Head-to-Head Model Comparison

Nemotron 3 Ultra 550B A55B vs Llama 5

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial

Ganti & Bandingkan Model Apa Saja

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

Nemotron 3 Ultra 550B A55BProfile

Llama 5Profile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

NVIDIA

Nemotron 3 Ultra 550B A55B

Model Dasar Bobot Terbuka

Nemotron 3 Ultra 550B A55B dari NVIDIA adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 1M, kecepatan 65 token/detik, indeks kecerdasan 60/100, serta t

Intelligence Index60 / 100

Input Token:

$1.00 / 1M

Output Token:

$3.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

64.5%

Llama 5

Model Dasar Bobot Terbuka

Llama 5 dari Meta adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 5M, kecepatan 65 token/detik, indeks kecerdasan 94/100, serta akses gratis open-weig

Intelligence Index94 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

5M tokens

SWE-bench Verified:

74%

Perbedaan Utama Sekilas

Llama 5 is 1.0x cheaper for API usage (input + output tokens combined).
Llama 5 has a larger context window (5M vs 1M tokens).
Llama 5 is faster at 65 tok/s vs 65 tok/s.
Llama 5 leads on SWE-bench coding (74.0% vs 64.5%).

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

Feature	Nemotron 3 Ultra 550B A55B	Llama 5
Intelligence Index Composite reasoning & knowledge score (0-100)	60	94
Provider	NVIDIA	Meta
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	5M tokens~7,500 A4 pages of text
Max Output Tokens Per single request	66K	128K
Throughput Speed Tokens generated per second	65 tok/s	65 tok/s
SWE-bench Verified Real-world software engineering	64.5%	74%
GPQA Diamond Graduate-level reasoning	76%	84%
Cached Input Price Prompt caching discount per 1M tokens	$0.25 / 1M	$0.00 / 1M

Siapa Unggul di Mana?

At-a-glance decision framework

Coding

Llama 5

+9.5% SWE-bench lead

Cost

Llama 5

1.0x cheaper monthly runtime

Speed

Llama 5

+0% faster generation

Context

Llama 5

5M tokens window

Skor Benchmark Terverifikasi

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

Nemotron 3 Ultra 550B A55BLlama 5

SWE-bench Verified (Autonomous Coding)

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

64.5%vs74%

GPQA Diamond (Doctorate-Level Science & Math)

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

76%vs84%

MMLU-Pro (Multi-task Language Understanding)

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

87.5%vs87%

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/Avs68%

AIME (Advanced Math Competition)

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

86%vs88%

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 vs65 tok/s

Latensi & Waktu Respons

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

Llama 50.85s

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

Nemotron 3 Ultra 550B A55B8.31s

Llama 58.54s

Simulator Biaya Token Interaktif

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

Llama 5 Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Llama 5 saves approximately $4.58 / month (100%) under this workload.1.0x Savings

Nemotron 3 Ultra 550B A55B · Strengths & Key Advantages

Jendela konteks 1M dengan kapasitas output maksimum hingga 66K token (SWE-bench Verified 64.5% · GPQA Diamond 76%)
Kecepatan generasi tinggi 65 token/detik dengan latensi token pertama (TTFT) hanya 620 ms
Bobot terbuka (550B (55B MoE)) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama

Considerations & Trade-offs

Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

Llama 5 · Strengths & Key Advantages

Jendela konteks 5M dengan kapasitas output maksimum hingga 128K token (SWE-bench Verified 74% · GPQA Diamond 84%)
Kecepatan generasi tinggi 65 token/detik dengan latensi token pertama (TTFT) hanya 850 ms
Bobot terbuka (600B+ total (~40-80B )) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama

Considerations & Trade-offs

Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

Token Use & Task Economics

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

Metric	Nemotron 3 Ultra 550B A55B	Llama 5
Cost per Task Estimated USD to finish one task	$0.85	$0.00
Output Tokens / Task Tokens generated per task	48K	40K
Reasoning Tokens / Task Thinking tokens burned per task	24K	20K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

Feature	Nemotron 3 Ultra 550B A55B	Llama 5
Official API Model ID (SDK)	nemotron-3-ultra-550b-a55b	meta-llama/Llama-5
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2026-06	2026-04
Knowledge Cutoff	February 2026	January 2026
Architecture	Colossal Sparse MoE (550B Total / 55B Active)	Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active)
Parameters	550B (55B active MoE)	600B+ total (~40-80B active)
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) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec)
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) · 372 GB VRAM (4-bit Quantized) · 1290 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, video, audio
Output Modalities	text	text

Nemotron 3 Ultra 550B A55B vs Llama 5: Jawaban Cepat (FAQ)

Which is more intelligent, Nemotron 3 Ultra 550B A55B or Llama 5?

Llama 5 is more intelligent. It scores 94 compared with 60 on the composite Intelligence Index.

Which model is cheaper to run?

Llama 5 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 0.85s.

Which model has a larger context window?

Llama 5 has a larger context window, supporting 5M tokens compared with 1M tokens.

Kesimpulan

Baik Nemotron 3 Ultra 550B A55B (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun Llama 5 (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Llama 5 unggul dalam penalaran dan pemrograman, Nemotron 3 Ultra 550B A55B memberikan kecepatan generasi token lebih tinggi, dan Llama 5 lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, Nemotron 3 Ultra 550B A55B atau Llama 5?+

Dalam indeks kecerdasan gabungan dan benchmark coding, Llama 5 memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API Nemotron 3 Ultra 550B A55B vs Llama 5?+

Nemotron 3 Ultra 550B A55B mengenakan biaya $1.00 / $3.00 per 1M token input/output, sedangkan Llama 5 sebesar $0.00 / $0.00. Secara keseluruhan, Llama 5 lebih hemat biaya.

Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+

Nemotron 3 Ultra 550B A55B mendukung konteks 1M token dibandingkan 5M token pada Llama 5, sementara Nemotron 3 Ultra 550B A55B unggul dalam kecepatan output token per detik.

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 Llama 5

Ganti & Bandingkan Model Apa Saja

Nemotron 3 Ultra 550B A55B

Llama 5

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

Nemotron 3 Ultra 550B A55B · Strengths & Key Advantages

Considerations & Trade-offs

Llama 5 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Spesifikasi Teknis

Nemotron 3 Ultra 550B A55B vs Llama 5: Jawaban Cepat (FAQ)

Kesimpulan

Pertanyaan Umum (FAQ)

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