Head-to-Head Model Comparison

Nemotron 3 Ultra 550B A55B vs Qwen3.8 Max Preview

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs 연구팀 검토 완료최종 검토일: October 2026편집 정책

원하는 AI 모델 선택 및 실시간 비교

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

Nemotron 3 Ultra 550B A55BProfile

Qwen3.8 Max PreviewProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

NVIDIA

Nemotron 3 Ultra 550B A55B

오픈 웨이트 파운데이션 모델

NVIDIA의 Nemotron 3 Ultra 550B A55B 모델은 프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보에 최적화된 AI 모델로, 1M 컨텍스트 윈도우, 초당 65 토큰의 생성 속도, 종합 지능 지수 60/100점(1M 토큰당 입력 $1 / 출력 $3)을 제공합니다.

Intelligence Index60 / 100

Input Token:

$1.00 / 1M

Output Token:

$3.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

64.5%

Alibaba

Qwen3.8 Max Preview

오픈 웨이트 파운데이션 모델

Alibaba의 Qwen3.8 Max Preview 모델은 프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보에 최적화된 AI 모델로, 1M 컨텍스트 윈도우, 초당 60 토큰의 생성 속도, 종합 지능 지수 93/100점(무료 오픈 웨이트 제공)을 제공합니다.

Intelligence Index93 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

75.2%

한눈에 보는 핵심 차이점

Qwen3.8 Max Preview is 1.0x cheaper for API usage (input + output tokens combined).
Nemotron 3 Ultra 550B A55B is faster at 65 tok/s vs 60 tok/s.
Qwen3.8 Max Preview leads on SWE-bench coding (75.2% vs 64.5%).

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

Feature	Nemotron 3 Ultra 550B A55B	Qwen3.8 Max Preview
Intelligence Index Composite reasoning & knowledge score (0-100)	60	93
Provider	NVIDIA	Alibaba
Input Price Cost per million input tokens	$1.00 / 1M	$0.00 / 1M
Output Price Cost per million output tokens	$3.00 / 1M	$0.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.68 / 1M	$0.00 / 1M
Context Window Maximum input + output tokens	1M tokens~1,500 A4 pages of text	1M tokens~1,500 A4 pages of text
Max Output Tokens Per single request	66K	32K
Throughput Speed Tokens generated per second	65 tok/s	60 tok/s
SWE-bench Verified Real-world software engineering	64.5%	75.2%
GPQA Diamond Graduate-level reasoning	76%	79.5%
Cached Input Price Prompt caching discount per 1M tokens	$0.25 / 1M	$0.00 / 1M

부문별 우위 모델

At-a-glance decision framework

Coding

Qwen3.8 Max Preview

+10.7% SWE-bench lead

Cost

Qwen3.8 Max Preview

1.0x cheaper monthly runtime

Speed

Nemotron 3 Ultra 550B A55B

+8% faster generation

Context

Tied

1M tokens window

검증된 벤치마크 점수 비교

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

Nemotron 3 Ultra 550B A55BQwen3.8 Max Preview

SWE-bench Verified (Autonomous Coding)

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

64.5%vs75.2%

GPQA Diamond (Doctorate-Level Science & Math)

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

76%vs79.5%

MMLU-Pro (Multi-task Language Understanding)

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

87.5%vs89%

LMSYS Chatbot Arena Elo

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

1425ptsvs1485pts

Terminal-Bench (Agentic Coding & Terminal Use)

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

53%vsN/A

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs77%

AIME (Advanced Math Competition)

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

86%vsN/A

Long-Context Reasoning

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

89%vsN/A

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

65 tok/s vs60 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

Nemotron 3 Ultra 550B A55B0.62s

Qwen3.8 Max Preview0.68s

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

Nemotron 3 Ultra 550B A55B8.31s

Qwen3.8 Max Preview9.01s

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)

Nemotron 3 Ultra 550B A55B Monthly API Bill

$4.58 / month

$1/1M in · $3/1M out

Qwen3.8 Max Preview Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Qwen3.8 Max Preview saves approximately $4.58 / month (100%) under this workload.1.0x Savings

Nemotron 3 Ultra 550B A55B · Strengths & Key Advantages

1M 컨텍스트 윈도우 및 최대 66K 출력 토큰 생성 지원 (SWE-bench Verified 64.5% · GPQA Diamond 76%)
초당 65 토큰의 빠른 처리 속도와 620ms에 불과한 첫 토큰 지연 시간(TTFT)
오픈 웨이트 아키텍처 (550B (55B MoE))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

Qwen3.8 Max Preview · Strengths & Key Advantages

1M 컨텍스트 윈도우 및 최대 32K 출력 토큰 생성 지원 (SWE-bench Verified 75.2% · GPQA Diamond 79.5%)
초당 60 토큰의 빠른 처리 속도와 680ms에 불과한 첫 토큰 지연 시간(TTFT)
오픈 웨이트 아키텍처 (2.4T total (95B MoE))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	Nemotron 3 Ultra 550B A55B	Qwen3.8 Max Preview
Cost per Task Estimated USD to finish one task	$0.85	$0.00
Output Tokens / Task Tokens generated per task	48K	35K
Reasoning Tokens / Task Thinking tokens burned per task	24K	15K

기술 사양 (Specifications)

Architecture, release timeline, capabilities, and modalities.

