Llama 3.1 Nemotron Ultra 253B vs DeepSeek-R1-0528
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Llama 3.1 Nemotron Ultra 253B dari NVIDIA dan DeepSeek-R1-0528 dari DeepSeek adalah dua model AI unggulan. Llama 3.1 Nemotron Ultra 253B menawarkan jendela konteks 128K token dengan harga $0.80/$2.40 per 1 juta token, sedangkan DeepSeek-R1-0528 menyediakan konteks 128K token seharga $0.55/$2.19 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.
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Llama 3.1 Nemotron Ultra 253B
Model Dasar Bobot TerbukaLlama 3.1 Nemotron Ultra 253B dari NVIDIA adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 128K, kecepatan 60 token/detik, indeks kecerdasan 54/100, se
$0.80 / 1M
$2.40 / 1M
128K tokens
52.8%
DeepSeek-R1-0528
Penalaran Bobot TerbukaDeepSeek-R1-0528 dari DeepSeek adalah model AI yang dirancang untuk penalaran rantai pemikiran (Chain-of-Thought) berbobot terbuka yang sangat efisien, menghadirkan jendela konteks 128K, kecepatan 48 token/detik, indeks kecerdasan 52/100, s
$0.55 / 1M
$2.19 / 1M
128K tokens
51.8%
Perbedaan Utama Sekilas
- DeepSeek-R1-0528 is 1.2x cheaper for API usage (input + output tokens combined).
- Llama 3.1 Nemotron Ultra 253B is faster at 60 tok/s vs 48 tok/s.
- Llama 3.1 Nemotron Ultra 253B leads on SWE-bench coding (52.8% vs 51.8%).
| Feature | Llama 3.1 Nemotron Ultra 253B | DeepSeek-R1-0528 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 54 | 52 |
Provider | NVIDIA | DeepSeek |
Input Price Cost per million input tokens | $0.80 / 1M | $0.55 / 1M |
Output Price Cost per million output tokens | $2.40 / 1M | $2.19 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.54 / 1M | $0.43 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 128K tokens~192 A4 pages of text |
Max Output Tokens Per single request | 16K | 8K |
Throughput Speed Tokens generated per second | 60 tok/s | 48 tok/s |
SWE-bench Verified Real-world software engineering | 52.8% | 51.8% |
GPQA Diamond Graduate-level reasoning | 66% | 74.2% |
Cached Input Price Prompt caching discount per 1M tokens | $0.20 / 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 3.1 Nemotron Ultra 253B · Strengths & Key Advantages
- Jendela konteks 128K dengan kapasitas output maksimum hingga 16K token (SWE-bench Verified 52.8% · GPQA Diamond 66%)
- Kecepatan generasi tinggi 60 token/detik dengan latensi token pertama (TTFT) hanya 600 ms
- Bobot terbuka (253B) 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-R1-0528 · Strengths & Key Advantages
- Jendela konteks 128K dengan kapasitas output maksimum hingga 8K token (SWE-bench Verified 51.8% · GPQA Diamond 74.2%)
- Kecepatan generasi tinggi 48 token/detik dengan latensi token pertama (TTFT) hanya 900 ms
- Bobot terbuka (671B (37B MoE)) 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 3.1 Nemotron Ultra 253B | DeepSeek-R1-0528 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.65 | $0.62 |
Output Tokens / Task Tokens generated per task | 42K | 45K |
Reasoning Tokens / Task Thinking tokens burned per task | 20K | 25K |
Spesifikasi Teknis
Architecture, release timeline, capabilities, and modalities.
| Feature | Llama 3.1 Nemotron Ultra 253B | DeepSeek-R1-0528 |
|---|---|---|
| Official API Model ID (SDK) | llama-3-1-nemotron-ultra-253b | deepseek-reasoner |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-04 | 2025-05 |
| Knowledge Cutoff | December 2024 | March 2025 |
| Architecture | High-Throughput Aligned Frontier MoE | Open-Weights Reasoning MoE |
| Parameters | 253B | 671B (37B active MoE) |
| 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 ($0.8/1M in) across full 128K window | Flat rate ($0.55/1M in) across full 128K window |
| Prompt Cache Policy & TTL | Cached input at $0.2/1M tokens | Automatic prefix caching at $0.14/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | 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) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 157 GB VRAM (4-bit Quantized) · 544 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 416 GB VRAM (4-bit Quantized) · 1443 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 | text |
| Output Modalities | text | text |
Llama 3.1 Nemotron Ultra 253B vs DeepSeek-R1-0528: Jawaban Cepat (FAQ)
Which is more intelligent, Llama 3.1 Nemotron Ultra 253B or DeepSeek-R1-0528?
Llama 3.1 Nemotron Ultra 253B is more intelligent. It scores 54 compared with 52 on the composite Intelligence Index.
Which model is faster?
Llama 3.1 Nemotron Ultra 253B is faster, generating 60 tokens per second compared with 48 tokens per second.
Which model is cheaper to run?
DeepSeek-R1-0528 is cheaper at $0.43 per 1M tokens blended, compared with $0.54 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Llama 3.1 Nemotron Ultra 253B has lower latency, with a time to first token of 0.60s compared with 0.90s.
Kesimpulan
Baik Llama 3.1 Nemotron Ultra 253B (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun DeepSeek-R1-0528 (Sangat cocok untuk penalaran rantai pemikiran (Chain-of-Thought) berbobot terbuka yang sangat efisien, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Llama 3.1 Nemotron Ultra 253B unggul dalam penalaran dan pemrograman, Llama 3.1 Nemotron Ultra 253B memberikan kecepatan generasi token lebih tinggi, dan DeepSeek-R1-0528 lebih hemat biaya untuk penggunaan API skala besar.
Pertanyaan Umum (FAQ)
Mana yang lebih baik untuk coding dan penalaran, Llama 3.1 Nemotron Ultra 253B atau DeepSeek-R1-0528?+
Dalam indeks kecerdasan gabungan dan benchmark coding, Llama 3.1 Nemotron Ultra 253B memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.
Berapa perbandingan harga API Llama 3.1 Nemotron Ultra 253B vs DeepSeek-R1-0528?+
Llama 3.1 Nemotron Ultra 253B mengenakan biaya $0.80 / $2.40 per 1M token input/output, sedangkan DeepSeek-R1-0528 sebesar $0.55 / $2.19. Secara keseluruhan, DeepSeek-R1-0528 lebih hemat biaya.
Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+
Llama 3.1 Nemotron Ultra 253B mendukung konteks 128K token dibandingkan 128K token pada DeepSeek-R1-0528, sementara Llama 3.1 Nemotron Ultra 253B 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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- Nemotron Research Reasoning vs Llama 3.1 Nemotron Ultra 253B: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
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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: Dokumentasi API resmi NVIDIA & DeepSeek serta evaluasi benchmark independen (2026).