Head-to-Head Model Comparison

Nemotron Research Reasoning vs Llama 3.1 Nemotron Ultra 253B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien

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Nemotron Research ReasoningProfile

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NVIDIA

Nemotron Research Reasoning

Open-Weights-Reasoning-Modell

Nemotron Research Reasoning von NVIDIA ist ein KI-Modell für Open-Weights-Reasoning mit transparenter Gedankenketten-Verarbeitung (Chain-of-Thought) mit 128K Kontextfenster, 45 Token/s Durchsatz, einem Intelligenz-Index von 68/100 und koste

Intelligence Index68 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

48.5%

NVIDIA

Llama 3.1 Nemotron Ultra 253B

Open-Weights-Basismodell

Llama 3.1 Nemotron Ultra 253B von NVIDIA ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 128K Kontextfenster, 60 Token/s Durchsatz, einem Intelligenz-Index von 54/100 und $0.8/$2.4 pr

Intelligence Index54 / 100

Input Token:

$0.80 / 1M

Output Token:

$2.40 / 1M

Context Window:

128K tokens

SWE-bench Verified:

52.8%

Wichtigste Unterschiede auf einen Blick

Nemotron Research Reasoning is 1.0x cheaper for API usage (input + output tokens combined).
Llama 3.1 Nemotron Ultra 253B is faster at 60 tok/s vs 45 tok/s.
Llama 3.1 Nemotron Ultra 253B leads on SWE-bench coding (52.8% vs 48.5%).

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

Feature	Nemotron Research Reasoning	Llama 3.1 Nemotron Ultra 253B
Intelligence Index Composite reasoning & knowledge score (0-100)	68	54
Provider	NVIDIA	NVIDIA
Input Price Cost per million input tokens	$0.00 / 1M	$0.80 / 1M
Output Price Cost per million output tokens	$0.00 / 1M	$2.40 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.00 / 1M	$0.54 / 1M
Context Window Maximum input + output tokens	128K tokens~192 A4 pages of text	128K tokens~192 A4 pages of text
Max Output Tokens Per single request	16K	16K
Throughput Speed Tokens generated per second	45 tok/s	60 tok/s
SWE-bench Verified Real-world software engineering	48.5%	52.8%
GPQA Diamond Graduate-level reasoning	62%	66%
Cached Input Price Prompt caching discount per 1M tokens	$0.00 / 1M	$0.20 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework

Coding

Llama 3.1 Nemotron Ultra 253B

+4.3% SWE-bench lead

Cost

Nemotron Research Reasoning

1.0x cheaper monthly runtime

Speed

Llama 3.1 Nemotron Ultra 253B

+33% faster generation

Context

Tied

128K tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Nemotron Research ReasoningLlama 3.1 Nemotron Ultra 253B

SWE-bench Verified (Autonomous Coding)

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

48.5%vs52.8%

GPQA Diamond (Doctorate-Level Science & Math)

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

62%vs66%

MMLU-Pro (Multi-task Language Understanding)

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

82%vs84%

LMSYS Chatbot Arena Elo

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

1340ptsvs1362pts

Terminal-Bench (Agentic Coding & Terminal Use)

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

42%vs46.5%

AIME (Advanced Math Competition)

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

68%vs76%

Long-Context Reasoning

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

78%vs82%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

45 tok/s vs60 tok/s

Latenz & Antwortzeit

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 Research Reasoning0.52s

Llama 3.1 Nemotron Ultra 253B0.60s

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

Nemotron Research Reasoning11.63s

Llama 3.1 Nemotron Ultra 253B8.93s

Interaktiver Token-Kosten-Simulator

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 Research Reasoning Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Llama 3.1 Nemotron Ultra 253B Monthly API Bill

$3.66 / month

$0.8/1M in · $2.4/1M out

Nemotron Research Reasoning saves approximately $3.66 / month (100%) under this workload.1.0x Savings

Nemotron Research Reasoning · Strengths & Key Advantages

128K Kontextfenster und bis zu 16K maximale Output-Token (SWE-bench Verified 48.5% · GPQA Diamond 62%)
Hohe Generierungsgeschwindigkeit von 45 Token/Sekunde bei nur 520 ms Erstlatenz (TTFT)
Offene Modellgewichte (70B) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama

