Llama 3.1 Nemotron Ultra 253B vs Llama 3.3 Nemotron Super 49B v1
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Llama 3.1 Nemotron Ultra 253B by NVIDIA and Llama 3.3 Nemotron Super 49B v1 by NVIDIA 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, Llama 3.3 Nemotron Super 49B v1 holds a notable pricing advantage for token ingestion. On output generation, Llama 3.3 Nemotron Super 49B v1 is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Llama 3.3 Nemotron Super 49B v1 provides an expanded memory horizon with 131K tokens. In latency-sensitive production environments, Llama 3.3 Nemotron Super 49B v1 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.
Cambia e Confronta Qualsiasi Modello
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Llama 3.1 Nemotron Ultra 253B
Modello a Pesi ApertiLlama 3.1 Nemotron Ultra 253B di NVIDIA è un modello di intelligenza artificiale con finestra di contesto da 128K token e tariffazione API di $0.8/1M token in input e $2.4/1M in output, ideale per coding, ragionamento e automazione aziendal
$0.80 / 1M
$2.40 / 1M
128K tokens
52.8%
Llama 3.3 Nemotron Super 49B v1
Modello a Pesi ApertiLlama 3.3 Nemotron Super 49B v1 di NVIDIA è un modello di intelligenza artificiale con finestra di contesto da 131K token e tariffazione API di $0.25/1M token in input e $0.5/1M in output, ideale per coding, ragionamento e automazione azien
$0.25 / 1M
$0.50 / 1M
131.1K tokens
40%
Differenze Chiave a Colpo d’Occhio
- Llama 3.3 Nemotron Super 49B v1 is 3.9x cheaper for API usage (input + output tokens combined).
- Llama 3.3 Nemotron Super 49B v1 has a larger context window (131K vs 128K tokens).
- Llama 3.3 Nemotron Super 49B v1 is faster at 95 tok/s vs 60 tok/s.
- Llama 3.1 Nemotron Ultra 253B leads on SWE-bench coding (52.8% vs 40.0%).
| Feature | Llama 3.1 Nemotron Ultra 253B | Llama 3.3 Nemotron Super 49B v1 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 54 | 46 |
Provider | NVIDIA | NVIDIA |
Input Price Cost per million input tokens | $0.80 / 1M | $0.25 / 1M |
Output Price Cost per million output tokens | $2.40 / 1M | $0.50 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.54 / 1M | $0.16 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 131.1K tokens~197 A4 pages of text |
Max Output Tokens Per single request | 16K | 8K |
Throughput Speed Tokens generated per second | 60 tok/s | 95 tok/s |
SWE-bench Verified Real-world software engineering | 52.8% | 40% |
GPQA Diamond Graduate-level reasoning | 66% | 56% |
Cached Input Price Prompt caching discount per 1M tokens | $0.20 / 1M | $0.08 / 1M |
Chi vince in ogni categoria?
At-a-glance decision frameworkPunteggi Benchmark Verificati
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.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
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.
Latenza e Tempo di Risposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulatore Interattivo dei Costi Token
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Llama 3.1 Nemotron Ultra 253B · Strengths & Key Advantages
- Architettura High-Throughput Aligned Frontier MoE avanzata di NVIDIA con supporto fino a 128K token di contesto
- Ottimo rapporto qualità-prezzo ($0.8/1M input, $2.4/1M output) e bassa latenza
- Elevate prestazioni nei benchmark di programmazione, logica e comprensione multilingue
Considerations & Trade-offs
- L'utilizzo intensivo di contesti molto ampi richiede un attento monitoraggio dei costi dei token
- Per casi d'uso verticali complessi è consigliabile un prompt engineering mirato
Llama 3.3 Nemotron Super 49B v1 · Strengths & Key Advantages
- Architettura Compact High-Density Transformer avanzata di NVIDIA con supporto fino a 131K token di contesto
- Ottimo rapporto qualità-prezzo ($0.25/1M input, $0.5/1M output) e bassa latenza
- Elevate prestazioni nei benchmark di programmazione, logica e comprensione multilingue
Considerations & Trade-offs
- L'utilizzo intensivo di contesti molto ampi richiede un attento monitoraggio dei costi dei token
- Per casi d'uso verticali complessi è consigliabile un prompt engineering mirato
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Llama 3.1 Nemotron Ultra 253B | Llama 3.3 Nemotron Super 49B v1 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.65 | $0.22 |
Output Tokens / Task Tokens generated per task | 42K | 30K |
Reasoning Tokens / Task Thinking tokens burned per task | 20K | 12K |
Specifiche Tecniche
Architecture, release timeline, capabilities, and modalities.
