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.
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Whisper Large v3 Turbo
Model Suara & Audio AsliWhisper Large v3 Turbo dari OpenAI adalah model AI yang dirancang untuk sintesis suara alami, pemrosesan audio real-time, dan transkripsi ucapan, menghadirkan jendela konteks 0K, kecepatan 180 token/detik, indeks kecerdasan 42/100, serta ta
$0.00 / 1M
$0.00 / 1M
448 tokens
36%
Llama 5
Model Dasar Bobot TerbukaLlama 5 dari Meta adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 5M, kecepatan 65 token/detik, indeks kecerdasan 94/100, serta akses gratis open-weig
$0.00 / 1M
$0.00 / 1M
5M tokens
74%
Perbedaan Utama Sekilas
- 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%).
| Feature | 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% |
Siapa Unggul di Mana?
At-a-glance decision frameworkSkor Benchmark Terverifikasi
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.
Latensi & Waktu Respons
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulator Biaya Token Interaktif
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Whisper Large v3 Turbo · Strengths & Key Advantages
- Jendela konteks 0K dengan kapasitas output maksimum hingga 0K token (SWE-bench Verified 36% · GPQA Diamond 46%)
- Kecepatan generasi tinggi 180 token/detik dengan latensi token pertama (TTFT) hanya 120 ms
- Bobot terbuka (809M) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama
Considerations & Trade-offs
- Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
- Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi
Llama 5 · Strengths & Key Advantages
- Jendela konteks 5M dengan kapasitas output maksimum hingga 128K token (SWE-bench Verified 74% · GPQA Diamond 84%)
- Kecepatan generasi tinggi 65 token/detik dengan latensi token pertama (TTFT) hanya 850 ms
- Bobot terbuka (600B+ total (~40-80B )) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama
Considerations & Trade-offs
- Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
- Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi
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 |
Spesifikasi Teknis
Architecture, release timeline, capabilities, and modalities.
| Feature | 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: Jawaban Cepat (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.
Kesimpulan
Choose Whisper Large v3 Turbo for sangat cocok untuk sintesis suara alami, pemrosesan audio real-time, dan transkripsi ucapan, sistem rag perusahaan, dan integrasi api skala produksi., or choose Llama 5 for sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem rag perusahaan, dan integrasi api skala produksi..
Pertanyaan Umum (FAQ)
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.
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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.
Sumber resmi: OpenAI & Meta Official Documentation