Considerations & Trade-offs

Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

Llama 3.1 Nemotron Ultra 253B · Strengths & Key Advantages

128K Kontextfenster und bis zu 16K maximale Output-Token (SWE-bench Verified 52.8% · GPQA Diamond 66%)
Hohe Generierungsgeschwindigkeit von 60 Token/Sekunde bei nur 600 ms Erstlatenz (TTFT)
Offene Modellgewichte (253B) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama

Considerations & Trade-offs

Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

Token Use & Task Economics

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

Metric	Nemotron Research Reasoning	Llama 3.1 Nemotron Ultra 253B
Cost per Task Estimated USD to finish one task	$0.00	$0.65
Output Tokens / Task Tokens generated per task	18K	42K
Reasoning Tokens / Task Thinking tokens burned per task	12K	20K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

Feature	Nemotron Research Reasoning	Llama 3.1 Nemotron Ultra 253B
Official API Model ID (SDK)	nemotron-research-reasoning	llama-3-1-nemotron-ultra-253b
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2025-08	2025-04
Knowledge Cutoff	July 2025	December 2024
Architecture	Research-Grade Dense Transformer	High-Throughput Aligned Frontier MoE
Parameters	70B	253B
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 ($0.8/1M in) across full 128K window
Prompt Cache Policy & TTL	Automatic prefix KV-caching in vLLM / SGLang (0 cost)	Cached input at $0.2/1M 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) · 43 GB VRAM (4-bit Quantized) · 151 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 157 GB VRAM (4-bit Quantized) · 544 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

Nemotron Research Reasoning vs Llama 3.1 Nemotron Ultra 253B: Schnelle Antworten (FAQ)

Which is more intelligent, Nemotron Research Reasoning or Llama 3.1 Nemotron Ultra 253B?

Nemotron Research Reasoning is more intelligent. It scores 68 compared with 54 on the composite Intelligence Index.

Which model is faster?

Llama 3.1 Nemotron Ultra 253B is faster, generating 60 tokens per second compared with 45 tokens per second.

Which model is cheaper to run?

Nemotron Research Reasoning is cheaper at $0.00 per 1M tokens blended, compared with $0.54 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Nemotron Research Reasoning has lower latency, with a time to first token of 0.52s compared with 0.60s.

Fazit

Sowohl Nemotron Research Reasoning (Optimal für Open-Weights-Reasoning mit transparenter Gedankenketten-Verarbeitung (Chain-of-Thought), Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Llama 3.1 Nemotron Ultra 253B (Optimal für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Nemotron Research Reasoning führt bei komplexem Reasoning und Coding, Llama 3.1 Nemotron Ultra 253B liefert den höheren Token-Durchsatz und Nemotron Research Reasoning ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Nemotron Research Reasoning oder Llama 3.1 Nemotron Ultra 253B?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Nemotron Research Reasoning vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Nemotron Research Reasoning und Llama 3.1 Nemotron Ultra 253B?+

Nemotron Research Reasoning kostet $0.00 / $0.00 pro 1M Input-/Output-Tokens, Llama 3.1 Nemotron Ultra 253B liegt bei $0.80 / $2.40. Beim gewichteten Gesamtpreis ist Nemotron Research Reasoning günstiger.

Welches Modell hat das größere Kontextfenster und die höhere Geschwindigkeit?+

Nemotron Research Reasoning bietet 128K Tokens Kontext gegenüber 128K Tokens bei Llama 3.1 Nemotron Ultra 253B. Bei der Generierungsgeschwindigkeit liegt Llama 3.1 Nemotron Ultra 253B vorn.

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 Research Reasoning vs Llama 3.1 Nemotron Ultra 253B

Beliebige Modelle wechseln & vergleichen

Nemotron Research Reasoning

Llama 3.1 Nemotron Ultra 253B

Wer gewinnt in welcher Disziplin?

Geprüfte Benchmark-Ergebnisse im Vergleich

Latenz & Antwortzeit

Interaktiver Token-Kosten-Simulator

Nemotron Research Reasoning · Strengths & Key Advantages

Considerations & Trade-offs

Llama 3.1 Nemotron Ultra 253B · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Technische Spezifikationen

Nemotron Research Reasoning vs Llama 3.1 Nemotron Ultra 253B: Schnelle Antworten (FAQ)

Fazit

Häufig gestellte Fragen (FAQ)

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