Head-to-Head Model Comparison

Llama 4 Behemoth vs Nemotron 3 Ultra 550B A55B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs araştırma ekibi tarafından incelendiSon inceleme: October 2026Yayın ilkeleri

Paylaş

İstediğiniz Modelleri Seçin ve Karşılaştırın

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

Llama 4 BehemothProfile

Nemotron 3 Ultra 550B A55BProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Llama 4 Behemoth

Açık Ağırlıklı Temel Model

Meta tarafından geliştirilen Llama 4 Behemoth; özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği için tasarlanmış olup 10M bağlam penceresi, 45 token/sn hız, 93/100 zeka indeksi ve ücretsiz açık ağırlık

Intelligence Index93 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

10M tokens

SWE-bench Verified:

75%

NVIDIA

Nemotron 3 Ultra 550B A55B

Açık Ağırlıklı Temel Model

NVIDIA tarafından geliştirilen Nemotron 3 Ultra 550B A55B; özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği için tasarlanmış olup 1M bağlam penceresi, 65 token/sn hız, 60/100 zeka indeksi ve 1M token b

Intelligence Index60 / 100

Input Token:

$1.00 / 1M

Output Token:

$3.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

64.5%

Bir Bakışta Temel Farklar

Llama 4 Behemoth is 1.0x cheaper for API usage (input + output tokens combined).
Llama 4 Behemoth has a larger context window (10M vs 1M tokens).
Nemotron 3 Ultra 550B A55B is faster at 65 tok/s vs 45 tok/s.
Llama 4 Behemoth leads on SWE-bench coding (75.0% vs 64.5%).

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

Feature	Llama 4 Behemoth	Nemotron 3 Ultra 550B A55B
Intelligence Index Composite reasoning & knowledge score (0-100)	93	60
Provider	Meta	NVIDIA
Input Price Cost per million input tokens	$0.00 / 1M	$1.00 / 1M
Output Price Cost per million output tokens	$0.00 / 1M	$3.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.00 / 1M	$0.68 / 1M
Context Window Maximum input + output tokens	10M tokens~15,000 A4 pages of text	1M tokens~1,500 A4 pages of text
Max Output Tokens Per single request	64K	66K
Throughput Speed Tokens generated per second	45 tok/s	65 tok/s
SWE-bench Verified Real-world software engineering	75%	64.5%
GPQA Diamond Graduate-level reasoning	82%	76%
Cached Input Price Prompt caching discount per 1M tokens	$0.00 / 1M	$0.25 / 1M

Hangi Alanda Kim Kazanıyor?

At-a-glance decision framework

Coding

Llama 4 Behemoth

+10.5% SWE-bench lead

Cost

Llama 4 Behemoth

1.0x cheaper monthly runtime

Speed

Nemotron 3 Ultra 550B A55B

+44% faster generation

Context

Llama 4 Behemoth

10M tokens window

Doğrulanmış Benchmark Puanları

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

Llama 4 BehemothNemotron 3 Ultra 550B A55B

SWE-bench Verified (Autonomous Coding)

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

75%vs64.5%

GPQA Diamond (Doctorate-Level Science & Math)

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

82%vs76%

MMLU-Pro (Multi-task Language Understanding)

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

88.5%vs87.5%

LMSYS Chatbot Arena Elo

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

1490ptsvs1425pts

Terminal-Bench (Agentic Coding & Terminal Use)

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

N/Avs53%

AIME (Advanced Math Competition)

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

N/Avs86%

Long-Context Reasoning

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

95%vs89%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

45 tok/s vs65 tok/s

Gecikme Süresi ve Yanıt Hızı

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 4 Behemoth1.20s

Nemotron 3 Ultra 550B A55B0.62s

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

Llama 4 Behemoth12.31s

Nemotron 3 Ultra 550B A55B8.31s

Gerçek Zamanlı Token Maliyet Simülatörü

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 4 Behemoth Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Nemotron 3 Ultra 550B A55B Monthly API Bill

$4.58 / month

$1/1M in · $3/1M out

Llama 4 Behemoth saves approximately $4.58 / month (100%) under this workload.1.0x Savings

Llama 4 Behemoth · Strengths & Key Advantages

10M bağlam penceresi ve yanıt başına maksimum 64K çıktı tokenı kapasitesi (SWE-bench Verified 75% · GPQA Diamond 82%)
Sadece 1200 ms ilk token gecikmesi (TTFT) ile saniyede 45 token yüksek üretim hızı
vLLM, SGLang veya Ollama üzerinden yerel GPU sunucularında barındırılabilen açık ağırlıklı mimari (2T total / 288B (16-of-2 MoE))

Considerations & Trade-offs

Tam hassasiyette yerel GPU çıkarımı için yeterli VRAM kapasitesine sahip donanım altyapısı gerektirir
Çok karmaşık akademik ve bilimsel çıkarımlarda üst düzey ağır akıl yürütme modelleri daha derin sonuçlar verebilir

Nemotron 3 Ultra 550B A55B · Strengths & Key Advantages

1M bağlam penceresi ve yanıt başına maksimum 66K çıktı tokenı kapasitesi (SWE-bench Verified 64.5% · GPQA Diamond 76%)
Sadece 620 ms ilk token gecikmesi (TTFT) ile saniyede 65 token yüksek üretim hızı
vLLM, SGLang veya Ollama üzerinden yerel GPU sunucularında barındırılabilen açık ağırlıklı mimari (550B (55B MoE))

