
$0.00 / 1M
448 tokens
36%
Differenze Chiave a Colpo d’Occhio
- 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% |
Chi vince in ogni categoria?
At-a-glance decision frameworkPunteggi Benchmark Verificati
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.
Latenza e Tempo di Risposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulatore Interattivo dei Costi Token
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Whisper Large v3 Turbo · Strengths & Key Advantages
- Finestra di contesto da 0K con capacità massima di output fino a 0K token (SWE-bench Verified 36% · GPQA Diamond 46%)
- Throughput elevato di 180 token/secondo con latenza al primo token (TTFT) di soli 120 ms
- Pesi aperti (809M) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama
Considerations & Trade-offs
- Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
- Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità
Whisper Large v3 · Strengths & Key Advantages
- Finestra di contesto da 0K con capacità massima di output fino a 0K token (SWE-bench Verified 36% · GPQA Diamond 46%)
- Throughput elevato di 55 token/secondo con latenza al primo token (TTFT) di soli 350 ms
- Pesi aperti (1.55B) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama
Considerations & Trade-offs
- Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
- Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità
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 |
Specifiche Tecniche
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: Risposte Rapide (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.
Verdetto
Sia Whisper Large v3 Turbo (Ideale per la sintesi vocale naturale, l’elaborazione audio in tempo reale e la trascrizione, sistemi RAG aziendali e integrazioni API scalabili.) sia Whisper Large v3 (Ideale per la sintesi vocale naturale, l’elaborazione audio in tempo reale e la trascrizione, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: Whisper Large v3 guida nei benchmark di ragionamento e programmazione, Whisper Large v3 Turbo garantisce una generazione più rapida e Whisper Large v3 Turbo offre il miglior rapporto qualità-prezzo per carichi API elevati.
Domande Frequenti (FAQ)
Quale modello è migliore per coding e ragionamento, Whisper Large v3 Turbo o Whisper Large v3?+
Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Whisper Large v3. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.
Qual è la differenza di prezzo API tra Whisper Large v3 Turbo e Whisper Large v3?+
Whisper Large v3 Turbo costa $0.00 / $0.00 per milione di token di input/output, mentre Whisper Large v3 costa $0.01 / $0.00. Nel costo combinato, Whisper Large v3 Turbo risulta più economico.
Quale modello ha la finestra di contesto più ampia e la velocità maggiore?+
Whisper Large v3 Turbo supporta 448 token di contesto contro 448 token di Whisper Large v3, mentre Whisper Large v3 Turbo offre una velocità di generazione superiore.
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.
Fonti ufficiali: Documentazione API ufficiale di OpenAI e OpenAI, schede di sistema e benchmark indipendenti (2026).