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Head-to-Head Model Comparison

Llama 3.1 Nemotron Ultra 253B vs Llama 3.3 Nemotron Super 49B v1

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Llama 3.1 Nemotron Ultra 253B vs Llama 3.3 Nemotron Super 49B v1: Benchmarks, Pricing & Speed Comparison

Llama 3.1 Nemotron Ultra 253B von NVIDIA und Llama 3.3 Nemotron Super 49B v1 von NVIDIA gehören zu den führenden KI-Modellen. Llama 3.1 Nemotron Ultra 253B bietet ein Kontextfenster von 128K Tokens bei API-Preisen von $0.80/$2.40 pro 1M Tokens, während Llama 3.3 Nemotron Super 49B v1 131.1K Tokens Kontext zu $0.25/$0.50 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.

Direktvergleich (VS)

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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%

NVIDIA

Llama 3.3 Nemotron Super 49B v1

Open-Weights-Basismodell

Llama 3.3 Nemotron Super 49B v1 von NVIDIA ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 131K Kontextfenster, 95 Token/s Durchsatz, einem Intelligenz-Index von 46/100 und $0.25/$0.5

Intelligence Index46 / 100
Input Token:

$0.25 / 1M

Output Token:

$0.50 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

40%

Wichtigste Unterschiede auf einen Blick

  • Llama 3.3 Nemotron Super 49B v1 is 3.9x cheaper for API usage (input + output tokens combined).
  • Llama 3.3 Nemotron Super 49B v1 has a larger context window (131K vs 128K tokens).
  • Llama 3.3 Nemotron Super 49B v1 is faster at 95 tok/s vs 60 tok/s.
  • Llama 3.1 Nemotron Ultra 253B leads on SWE-bench coding (52.8% vs 40.0%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureLlama 3.1 Nemotron Ultra 253BLlama 3.3 Nemotron Super 49B v1
Intelligence Index
Composite reasoning & knowledge score (0-100)
5446
Provider
NVIDIANVIDIA
Input Price
Cost per million input tokens
$0.80 / 1M$0.25 / 1M
Output Price
Cost per million output tokens
$2.40 / 1M$0.50 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.54 / 1M$0.16 / 1M
Context Window
Maximum input + output tokens
128K tokens~192 A4 pages of text131.1K tokens~197 A4 pages of text
Max Output Tokens
Per single request
16K8K
Throughput Speed
Tokens generated per second
60 tok/s95 tok/s
SWE-bench Verified
Real-world software engineering
52.8%40%
GPQA Diamond
Graduate-level reasoning
66%56%
Cached Input Price
Prompt caching discount per 1M tokens
$0.20 / 1M$0.08 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework
Coding
Llama 3.1 Nemotron Ultra 253B
+12.8% SWE-bench lead
Cost
Llama 3.3 Nemotron Super 49B v1
3.9x cheaper monthly runtime
Speed
Llama 3.3 Nemotron Super 49B v1
+58% faster generation
Context
Llama 3.3 Nemotron Super 49B v1
131.1K tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Llama 3.1 Nemotron Ultra 253BLlama 3.3 Nemotron Super 49B v1
SWE-bench Verified (Autonomous Coding)

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

52.8%vs40%
GPQA Diamond (Doctorate-Level Science & Math)

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

66%vs56%
MMLU-Pro (Multi-task Language Understanding)

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

84%vs78%
LMSYS Chatbot Arena Elo

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

1362ptsvs1305pts
Terminal-Bench (Agentic Coding & Terminal Use)

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

46.5%vs36%
AIME (Advanced Math Competition)

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

76%vs66%
Long-Context Reasoning

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

82%vs74%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

60 tok/s vs95 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
Llama 3.1 Nemotron Ultra 253B0.60s
Llama 3.3 Nemotron Super 49B v10.40s
Est. End-to-End Response (500 tokens)TTFT + generation time
Llama 3.1 Nemotron Ultra 253B8.93s
Llama 3.3 Nemotron Super 49B v15.66s

Interaktiver Token-Kosten-Simulator

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)
Llama 3.1 Nemotron Ultra 253B Monthly API Bill
$3.66 / month
$0.8/1M in · $2.4/1M out
Llama 3.3 Nemotron Super 49B v1 Monthly API Bill
$0.95 / month
$0.25/1M in · $0.5/1M out
Llama 3.3 Nemotron Super 49B v1 saves approximately $2.71 / month (74%) under this workload.3.9x Savings

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

Llama 3.3 Nemotron Super 49B v1 · Strengths & Key Advantages

  • 131K Kontextfenster und bis zu 8K maximale Output-Token (SWE-bench Verified 40% · GPQA Diamond 56%)
  • Hohe Generierungsgeschwindigkeit von 95 Token/Sekunde bei nur 400 ms Erstlatenz (TTFT)
  • Offene Modellgewichte (49B) 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.