Considerations & Trade-offs

Tam hassasiyette yerel GPU çıkarımı için yeterli VRAM kapasitesine sahip donanım altyapısı gerektirir
Çok karmaşık akademik ve bilimsel çıkarımlarda üst düzey ağır akıl yürütme modelleri daha derin sonuçlar verebilir

Token Use & Task Economics

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

Metric	Llama 4 Behemoth	Nemotron 3 Ultra 550B A55B
Cost per Task Estimated USD to finish one task	$0.00	$0.85
Output Tokens / Task Tokens generated per task	35K	48K
Reasoning Tokens / Task Thinking tokens burned per task	15K	24K

Teknik Özellikler

Architecture, release timeline, capabilities, and modalities.

Feature	Llama 4 Behemoth	Nemotron 3 Ultra 550B A55B
Official API Model ID (SDK)	meta-llama/Llama-4-Behemoth	nemotron-3-ultra-550b-a55b
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2026-05	2026-06
Knowledge Cutoff	February 2026	February 2026
Architecture	Colossal sparse MoE architecture (288B active / 2T total, 16-of-2 MoE)	Colossal Sparse MoE (550B Total / 55B Active)
Parameters	2T total / 288B active (16-of-2 MoE)	550B (55B 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)	Free across full context window (only KV-cache VRAM scales)	Flat rate ($1/1M in) across full 1000K window
Prompt Cache Policy & TTL	Automatic prefix KV-caching in vLLM / SGLang (0 cost)	Cached input at $0.25/1M tokens
Computer Use / GUI Agent	Supported (Screen coordinate grounding, browser & desktop GUI action loops)	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) · Vision (~258–765 tokens per image tile)	Text BPE (~4 chars/token)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 1240 GB VRAM (4-bit Quantized) · 4300 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 341 GB VRAM (4-bit Quantized) · 1183 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, vision	text
Output Modalities	text	text

Llama 4 Behemoth vs Nemotron 3 Ultra 550B A55B: Hızlı Yanıtlar (SSS)

Which is more intelligent, Llama 4 Behemoth or Nemotron 3 Ultra 550B A55B?

Llama 4 Behemoth 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 45 tokens per second.

Which model is cheaper to run?

Llama 4 Behemoth 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 1.20s.

Which model has a larger context window?

Llama 4 Behemoth has a larger context window, supporting 10M tokens compared with 1M tokens.

Sonuç

Hem Llama 4 Behemoth (özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği, kurumsal RAG mimarileri ve ölçeklenebilir üretim API entegrasyonları için idealdir.) hem de Nemotron 3 Ultra 550B A55B (özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği, kurumsal RAG mimarileri ve ölçeklenebilir üretim API entegrasyonları için idealdir.) güçlü üretim seçenekleridir: Llama 4 Behemoth karmaşık akıl yürütme ve kodlama testlerinde öne çıkarken, Nemotron 3 Ultra 550B A55B daha yüksek üretim hızı sağlar; Llama 4 Behemoth ise yüksek hacimli API kullanımlarında daha ekonomiktir.

Sıkça Sorulan Sorular (SSS)

Kodlama ve akıl yürütme için Llama 4 Behemoth mi yoksa Nemotron 3 Ultra 550B A55B mi daha iyi?+

Bileşik zeka endeksi ve yazılım testlerinde Llama 4 Behemoth önde yer alıyor. Detaylı karşılaştırma için yukarıdaki SWE-bench ve GPQA tablolarına göz atabilirsiniz.

Llama 4 Behemoth ve Nemotron 3 Ultra 550B A55B API fiyatları nasıl karşılaştırılır?+

Llama 4 Behemoth 1M giriş/çıkış tokeni başına $0.00 / $0.00 ücretlendirilirken, Nemotron 3 Ultra 550B A55B $1.00 / $3.00 seviyesindedir. Karma maliyette Llama 4 Behemoth daha avantajlıdır.

Hangi modelin bağlam penceresi daha geniş ve hızı daha yüksek?+

Llama 4 Behemoth 10M token bağlam penceresi sunarken Nemotron 3 Ultra 550B A55B 1M token kapasiteye sahiptir. Saniyedeki token üretim hızında ise Nemotron 3 Ultra 550B A55B öndedir.

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 4 Behemoth vs Nemotron 3 Ultra 550B A55B

İstediğiniz Modelleri Seçin ve Karşılaştırın

Llama 4 Behemoth

Nemotron 3 Ultra 550B A55B

Hangi Alanda Kim Kazanıyor?

Doğrulanmış Benchmark Puanları

Gecikme Süresi ve Yanıt Hızı

Gerçek Zamanlı Token Maliyet Simülatörü

Llama 4 Behemoth · Strengths & Key Advantages

Considerations & Trade-offs

Nemotron 3 Ultra 550B A55B · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Teknik Özellikler

Llama 4 Behemoth vs Nemotron 3 Ultra 550B A55B: Hızlı Yanıtlar (SSS)

Sonuç

Sıkça Sorulan Sorular (SSS)

Related reading