Llama 4 Behemoth vs Nemotron 3 Ultra 550B A55B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Llama 4 Behemoth by Meta and Nemotron 3 Ultra 550B A55B by NVIDIA represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, Llama 4 Behemoth holds a notable pricing advantage for token ingestion. On output generation, Llama 4 Behemoth is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Llama 4 Behemoth provides an expanded memory horizon with 10,000K tokens. In latency-sensitive production environments, Nemotron 3 Ultra 550B A55B leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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Llama 4 Behemoth
Open-Weights-ModellLlama 4 Behemoth von Meta ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 10M Token zu API-Preisen von $0/1M Input- und $0/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$0.00 / 1M
$0.00 / 1M
10M tokens
75%
Nemotron 3 Ultra 550B A55B
Open-Weights-ModellNemotron 3 Ultra 550B A55B von NVIDIA ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 1M Token zu API-Preisen von $1/1M Input- und $3/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$1.00 / 1M
$3.00 / 1M
1M tokens
64.5%
Wichtigste Unterschiede auf einen Blick
- 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%).
| 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 |
Wer gewinnt in welcher Disziplin?
At-a-glance decision frameworkGeprüfte Benchmark-Ergebnisse im Vergleich
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
Latenz & Antwortzeit
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Interaktiver Token-Kosten-Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Llama 4 Behemoth · Strengths & Key Advantages
- Fortschrittliche Colossal sparse MoE architecture (288B active / 2T total, 16-of-2 MoE)-Architektur von Meta mit 10M Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($0/1M Input, $0/1M Output) bei hoher Durchsatzrate
- Hohe Präzision bei Code-Generierung, logischem Schließen und mehrsprachigen Benchmarks
Considerations & Trade-offs
- Bei sehr großen Kontextmengen ist eine sorgfältige Token-Budgetierung ratsam
- Spezialisierte Fachaufgaben profitieren von gezieltem Prompt-Engineering
Nemotron 3 Ultra 550B A55B · Strengths & Key Advantages
- Fortschrittliche Colossal Sparse MoE (550B Total / 55B Active)-Architektur von NVIDIA mit 1M Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($1/1M Input, $3/1M Output) bei hoher Durchsatzrate
- Hohe Präzision bei Code-Generierung, logischem Schließen und mehrsprachigen Benchmarks
Considerations & Trade-offs
- Bei sehr großen Kontextmengen ist eine sorgfältige Token-Budgetierung ratsam
- Spezialisierte Fachaufgaben profitieren von gezieltem Prompt-Engineering
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 |
Technische Spezifikationen
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: Schnelle Antworten (FAQ)
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.
Fazit
Verdict & Model Selection Recommendation: Choose Llama 4 Behemoth if your priority is National laboratories, large enterprise AI clusters, and extreme scientific modeling. Its performance in demanding tasks justifies its operational token cost. Choose Nemotron 3 Ultra 550B A55B if your workflow prioritizes National AI initiatives, sovereign defense intelligence, and enterprise-wide autonomous agent swarms. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Häufig gestellte Fragen (FAQ)
How does Llama 4 Behemoth compare to Nemotron 3 Ultra 550B A55B in API token pricing and cost per token?+
Llama 4 Behemoth costs $0.00 per 1M input tokens and $0.00 per 1M output tokens, whereas Nemotron 3 Ultra 550B A55B costs $1.00 input and $3.00 output. Overall, Llama 4 Behemoth is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Llama 4 Behemoth or Nemotron 3 Ultra 550B A55B?+
Llama 4 Behemoth supports a context window of 10,000K tokens (~7,500k words), while Nemotron 3 Ultra 550B A55B supports 1,000K tokens (~750k words). Therefore, Llama 4 Behemoth can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Llama 4 Behemoth or Nemotron 3 Ultra 550B A55B?+
Llama 4 Behemoth scores 75% on SWE-bench Verified while Nemotron 3 Ultra 550B A55B scores 64.5%. For autonomous software engineering and multi-file debugging, Llama 4 Behemoth holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Llama 4 Behemoth and Nemotron 3 Ultra 550B A55B?+
Llama 4 Behemoth features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Nemotron 3 Ultra 550B A55B provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Llama 4 Behemoth and Nemotron 3 Ultra 550B A55B?+
Llama 4 Behemoth generates approximately 45 tokens per second, while Nemotron 3 Ultra 550B A55B delivers 65 tokens per second. In latency-sensitive production environments, Nemotron 3 Ultra 550B A55B provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Llama 4 Behemoth and Nemotron 3 Ultra 550B A55B to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Llama 4 Behemoth to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Nemotron 3 Ultra 550B A55B. This tiered routing pattern cuts overall API bills by up to 70%.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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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: GetInfoUs Independent AI Benchmarks & Official API Specs