Nemotron 3 Ultra 550B A55B vs Gemini 4 Argon
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Nemotron 3 Ultra 550B A55B by NVIDIA and Gemini 4 Argon by Google 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, Nemotron 3 Ultra 550B A55B holds a notable pricing advantage for token ingestion. On output generation, Nemotron 3 Ultra 550B A55B is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical memory horizon with 1,000K tokens. In latency-sensitive production environments, Gemini 4 Argon 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.
بدّل وقارن بين أي نموذجين
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Nemotron 3 Ultra 550B A55B
نموذج مفتوح الأوزاننموذج Nemotron 3 Ultra 550B A55B المقدم من NVIDIA يوفر نافذة سياق تصل إلى 1M رمز، بتكلفة $1 لكل مليون رمز إدخال و$3 لكل مليون رمز إخراج، ومصمم للبرمجة والاستدلال وتطبيقات الأعمال.
$1.00 / 1M
$3.00 / 1M
1M tokens
64.5%
Gemini 4 Argon
نموذج استدلال عميقنموذج Gemini 4 Argon المقدم من Google يوفر نافذة سياق تصل إلى 1M رمز، بتكلفة $2 لكل مليون رمز إدخال و$10 لكل مليون رمز إخراج، ومصمم للبرمجة والاستدلال وتطبيقات الأعمال.
$2.00 / 1M
$10.00 / 1M
1M tokens
81.9%
أهم الفروقات في لمحة
- Nemotron 3 Ultra 550B A55B is 2.7x cheaper for API usage (input + output tokens combined).
- Gemini 4 Argon is faster at 115 tok/s vs 65 tok/s.
- Gemini 4 Argon leads on SWE-bench coding (81.9% vs 64.5%).
| Feature | Nemotron 3 Ultra 550B A55B | Gemini 4 Argon |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 60 | 97 |
Provider | NVIDIA | |
Input Price Cost per million input tokens | $1.00 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $3.00 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.68 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 66K | 1000K |
Throughput Speed Tokens generated per second | 65 tok/s | 115 tok/s |
SWE-bench Verified Real-world software engineering | 64.5% | 81.9% |
GPQA Diamond Graduate-level reasoning | 76% | 81.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.25 / 1M | $0.10 / 1M |
من يتفوق في كل مجال؟
At-a-glance decision frameworkنتائج الاختبارات المعيارية الموثقة
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.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
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.
زمن الاستجابة والسرعة
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
محاكي تكلفة الرموز التفاعلي
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Nemotron 3 Ultra 550B A55B · Strengths & Key Advantages
- بنية Colossal Sparse MoE (550B Total / 55B Active) متطورة من NVIDIA مع سعة سياق تبلغ 1M رمز
- كفاءة عالية في التكلفة ($1/1M إدخال و$3/1M إخراج) وسرعة استجابة ممتازة
- أداء قوي في اختبارات البرمجة، الاستدلال المنطقي، ودعم اللغات المتعددة
Considerations & Trade-offs
- يتطلب إدارة دقيقة لاستهلاك الرموز عند استخدام نافذة السياق الكاملة
- قد يحتاج إلى ضبط دقيق للتعليمات (Prompt Engineering) في المهام التخصصية المعقدة
Gemini 4 Argon · Strengths & Key Advantages
- بنية Pathways Multimodal Sparse Mixture-of-Experts متطورة من Google مع سعة سياق تبلغ 1M رمز
- كفاءة عالية في التكلفة ($2/1M إدخال و$10/1M إخراج) وسرعة استجابة ممتازة
- أداء قوي في اختبارات البرمجة، الاستدلال المنطقي، ودعم اللغات المتعددة
Considerations & Trade-offs
- يتطلب إدارة دقيقة لاستهلاك الرموز عند استخدام نافذة السياق الكاملة
- قد يحتاج إلى ضبط دقيق للتعليمات (Prompt Engineering) في المهام التخصصية المعقدة
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Nemotron 3 Ultra 550B A55B | Gemini 4 Argon |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.85 | $2.45 |
Output Tokens / Task Tokens generated per task | 48K | 75K |
Reasoning Tokens / Task Thinking tokens burned per task | 24K | 36K |
المواصفات التقنية
Architecture, release timeline, capabilities, and modalities.
