Whisper Large v3 Turbo vs Llama 5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Whisper Large v3 Turbo by OpenAI against Llama 5 by Meta across real-world coding, reasoning, token economics, and latency.
Changez et Comparez les Modèles
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.
Whisper Large v3 Turbo
ASR rapideun rapide, accurate parole-à-texte modèle pour transcription et captioning.
$0.00 / 1M
$0.00 / 1M
448 tokens
36%
Llama 5
Auto-amélioration récursivede Meta phare ouvert modèle intégrant natif agent training, recursive self-improvement, et 5M token contexte.
$0.00 / 1M
$0.00 / 1M
5M tokens
74%
Différences Clés en un Coup d’Œil
- Both models cost the same under this workload — choose on capability, not price.
- Llama 5 has a larger context window (5M vs 0K tokens).
- Whisper Large v3 Turbo is faster at 180 tok/s vs 65 tok/s.
- Llama 5 leads on SWE-bench coding (74.0% vs 36.0%).
| Caractéristique | Whisper Large v3 Turbo | Llama 5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 42 | 94 |
Provider | OpenAI | Meta |
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 | 5M tokens~7,500 A4 pages of text |
Max Output Tokens Per single request | 0K | 128K |
Throughput Speed Tokens generated per second | 180 tok/s | 65 tok/s |
SWE-bench Verified Real-world software engineering | 36% | 74% |
GPQA Diamond Graduate-level reasoning | 46% | 84% |
Qui gagne où ?
At-a-glance decision frameworkScores de Benchmarks Vérifiés
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.
Latence et Temps de Réponse
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulateur Interactif de Coût de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Whisper Large v3 Turbo · Strengths & Key Advantages
- Roughly eight times plus rapide que Whisper grand v3
- Far cheaper par minute d'audio
- très close à v3 précision sur clean parole
- Excellent précision-à-vitesse ratio pour bulk jobs
Considerations & Trade-offs
- Slightly moins accurate que v3 sur difficult ou noisy audio
- Still pas un true diffusion continue modèle
Llama 5 · Strengths & Key Advantages
- 5 millions token contexte avec natif multi-audio/vidéo compréhension
- Boucle d'entraînement à auto-amélioration récursive
- Completely à poids ouverts avec d'entreprise commercial license
Considerations & Trade-offs
- complexe MoE hébergement requirements
- Les modifications du tokeniseur 4B nécessitent la mise à jour des environnements d'ajustement existants
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Whisper Large v3 Turbo | Llama 5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.03 | $0.00 |
Output Tokens / Task Tokens generated per task | 3K | 40K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 20K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | Whisper Large v3 Turbo | Llama 5 |
|---|---|---|
| Official API Model ID (SDK) | whisper-large-v3-turbo | meta-llama/Llama-5 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2024-10 | 2026-04 |
| Knowledge Cutoff | 2024 (audio corpus) | January 2026 |
| Architecture | Distilled encoder-decoder speech transformer | Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active) |
| Parameters | 809M | 600B+ total (~40-80B active) |
| 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) · Audio (~25–32 tokens/sec) · Video (~258–300 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) · 372 GB VRAM (4-bit Quantized) · 1290 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, video, audio |
| Output Modalities | text | text |
Whisper Large v3 Turbo vs Llama 5: Réponses Rapides (FAQ)
Which is more intelligent, Whisper Large v3 Turbo or Llama 5?
Llama 5 is more intelligent. It scores 94 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 65 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 0.85s.
Which model has a larger context window?
Llama 5 has a larger context window, supporting 5M tokens compared with 448 tokens.
Verdict
Choose Whisper Large v3 Turbo for rapide, cheap transcription à échelle, or choose Llama 5 for autonome open source multi-agent systems, profond logiciel refactorisation, et de pointe recherche.
Questions fréquentes
Which is cheaper: Whisper Large v3 Turbo or Llama 5?+
Whisper Large v3 Turbo costs $0.0006/1M input and $0/1M output tokens, while Llama 5 costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: Whisper Large v3 Turbo or Llama 5?+
Whisper Large v3 Turbo supports 0K tokens whereas Llama 5 supports 5M tokens.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
- Aion-3.0 vs Aion-Mini: Benchmarks, Pricing & Speed ComparisonComparer
- Aion-3.0 vs Gemini 3.6 Flash: Benchmarks, Pricing & Speed ComparisonComparer
- Aion-Mini vs Laguna XS 2.1: Benchmarks, Pricing & Speed ComparisonComparer
- Aya Expanse 32B vs Llama 3.2 90B Vision: Benchmarks, Pricing & Speed ComparisonComparer
- Aya Expanse 8B vs Command R+: Benchmarks, Pricing & Speed ComparisonComparer
- Aya Expanse 8B vs Qwen 2.5 7B Instruct: Benchmarks, Pricing & Speed ComparisonComparer
Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: OpenAI & Meta Official Documentation