Head-to-Head Model Comparison

Llama 3.1 Nemotron Ultra 253B vs Llama 3.3 Nemotron Super 49B v1

Benchmarks, API Token Pricing, Context Window & Speed Comparison

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Llama 3.1 Nemotron Ultra 253BProfile

Llama 3.3 Nemotron Super 49B v1Profile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

NVIDIA

Llama 3.1 Nemotron Ultra 253B

오픈 웨이트 파운데이션 모델

NVIDIA의 Llama 3.1 Nemotron Ultra 253B 모델은 프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보에 최적화된 AI 모델로, 128K 컨텍스트 윈도우, 초당 60 토큰의 생성 속도, 종합 지능 지수 54/100점(1M 토큰당 입력 $0.8 / 출력 $2.4)을 제공합니다.

Intelligence Index54 / 100

Input Token:

$0.80 / 1M

Output Token:

$2.40 / 1M

Context Window:

128K tokens

SWE-bench Verified:

52.8%

NVIDIA

Llama 3.3 Nemotron Super 49B v1

오픈 웨이트 파운데이션 모델

NVIDIA의 Llama 3.3 Nemotron Super 49B v1 모델은 프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보에 최적화된 AI 모델로, 131K 컨텍스트 윈도우, 초당 95 토큰의 생성 속도, 종합 지능 지수 46/100점(1M 토큰당 입력 $0.25 / 출력 $0.5)을 제공합니다.

Intelligence Index46 / 100

Input Token:

$0.25 / 1M

Output Token:

$0.50 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

40%

한눈에 보는 핵심 차이점

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%).

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

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

부문별 우위 모델

At-a-glance decision framework

Coding

Llama 3.1 Nemotron Ultra 253B

+12.8% SWE-bench lead

Cost

Llama 3.3 Nemotron Super 49B v1

3.9x cheaper monthly runtime

Speed

Llama 3.3 Nemotron Super 49B v1

+58% faster generation

Context

Llama 3.3 Nemotron Super 49B v1

131.1K tokens window

검증된 벤치마크 점수 비교

Official evaluation metrics verified by independent third-party research labs.

Llama 3.1 Nemotron Ultra 253BLlama 3.3 Nemotron Super 49B v1

SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

52.8%vs40%

GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

66%vs56%

MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

84%vs78%

LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1362ptsvs1305pts

Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

46.5%vs36%

AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

76%vs66%

Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

82%vs74%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

60 tok/s vs95 tok/s

지연 시간(Latency) 및 응답 속도

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better

Llama 3.1 Nemotron Ultra 253B0.60s

Llama 3.3 Nemotron Super 49B v10.40s

Est. End-to-End Response (500 tokens)TTFT + generation time

Llama 3.1 Nemotron Ultra 253B8.93s

Llama 3.3 Nemotron Super 49B v15.66s

실시간 API 토큰 비용 시뮬레이터

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% cached

0% (Cold)50%90% (Warm)

Llama 3.1 Nemotron Ultra 253B Monthly API Bill

$3.66 / month

$0.8/1M in · $2.4/1M out

Llama 3.3 Nemotron Super 49B v1 Monthly API Bill

$0.95 / month

$0.25/1M in · $0.5/1M out

Llama 3.3 Nemotron Super 49B v1 saves approximately $2.71 / month (74%) under this workload.3.9x Savings

Llama 3.1 Nemotron Ultra 253B · Strengths & Key Advantages

128K 컨텍스트 윈도우 및 최대 16K 출력 토큰 생성 지원 (SWE-bench Verified 52.8% · GPQA Diamond 66%)
초당 60 토큰의 빠른 처리 속도와 600ms에 불과한 첫 토큰 지연 시간(TTFT)
오픈 웨이트 아키텍처 (253B)를 통해 vLLM, SGLang, Ollama 기반 자체 GPU 서버 구축 및 파인튜닝 가능

Considerations & Trade-offs

비양자화 전체 정밀도로 로컬 서버에서 구동하려면 충분한 GPU VRAM 용량이 필요합니다
초고난도 학술 연구 및 수학 증명 과제에서는 상위 대형 추론 특화 모델이 더 깊은 분석을 제공할 수 있습니다