| Feature | Nemotron 3 Ultra 550B A55B | Gemini 4 Argon |
|---|---|---|
| Official API Model ID (SDK) | nemotron-3-ultra-550b-a55b | models/gemini-4-argon |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2026-06 | 2026-09 |
| Knowledge Cutoff | February 2026 | June 2026 |
| Architecture | Colossal Sparse MoE (550B Total / 55B Active) | Pathways Multimodal Sparse Mixture-of-Experts |
| Parameters | 550B (55B active MoE) | Confidential |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($1/1M in) across full 1000K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Cached input at $0.25/1M tokens | Implicit + Explicit Context Caching at $0.1/1M with configurable TTL |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| 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 | Built-in Google Search Grounding + Managed Python Code Execution Sandbox |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Vertex AI Supervised Adapter Tuning & Context Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 341 GB VRAM (4-bit Quantized) · 1183 GB VRAM (Native BF16) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Google DeepMind Frontier Safety Framework Verified |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text | text, vision, audio, video |
| Output Modalities | text | text |
Nemotron 3 Ultra 550B A55B vs Gemini 4 Argon: إجابات سريعة (FAQ)
Which is more intelligent, Nemotron 3 Ultra 550B A55B or Gemini 4 Argon?
Gemini 4 Argon is more intelligent. It scores 97 compared with 60 on the composite Intelligence Index.
Which model is faster?
Gemini 4 Argon is faster, generating 115 tokens per second compared with 65 tokens per second.
Which model is cheaper to run?
Nemotron 3 Ultra 550B A55B is cheaper at $0.68 per 1M tokens blended, compared with $1.47 (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 0.85s.
الحكم النهائي
Verdict & Model Selection Recommendation: Choose Nemotron 3 Ultra 550B A55B if your priority is National AI initiatives, sovereign defense intelligence, and enterprise-wide autonomous agent swarms. Its performance in demanding tasks justifies its operational token cost. Choose Gemini 4 Argon if your workflow prioritizes Whole-repository code generation, full-book synthesis, video understanding, and multi-hour audio processing. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
الأسئلة الشائعة (FAQ)
How does Nemotron 3 Ultra 550B A55B compare to Gemini 4 Argon in API token pricing and cost per token?+
Nemotron 3 Ultra 550B A55B costs $1.00 per 1M input tokens and $3.00 per 1M output tokens, whereas Gemini 4 Argon costs $2.00 input and $10.00 output. Overall, Nemotron 3 Ultra 550B A55B 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: Nemotron 3 Ultra 550B A55B or Gemini 4 Argon?+
Both Nemotron 3 Ultra 550B A55B and Gemini 4 Argon offer an identical context window of 1,000K tokens (~750k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.
Which AI model is better for software engineering and coding: Nemotron 3 Ultra 550B A55B or Gemini 4 Argon?+
Nemotron 3 Ultra 550B A55B scores 64.5% on SWE-bench Verified while Gemini 4 Argon scores 81.9%. For autonomous software engineering and multi-file debugging, Gemini 4 Argon holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Nemotron 3 Ultra 550B A55B and Gemini 4 Argon?+
Nemotron 3 Ultra 550B A55B features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Gemini 4 Argon provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Nemotron 3 Ultra 550B A55B and Gemini 4 Argon?+
Nemotron 3 Ultra 550B A55B generates approximately 65 tokens per second, while Gemini 4 Argon delivers 115 tokens per second. In latency-sensitive production environments, Gemini 4 Argon provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Nemotron 3 Ultra 550B A55B and Gemini 4 Argon to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Nemotron 3 Ultra 550B A55B to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Gemini 4 Argon. 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.
Related reading
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- Command A Plus vs Nemotron 3 Ultra 550B A55B: Benchmarks, Pricing & Speed Comparisonمقارنة
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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.
المصادر الرسمية: GetInfoUs Independent AI Benchmarks & Official API Specs