Head-to-Head Model Comparison

Nemotron 3 Nano Omni 30B A3B Reasoning vs Claude Opus 5.5

Benchmarks, API Token Pricing, Context Window & Speed Comparison

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NVIDIAAnthropicopen-sourceflagship

Compare Nemotron 3 Nano Omni 30B A3B Reasoning by NVIDIA against Claude Opus 5.5 by Anthropic across real-world coding, reasoning, token economics, and latency.

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NVIDIA

Nemotron 3 Nano Omni 30B A3B Reasoning

오픈 웨이트 모델

NVIDIA의 Nemotron 3 Nano Omni 30B A3B Reasoning 모델은 256K 토큰 컨텍스트 윈도우와 입력 $0.2/1M 토큰, 출력 $0.6/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.

Intelligence Index48 / 100
Input Token:

$0.20 / 1M

Output Token:

$0.60 / 1M

Context Window:

256K tokens

SWE-bench Verified:

42%

Anthropic

Claude Opus 5.5

심층 추론 모델

Anthropic의 Claude Opus 5.5 모델은 1M 토큰 컨텍스트 윈도우와 입력 $4.0/1M 토큰, 출력 $20.0/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.

Intelligence Index94 / 100
Input Token:

$4.00 / 1M

Output Token:

$20.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

84.6%

한눈에 보는 핵심 차이점

  • Nemotron 3 Nano Omni 30B A3B Reasoning is 26.9x cheaper for API usage (input + output tokens combined).
  • Claude Opus 5.5 has a larger context window (1M vs 256K tokens).
  • Nemotron 3 Nano Omni 30B A3B Reasoning is faster at 105 tok/s vs 78 tok/s.
  • Claude Opus 5.5 leads on SWE-bench coding (84.6% vs 42.0%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureNemotron 3 Nano Omni 30B A3B ReasoningClaude Opus 5.5
Intelligence Index
Composite reasoning & knowledge score (0-100)
4894
Provider
NVIDIAAnthropic
Input Price
Cost per million input tokens
$0.20 / 1M$4.00 / 1M
Output Price
Cost per million output tokens
$0.60 / 1M$20.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.14 / 1M$3.08 / 1M
Context Window
Maximum input + output tokens
256K tokens~384 A4 pages of text1M tokens~1,500 A4 pages of text
Max Output Tokens
Per single request
16K128K
Throughput Speed
Tokens generated per second
105 tok/s78 tok/s
SWE-bench Verified
Real-world software engineering
42%84.6%
GPQA Diamond
Graduate-level reasoning
56.5%82.3%
Cached Input Price
Prompt caching discount per 1M tokens
$0.05 / 1M$0.40 / 1M

부문별 우위 모델

At-a-glance decision framework
Coding
Claude Opus 5.5
+42.6% SWE-bench lead
Cost
Nemotron 3 Nano Omni 30B A3B Reasoning
26.9x cheaper monthly runtime
Speed
Nemotron 3 Nano Omni 30B A3B Reasoning
+35% faster generation
Context
Claude Opus 5.5
1M tokens window

검증된 벤치마크 점수 비교

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

Nemotron 3 Nano Omni 30B A3B ReasoningClaude Opus 5.5
SWE-bench Verified (Autonomous Coding)

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

42%vs84.6%
GPQA Diamond (Doctorate-Level Science & Math)

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

56.5%vs82.3%
MMLU-Pro (Multi-task Language Understanding)

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

77.2%vs91.5%
LMSYS Chatbot Arena Elo

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

1310ptsvs1507pts
Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

N/Avs19.8%
Terminal-Bench (Agentic Coding & Terminal Use)

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

36.5%vs66.4%
SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

N/Avs58.7%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs83.1%
AIME (Advanced Math Competition)

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

66%vs95.4%
Long-Context Reasoning

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

78%vs92.6%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

105 tok/s vs78 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 Nano Omni 30B A3B Reasoning0.38s
Claude Opus 5.51.40s
Est. End-to-End Response (500 tokens)TTFT + generation time
Nemotron 3 Nano Omni 30B A3B Reasoning5.14s
Claude Opus 5.57.81s

실시간 API 토큰 비용 시뮬레이터

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

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
Nemotron 3 Nano Omni 30B A3B Reasoning Monthly API Bill
$0.92 / month
$0.2/1M in · $0.6/1M out
Claude Opus 5.5 Monthly API Bill
$24.60 / month
$4/1M in · $20/1M out
Nemotron 3 Nano Omni 30B A3B Reasoning saves approximately $23.69 / month (96%) under this workload.26.9x Savings