Feature	Nemotron 3 Ultra 550B A55B	Qwen3.8 Max Preview
Official API Model ID (SDK)	nemotron-3-ultra-550b-a55b	qwen3-8-max-preview-instruct
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2026-06	2026-07
Knowledge Cutoff	February 2026	April 2026
Architecture	Colossal Sparse MoE (550B Total / 55B Active)	Colossus-Scale Open Sparse Mixture-of-Experts
Parameters	550B (55B active MoE)	2.4T total (95B 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 ($1/1M in) across full 1000K window	Free across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTL	Cached input at $0.25/1M tokens	Automatic prefix KV-caching in vLLM / SGLang (0 cost)
Computer Use / GUI Agent	API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)	Supported (Screen coordinate grounding, browser & desktop GUI action loops)
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) · Vision (~258–765 tokens per image tile)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 341 GB VRAM (4-bit Quantized) · 1183 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 1488 GB VRAM (4-bit Quantized) · 5160 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, vision
Output Modalities	text	text

Nemotron 3 Ultra 550B A55B vs Qwen3.8 Max Preview: 핵심 요약 문답 (FAQ)

Which is more intelligent, Nemotron 3 Ultra 550B A55B or Qwen3.8 Max Preview?

Qwen3.8 Max Preview is more intelligent. It scores 93 compared with 60 on the composite Intelligence Index.

Which model is faster?

Nemotron 3 Ultra 550B A55B is faster, generating 65 tokens per second compared with 60 tokens per second.

Which model is cheaper to run?

Qwen3.8 Max Preview is cheaper at $0.00 per 1M tokens blended, compared with $0.68 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Nemotron 3 Ultra 550B A55B has lower latency, with a time to first token of 0.62s compared with 0.68s.

종합 평가

Nemotron 3 Ultra 550B A55B(프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보, 엔터프라이즈 RAG 지식베이스 및 대규모 프로덕션 API 연동에 최적.)와 Qwen3.8 Max Preview(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 Qwen3.8 Max Preview가 앞서며, 토큰 생성 속도에서는 Nemotron 3 Ultra 550B A55B가 빠르고, 대규모 API 운영 비용 면에서는 Qwen3.8 Max Preview가 더 경제적입니다.

자주 묻는 질문 (FAQ)

코딩과 복잡한 추론에는 Nemotron 3 Ultra 550B A55B와 Qwen3.8 Max Preview 중 어느 모델이 더 나은가요?+

종합 지능 지수 및 코딩 벤치마크에서는 Qwen3.8 Max Preview가 우위를 보입니다. 상단의 SWE-bench 및 GPQA 점수를 함께 확인해 보세요.

Nemotron 3 Ultra 550B A55B와 Qwen3.8 Max Preview의 API 요금 차이는 얼마인가요?+

Nemotron 3 Ultra 550B A55B는 100만 토큰당 입력 $1.00 / 출력 $3.00이며, Qwen3.8 Max Preview는 입력 $0.00 / 출력 $0.00입니다. 혼합 비용 기준으로는 Qwen3.8 Max Preview가 더 저렴합니다.

어느 모델이 더 큰 컨텍스트 윈도우와 빠른 속도를 제공하나요?+

컨텍스트 길이는 Nemotron 3 Ultra 550B A55B가 1M 토큰, Qwen3.8 Max Preview가 1M 토큰를 지원하며, 출력 속도 면에서는 Nemotron 3 Ultra 550B A55B가 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)

Nemotron 3 Ultra 550B A55B vs Qwen3.8 Max Preview

원하는 AI 모델 선택 및 실시간 비교

Nemotron 3 Ultra 550B A55B

Qwen3.8 Max Preview

부문별 우위 모델

검증된 벤치마크 점수 비교

지연 시간(Latency) 및 응답 속도

실시간 API 토큰 비용 시뮬레이터

Nemotron 3 Ultra 550B A55B · Strengths & Key Advantages

Considerations & Trade-offs

Qwen3.8 Max Preview · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

기술 사양 (Specifications)

Nemotron 3 Ultra 550B A55B vs Qwen3.8 Max Preview: 핵심 요약 문답 (FAQ)

종합 평가

자주 묻는 질문 (FAQ)

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