| Feature | Llama 3.1 Nemotron Ultra 253B | Llama 3.3 Nemotron Super 49B v1 |
|---|---|---|
| Official API Model ID (SDK) | llama-3-1-nemotron-ultra-253b | llama-3-3-nemotron-super-49b-v1 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-04 | 2025-04 |
| Knowledge Cutoff | December 2024 | January 2025 |
| Architecture | High-Throughput Aligned Frontier MoE | Compact High-Density Transformer |
| Parameters | 253B | 49B |
| 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 ($0.25/1M in) across full 131K window |
| Prompt Cache Policy & TTL | Cached input at $0.2/1M tokens | Cached input at $0.08/1M tokens |
| 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) · 30 GB VRAM (4-bit Quantized) · 105 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 Llama 3.3 Nemotron Super 49B v1: Risposte Rapide (FAQ)
Which is more intelligent, Llama 3.1 Nemotron Ultra 253B or Llama 3.3 Nemotron Super 49B v1?
Llama 3.1 Nemotron Ultra 253B is more intelligent. It scores 54 compared with 46 on the composite Intelligence Index.
Which model is faster?
Llama 3.3 Nemotron Super 49B v1 is faster, generating 95 tokens per second compared with 60 tokens per second.
Which model is cheaper to run?
Llama 3.3 Nemotron Super 49B v1 is cheaper at $0.16 per 1M tokens blended, compared with $0.54 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Llama 3.3 Nemotron Super 49B v1 has lower latency, with a time to first token of 0.40s compared with 0.60s.
Which model has a larger context window?
Llama 3.3 Nemotron Super 49B v1 has a larger context window, supporting 131.1K tokens compared with 128K tokens.
Verdetto
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 Llama 3.3 Nemotron Super 49B v1 if your workflow prioritizes Mid-sized enterprise deployments, agentic tool workflows, and cost-effective reasoning. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Domande Frequenti (FAQ)
How does Llama 3.1 Nemotron Ultra 253B compare to Llama 3.3 Nemotron Super 49B v1 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 Llama 3.3 Nemotron Super 49B v1 costs $0.25 input and $0.50 output. Overall, Llama 3.3 Nemotron Super 49B v1 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 Llama 3.3 Nemotron Super 49B v1?+
Llama 3.1 Nemotron Ultra 253B supports a context window of 128K tokens (~96k words), while Llama 3.3 Nemotron Super 49B v1 supports 131K tokens (~98k words). Therefore, Llama 3.3 Nemotron Super 49B v1 can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Llama 3.1 Nemotron Ultra 253B or Llama 3.3 Nemotron Super 49B v1?+
Llama 3.1 Nemotron Ultra 253B scores 52.8% on SWE-bench Verified while Llama 3.3 Nemotron Super 49B v1 scores 40%. 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 Llama 3.3 Nemotron Super 49B v1?+
Llama 3.1 Nemotron Ultra 253B features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Llama 3.3 Nemotron Super 49B v1 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 Llama 3.3 Nemotron Super 49B v1?+
Llama 3.1 Nemotron Ultra 253B generates approximately 60 tokens per second, while Llama 3.3 Nemotron Super 49B v1 delivers 95 tokens per second. In latency-sensitive production environments, Llama 3.3 Nemotron Super 49B v1 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 Llama 3.3 Nemotron Super 49B v1 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Llama 3.3 Nemotron Super 49B v1 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.
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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.
Fonti ufficiali: GetInfoUs Independent AI Benchmarks & Official API Specs