Llama 4 Behemoth vs Nemotron 3 Ultra 550B A55B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Llama 4 Behemoth dari Meta dan Nemotron 3 Ultra 550B A55B dari NVIDIA adalah dua model AI unggulan. Llama 4 Behemoth menawarkan jendela konteks 10M token dengan harga $0.00/$0.00 per 1 juta token, sedangkan Nemotron 3 Ultra 550B A55B menyediakan konteks 1M token seharga $1.00/$3.00 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.
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Llama 4 Behemoth
Model Dasar Bobot TerbukaLlama 4 Behemoth dari Meta adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 10M, kecepatan 45 token/detik, indeks kecerdasan 93/100, serta akses gratis
$0.00 / 1M
$0.00 / 1M
10M tokens
75%
Nemotron 3 Ultra 550B A55B
Model Dasar Bobot TerbukaNemotron 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
$1.00 / 1M
$3.00 / 1M
1M tokens
64.5%
Perbedaan Utama Sekilas
- 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 |
Siapa Unggul di Mana?
At-a-glance decision frameworkSkor Benchmark Terverifikasi
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.
Latensi & Waktu Respons
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulator Biaya Token Interaktif
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Llama 4 Behemoth · Strengths & Key Advantages
- Jendela konteks 10M dengan kapasitas output maksimum hingga 64K token (SWE-bench Verified 75% · GPQA Diamond 82%)
- Kecepatan generasi tinggi 45 token/detik dengan latensi token pertama (TTFT) hanya 1200 ms
- Bobot terbuka (2T total / 288B (16-of-2 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
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
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 |
Spesifikasi Teknis
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: Jawaban Cepat (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.
Kesimpulan
Baik Llama 4 Behemoth (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun 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.) sangat tangguh: Llama 4 Behemoth unggul dalam penalaran dan pemrograman, Nemotron 3 Ultra 550B A55B memberikan kecepatan generasi token lebih tinggi, dan Llama 4 Behemoth lebih hemat biaya untuk penggunaan API skala besar.
Pertanyaan Umum (FAQ)
Mana yang lebih baik untuk coding dan penalaran, Llama 4 Behemoth atau Nemotron 3 Ultra 550B A55B?+
Dalam indeks kecerdasan gabungan dan benchmark coding, Llama 4 Behemoth memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.
Berapa perbandingan harga API Llama 4 Behemoth vs Nemotron 3 Ultra 550B A55B?+
Llama 4 Behemoth mengenakan biaya $0.00 / $0.00 per 1M token input/output, sedangkan Nemotron 3 Ultra 550B A55B sebesar $1.00 / $3.00. Secara keseluruhan, Llama 4 Behemoth lebih hemat biaya.
Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+
Llama 4 Behemoth mendukung konteks 10M token dibandingkan 1M token pada Nemotron 3 Ultra 550B A55B, sementara Nemotron 3 Ultra 550B A55B unggul dalam kecepatan output token per detik.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
- Command A Plus vs Nemotron 3 Ultra 550B A55B: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Llama 4 Behemoth vs Llama 5: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Nemotron 3 Ultra 550B A55B vs Gemini 4 Argon: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Nemotron 3 Ultra 550B A55B vs GPT-OSS 120B: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Nemotron 3 Ultra 550B A55B vs Llama 5: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Nemotron 3 Ultra 550B A55B vs Qwen3.8 Max Preview: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
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.
Sumber resmi: Dokumentasi API resmi Meta & NVIDIA serta evaluasi benchmark independen (2026).