MetricLlama 3.1 Nemotron Ultra 253BLlama 3.3 Nemotron Super 49B v1
Cost per Task
Estimated USD to finish one task
$0.65$0.22
Output Tokens / Task
Tokens generated per task
42K30K
Reasoning Tokens / Task
Thinking tokens burned per task
20K12K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

FeatureLlama 3.1 Nemotron Ultra 253BLlama 3.3 Nemotron Super 49B v1
Official API Model ID (SDK)llama-3-1-nemotron-ultra-253bllama-3-3-nemotron-super-49b-v1
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2025-042025-04
Knowledge CutoffDecember 2024January 2025
ArchitectureHigh-Throughput Aligned Frontier MoECompact High-Density Transformer
Parameters253B49B
Enterprise Rate Limits (RPM / TPM)Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed CloudUnlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)Flat rate ($0.8/1M in) across full 128K windowFlat rate ($0.25/1M in) across full 131K window
Prompt Cache Policy & TTLCached input at $0.2/1M tokensCached input at $0.08/1M tokens
Computer Use / GUI AgentAPI / 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 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 toolsSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportFull 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 BillingText BPE (~4 chars/token)Text BPE (~4 chars/token)
Quantization & Min GPU VRAMBF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 157 GB VRAM (4-bit Quantized) · 544 GB VRAM (Native BF16)BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 30 GB VRAM (4-bit Quantized) · 105 GB VRAM (Native BF16)
Safety & Frontier ClassificationOpen-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 Control100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestexttext
Output Modalitiestexttext

Llama 3.1 Nemotron Ultra 253B vs Llama 3.3 Nemotron Super 49B v1: Schnelle Antworten (FAQ)

Which is more intelligent, Llama 3.1 Nemotron Ultra 253B or Llama 3.3 Nemotron Super 49B v1?

Llama 3.1 Nemotron Ultra 253B is more intelligent. It scores 54 compared with 46 on the composite Intelligence Index.

Which model is faster?

Llama 3.3 Nemotron Super 49B v1 is faster, generating 95 tokens per second compared with 60 tokens per second.

Which model is cheaper to run?

Llama 3.3 Nemotron Super 49B v1 is cheaper at $0.16 per 1M tokens blended, compared with $0.54 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Llama 3.3 Nemotron Super 49B v1 has lower latency, with a time to first token of 0.40s compared with 0.60s.

Which model has a larger context window?

Llama 3.3 Nemotron Super 49B v1 has a larger context window, supporting 131.1K tokens compared with 128K tokens.

Fazit

Sowohl 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.) als auch Llama 3.3 Nemotron Super 49B v1 (Optimal für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Llama 3.1 Nemotron Ultra 253B führt bei komplexem Reasoning und Coding, Llama 3.3 Nemotron Super 49B v1 liefert den höheren Token-Durchsatz und Llama 3.3 Nemotron Super 49B v1 ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Llama 3.1 Nemotron Ultra 253B oder Llama 3.3 Nemotron Super 49B v1?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Llama 3.1 Nemotron Ultra 253B vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Llama 3.1 Nemotron Ultra 253B und Llama 3.3 Nemotron Super 49B v1?+

Llama 3.1 Nemotron Ultra 253B kostet $0.80 / $2.40 pro 1M Input-/Output-Tokens, Llama 3.3 Nemotron Super 49B v1 liegt bei $0.25 / $0.50. Beim gewichteten Gesamtpreis ist Llama 3.3 Nemotron Super 49B v1 günstiger.

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

Llama 3.1 Nemotron Ultra 253B bietet 128K Tokens Kontext gegenüber 131.1K Tokens bei Llama 3.3 Nemotron Super 49B v1. Bei der Generierungsgeschwindigkeit liegt Llama 3.3 Nemotron Super 49B v1 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 & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

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.

Offizielle Quellen: Offizielle API-Dokumentation von NVIDIA und NVIDIA, Systemkarten und unabhängige Benchmark-Auswertungen (2026).