Llama 3.3 Nemotron Super 49B v1 · Strengths & Key Advantages

131K 컨텍스트 윈도우 및 최대 8K 출력 토큰 생성 지원 (SWE-bench Verified 40% · GPQA Diamond 56%)
초당 95 토큰의 빠른 처리 속도와 400ms에 불과한 첫 토큰 지연 시간(TTFT)
오픈 웨이트 아키텍처 (49B)를 통해 vLLM, SGLang, Ollama 기반 자체 GPU 서버 구축 및 파인튜닝 가능

Considerations & Trade-offs

비양자화 전체 정밀도로 로컬 서버에서 구동하려면 충분한 GPU VRAM 용량이 필요합니다
초고난도 학술 연구 및 수학 증명 과제에서는 상위 대형 추론 특화 모델이 더 깊은 분석을 제공할 수 있습니다

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

기술 사양 (Specifications)

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: 핵심 요약 문답 (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.

종합 평가

Llama 3.1 Nemotron Ultra 253B(프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보, 엔터프라이즈 RAG 지식베이스 및 대규모 프로덕션 API 연동에 최적.)와 Llama 3.3 Nemotron Super 49B v1(프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보, 엔터프라이즈 RAG 지식베이스 및 대규모 프로덕션 API 연동에 최적.) 모두 뛰어난 성능을 자랑합니다. 고난도 추론과 코딩 벤치마크에서는 Llama 3.1 Nemotron Ultra 253B가 앞서며, 토큰 생성 속도에서는 Llama 3.3 Nemotron Super 49B v1가 빠르고, 대규모 API 운영 비용 면에서는 Llama 3.3 Nemotron Super 49B v1가 더 경제적입니다.

자주 묻는 질문 (FAQ)

코딩과 복잡한 추론에는 Llama 3.1 Nemotron Ultra 253B와 Llama 3.3 Nemotron Super 49B v1 중 어느 모델이 더 나은가요?+

종합 지능 지수 및 코딩 벤치마크에서는 Llama 3.1 Nemotron Ultra 253B가 우위를 보입니다. 상단의 SWE-bench 및 GPQA 점수를 함께 확인해 보세요.

Llama 3.1 Nemotron Ultra 253B와 Llama 3.3 Nemotron Super 49B v1의 API 요금 차이는 얼마인가요?+

Llama 3.1 Nemotron Ultra 253B는 100만 토큰당 입력 $0.80 / 출력 $2.40이며, Llama 3.3 Nemotron Super 49B v1는 입력 $0.25 / 출력 $0.50입니다. 혼합 비용 기준으로는 Llama 3.3 Nemotron Super 49B v1가 더 저렴합니다.

어느 모델이 더 큰 컨텍스트 윈도우와 빠른 속도를 제공하나요?+

컨텍스트 길이는 Llama 3.1 Nemotron Ultra 253B가 128K 토큰, Llama 3.3 Nemotron Super 49B v1가 131.1K 토큰를 지원하며, 출력 속도 면에서는 Llama 3.3 Nemotron Super 49B v1가 더 빠릅니다.

AI Terminology & Guide

Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

Llama 3.1 Nemotron Ultra 253B vs Llama 3.3 Nemotron Super 49B v1

원하는 AI 모델 선택 및 실시간 비교

Llama 3.1 Nemotron Ultra 253B

Llama 3.3 Nemotron Super 49B v1

부문별 우위 모델

검증된 벤치마크 점수 비교

지연 시간(Latency) 및 응답 속도

실시간 API 토큰 비용 시뮬레이터

Llama 3.1 Nemotron Ultra 253B · Strengths & Key Advantages

Considerations & Trade-offs

Llama 3.3 Nemotron Super 49B v1 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

기술 사양 (Specifications)

Llama 3.1 Nemotron Ultra 253B vs Llama 3.3 Nemotron Super 49B v1: 핵심 요약 문답 (FAQ)

종합 평가

자주 묻는 질문 (FAQ)

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