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 by NVIDIA and DeepSeek-R1-0528 by DeepSeek represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, DeepSeek-R1-0528 holds a notable pricing advantage for token ingestion. On output generation, DeepSeek-R1-0528 is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical memory horizon with 128K tokens. In latency-sensitive production environments, Llama 3.1 Nemotron Ultra 253B leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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Llama 3.1 Nemotron Ultra 253B
Açık Ağırlıklı ModelNVIDIA tarafından geliştirilen Llama 3.1 Nemotron Ultra 253B, 128K token bağlam penceresi ve 1M girdi başına $0.8, 1M çıktı başına $2.4 API fiyatlandırmasıyla yazılım geliştirme, analiz ve kurumsal otomasyon için tasarlanmıştır.
$0.80 / 1M
$2.40 / 1M
128K tokens
52.8%
DeepSeek-R1-0528
Açık Ağırlıklı ModelDeepSeek tarafından geliştirilen DeepSeek-R1-0528, 128K token bağlam penceresi ve 1M girdi başına $0.55, 1M çıktı başına $2.19 API fiyatlandırmasıyla yazılım geliştirme, analiz ve kurumsal otomasyon için tasarlanmıştır.
$0.55 / 1M
$2.19 / 1M
128K tokens
51.8%
Bir Bakışta Temel Farklar
- 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 |
Hangi Alanda Kim Kazanıyor?
At-a-glance decision frameworkDoğrulanmış Benchmark Puanları
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.
Gecikme Süresi ve Yanıt Hızı
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Gerçek Zamanlı Token Maliyet Simülatörü
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Llama 3.1 Nemotron Ultra 253B · Strengths & Key Advantages
- NVIDIA imzalı gelişmiş High-Throughput Aligned Frontier MoE mimarisi ve 128K token bağlam kapasitesi
- Dengeli API maliyeti ($0.8/1M girdi, $2.4/1M çıktı) ve yüksek yanıt hızı
- Kod üretimi, mantıksal çıkarım ve çok dilli görevlerde güçlü kıyaslama (benchmark) sonuçları
Considerations & Trade-offs
- Çok uzun bağlamlı sorgularda token maliyetlerinin dikkatli yönetilmesi gerekir
- Alan bazlı özel iş akışlarında en yüksek verim için doğru istem (prompt) tasarımı önerilir
DeepSeek-R1-0528 · Strengths & Key Advantages
- DeepSeek imzalı gelişmiş Open-Weights Reasoning MoE mimarisi ve 128K token bağlam kapasitesi
- Dengeli API maliyeti ($0.55/1M girdi, $2.19/1M çıktı) ve yüksek yanıt hızı
- Kod üretimi, mantıksal çıkarım ve çok dilli görevlerde güçlü kıyaslama (benchmark) sonuçları
Considerations & Trade-offs
- Çok uzun bağlamlı sorgularda token maliyetlerinin dikkatli yönetilmesi gerekir
- Alan bazlı özel iş akışlarında en yüksek verim için doğru istem (prompt) tasarımı önerilir
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 |
Teknik Özellikler
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: Hızlı Yanıtlar (SSS)
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.
Sonuç
Verdict & Model Selection Recommendation: Choose Llama 3.1 Nemotron Ultra 253B if your priority is Enterprise datacenter intelligence, automated code migrations, and mission-critical reasoning. Its performance in demanding tasks justifies its operational token cost. Choose DeepSeek-R1-0528 if your workflow prioritizes Mathematical theorem proving, rigorous algorithm design, and privacy-compliant research. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Sıkça Sorulan Sorular (SSS)
How does Llama 3.1 Nemotron Ultra 253B compare to DeepSeek-R1-0528 in API token pricing and cost per token?+
Llama 3.1 Nemotron Ultra 253B costs $0.80 per 1M input tokens and $2.40 per 1M output tokens, whereas DeepSeek-R1-0528 costs $0.55 input and $2.19 output. Overall, DeepSeek-R1-0528 is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Llama 3.1 Nemotron Ultra 253B or DeepSeek-R1-0528?+
Both Llama 3.1 Nemotron Ultra 253B and DeepSeek-R1-0528 offer an identical context window of 128K tokens (~96k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.
Which AI model is better for software engineering and coding: Llama 3.1 Nemotron Ultra 253B or DeepSeek-R1-0528?+
Llama 3.1 Nemotron Ultra 253B scores 52.8% on SWE-bench Verified while DeepSeek-R1-0528 scores 51.8%. For autonomous software engineering and multi-file debugging, Llama 3.1 Nemotron Ultra 253B holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Llama 3.1 Nemotron Ultra 253B and DeepSeek-R1-0528?+
Llama 3.1 Nemotron Ultra 253B features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, DeepSeek-R1-0528 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Llama 3.1 Nemotron Ultra 253B and DeepSeek-R1-0528?+
Llama 3.1 Nemotron Ultra 253B generates approximately 60 tokens per second, while DeepSeek-R1-0528 delivers 48 tokens per second. In latency-sensitive production environments, Llama 3.1 Nemotron Ultra 253B provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Llama 3.1 Nemotron Ultra 253B and DeepSeek-R1-0528 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to DeepSeek-R1-0528 to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Llama 3.1 Nemotron Ultra 253B. This tiered routing pattern cuts overall API bills by up to 70%.
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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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.
Resmi kaynaklar: GetInfoUs Independent AI Benchmarks & Official API Specs