Llama 5 vs DeepSeek V4 Pro
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Llama 5 dari Meta dan DeepSeek V4 Pro dari DeepSeek adalah dua model AI unggulan. Llama 5 menawarkan jendela konteks 5M token dengan harga $0.00/$0.00 per 1 juta token, sedangkan DeepSeek V4 Pro menyediakan konteks 1M token seharga $1.32/$3.96 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.
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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%
DeepSeek V4 Pro
Model Agen Otonom & AlatDeepSeek V4 Pro dari DeepSeek adalah model AI yang dirancang untuk alur kerja agen otonom, pemanggilan alat (tool calling), dan otomatisasi multi-langkah, menghadirkan jendela konteks 1M, kecepatan 80 token/detik, indeks kecerdasan 53/100,
$1.32 / 1M
$3.96 / 1M
1M tokens
68.5%
Perbedaan Utama Sekilas
- Llama 5 is 1.0x cheaper for API usage (input + output tokens combined).
- Llama 5 has a larger context window (5M vs 1M tokens).
- DeepSeek V4 Pro is faster at 80 tok/s vs 65 tok/s.
- Llama 5 leads on SWE-bench coding (74.0% vs 68.5%).
| Feature | Llama 5 | DeepSeek V4 Pro |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 94 | 53 |
Provider | Meta | DeepSeek |
Input Price Cost per million input tokens | $0.00 / 1M | $1.32 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $3.96 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $0.76 / 1M |
Context Window Maximum input + output tokens | 5M tokens~7,500 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 128K | 128K |
Throughput Speed Tokens generated per second | 65 tok/s | 80 tok/s |
SWE-bench Verified Real-world software engineering | 74% | 68.5% |
GPQA Diamond Graduate-level reasoning | 84% | 81.2% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.14 / 1M |
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.
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
DeepSeek V4 Pro · Strengths & Key Advantages
- Jendela konteks 1M dengan kapasitas output maksimum hingga 128K token (SWE-bench Verified 68.5% · GPQA Diamond 81.2%)
- Kecepatan generasi tinggi 80 token/detik dengan latensi token pertama (TTFT) hanya 580 ms
- Bobot terbuka (MoE (Colossal)) 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 | Llama 5 | DeepSeek V4 Pro |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.67 |
Output Tokens / Task Tokens generated per task | 40K | 52K |
Reasoning Tokens / Task Thinking tokens burned per task | 20K | 25K |
Spesifikasi Teknis
Architecture, release timeline, capabilities, and modalities.
| Feature | Llama 5 | DeepSeek V4 Pro |
|---|---|---|
| Official API Model ID (SDK) | meta-llama/Llama-5 | deepseek-reasoner |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-04 | 2026-08 |
| Knowledge Cutoff | January 2026 | May 2026 |
| Architecture | Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active) | Next-Gen Sparse MoE with Agent RL Tuning |
| Parameters | 600B+ total (~40-80B active) | MoE (Colossal) |
| 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) | Free across full context window (only KV-cache VRAM scales) | Flat rate ($1.32/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Automatic prefix caching at $0.14/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) |
| MCP & Parallel Tool Calling | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | 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) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 372 GB VRAM (4-bit Quantized) · 1290 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 43 GB VRAM (4-bit Quantized) · 151 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | 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 | text, vision, video, audio | text |
| Output Modalities | text | text |
Llama 5 vs DeepSeek V4 Pro: Jawaban Cepat (FAQ)
Which is more intelligent, Llama 5 or DeepSeek V4 Pro?
Llama 5 is more intelligent. It scores 94 compared with 53 on the composite Intelligence Index.
Which model is faster?
DeepSeek V4 Pro is faster, generating 80 tokens per second compared with 65 tokens per second.
Which model is cheaper to run?
Llama 5 is cheaper at $0.00 per 1M tokens blended, compared with $0.76 (7:2:1 cache:input:output ratio).
Which model has lower latency?
DeepSeek V4 Pro has lower latency, with a time to first token of 0.58s compared with 0.85s.
Which model has a larger context window?
Llama 5 has a larger context window, supporting 5M tokens compared with 1M tokens.
Kesimpulan
Baik Llama 5 (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun DeepSeek V4 Pro (Sangat cocok untuk alur kerja agen otonom, pemanggilan alat (tool calling), dan otomatisasi multi-langkah, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Llama 5 unggul dalam penalaran dan pemrograman, DeepSeek V4 Pro memberikan kecepatan generasi token lebih tinggi, dan Llama 5 lebih hemat biaya untuk penggunaan API skala besar.
Pertanyaan Umum (FAQ)
Mana yang lebih baik untuk coding dan penalaran, Llama 5 atau DeepSeek V4 Pro?+
Dalam indeks kecerdasan gabungan dan benchmark coding, Llama 5 memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.
Berapa perbandingan harga API Llama 5 vs DeepSeek V4 Pro?+
Llama 5 mengenakan biaya $0.00 / $0.00 per 1M token input/output, sedangkan DeepSeek V4 Pro sebesar $1.32 / $3.96. Secara keseluruhan, Llama 5 lebih hemat biaya.
Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+
Llama 5 mendukung konteks 5M token dibandingkan 1M token pada DeepSeek V4 Pro, sementara DeepSeek V4 Pro unggul dalam kecepatan output token per detik.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
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- Gemini 3.8 Flash vs Llama 5: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- GPT-OSS 120B vs DeepSeek V4 Pro: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- GPT-OSS 120B vs Llama 5: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
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: Dokumentasi API resmi Meta & DeepSeek serta evaluasi benchmark independen (2026).