Nemotron 3 Nano Omni 30B A3B Reasoning · Strengths & Key Advantages

  • NVIDIA의 첨단 Omnimodal Sparse MoE (30B Total / 3B Active) 아키텍처와 최대 256K 토큰 컨텍스트 지원
  • 경쟁력 있는 API 가격(입력 $0.2/1M, 출력 $0.6/1M)과 빠른 응답 속도
  • 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
  • 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
  • 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨

Claude Opus 5.5 · Strengths & Key Advantages

  • Anthropic의 첨단 Extended Autonomous Hybrid Reasoning Transformer 아키텍처와 최대 1M 토큰 컨텍스트 지원
  • 경쟁력 있는 API 가격(입력 $4.0/1M, 출력 $20.0/1M)과 빠른 응답 속도
  • 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
  • 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
  • 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

MetricNemotron 3 Nano Omni 30B A3B ReasoningClaude Opus 5.5
Cost per Task
Estimated USD to finish one task
$0.22$3.85
Output Tokens / Task
Tokens generated per task
28K68K
Reasoning Tokens / Task
Thinking tokens burned per task
12K42K

기술 사양 (Specifications)

Architecture, release timeline, capabilities, and modalities.

FeatureNemotron 3 Nano Omni 30B A3B ReasoningClaude Opus 5.5
Official API Model ID (SDK)nemotron-3-nano-omni-30b-a3bclaude-opus-5-5-20260901
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIAnthropic Messages API + AWS Bedrock + Vertex AI
Release Date2026-042026-09
Knowledge CutoffFebruary 2026June 2026
ArchitectureOmnimodal Sparse MoE (30B Total / 3B Active)Extended Autonomous Hybrid Reasoning Transformer
Parameters30B (3B active MoE)Confidential
Enterprise Rate Limits (RPM / TPM)Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)Flat rate ($0.2/1M in) across full 256K windowStandard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTLCached input at $0.05/1M tokens$0.4/1M cached read (90% off); 5-min default & 1-hour extended TTL
Computer Use / GUI AgentAPI / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)Supported (Screen coordinate grounding, browser & desktop GUI action loops)
MCP & Parallel Tool CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsNative Web Search Citations + Code Execution Tool + MCP Connectors
Official Fine-Tuning SupportFull Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)Custom Model Tuning via Amazon Bedrock & Prompt Distillation
Multimodal Token BillingText BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec)Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
Quantization & Min GPU VRAMBF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 19 GB VRAM (4-bit Quantized) · 65 GB VRAM (Native BF16)Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier ClassificationOpen-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated
Enterprise Compliance (ZDR/HIPAA/SOC2)100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 ControlSOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalitiestext, vision, audiotext, vision
Output Modalitiestexttext

Nemotron 3 Nano Omni 30B A3B Reasoning vs Claude Opus 5.5: 핵심 요약 문답 (FAQ)

Which is more intelligent, Nemotron 3 Nano Omni 30B A3B Reasoning or Claude Opus 5.5?

Claude Opus 5.5 is more intelligent. It scores 94 compared with 48 on the composite Intelligence Index.

Which model is faster?

Nemotron 3 Nano Omni 30B A3B Reasoning is faster, generating 105 tokens per second compared with 78 tokens per second.

Which model is cheaper to run?

Nemotron 3 Nano Omni 30B A3B Reasoning is cheaper at $0.14 per 1M tokens blended, compared with $3.08 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Nemotron 3 Nano Omni 30B A3B Reasoning has lower latency, with a time to first token of 0.38s compared with 1.40s.

Which model has a larger context window?

Claude Opus 5.5 has a larger context window, supporting 1M tokens compared with 256K tokens.

종합 평가

Choose Nemotron 3 Nano Omni 30B A3B Reasoning for 기업 업무 자동화, 소프트웨어 개발, 자율형 ai 에이전트 및 nvidia 기반 프로덕션 파이프라인, or choose Claude Opus 5.5 for 기업 업무 자동화, 소프트웨어 개발, 자율형 ai 에이전트 및 anthropic 기반 프로덕션 파이프라인.

자주 묻는 질문 (FAQ)

Which is cheaper: Nemotron 3 Nano Omni 30B A3B Reasoning or Claude Opus 5.5?+

Nemotron 3 Nano Omni 30B A3B Reasoning costs $0.2/1M input and $0.6/1M output tokens, while Claude Opus 5.5 costs $4/1M input and $20/1M output tokens.

Which model has a larger context window: Nemotron 3 Nano Omni 30B A3B Reasoning or Claude Opus 5.5?+

Nemotron 3 Nano Omni 30B A3B Reasoning supports 256K tokens whereas Claude Opus 5.5 supports 1M tokens.

AI Terminology & Guide
Explore Glossary →

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

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.

공식 출처: NVIDIA & Anthropic Official Documentation