Nemotron Content Safety Reasoning 4B vs Gemini 4 Argon
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Nemotron Content Safety Reasoning 4B by NVIDIA against Gemini 4 Argon by Google across real-world coding, reasoning, token economics, and latency.
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Nemotron Content Safety Reasoning 4B
Raisonnement à Poids OuvertsNemotron Content Safety Reasoning 4B de NVIDIA est un modèle d’IA conçu pour le raisonnement par chaîne de pensée à poids ouverts et haute efficacité, offrant une fenêtre de contexte de 128K, une vitesse de 150 tokens/s, un indice d’intelli
$0.05 / 1M
$0.15 / 1M
128K tokens
N/A
Gemini 4 Argon
IA Multimodale NativeGemini 4 Argon de Google est un modèle d’IA conçu pour la compréhension multimodale native (texte, vision) et l’analyse de documents complexes, offrant une fenêtre de contexte de 1M, une vitesse de 115 tokens/s, un indice d’intelligence de
$2.00 / 1M
$10.00 / 1M
1M tokens
81.9%
Différences Clés en un Coup d’Œil
- Nemotron Content Safety Reasoning 4B is 54.0x cheaper for API usage (input + output tokens combined).
- Gemini 4 Argon has a larger context window (1M vs 128K tokens).
- Nemotron Content Safety Reasoning 4B is faster at 150 tok/s vs 115 tok/s.
| Caractéristique | Nemotron Content Safety Reasoning 4B | Gemini 4 Argon |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 38 | 97 |
Provider | NVIDIA | |
Input Price Cost per million input tokens | $0.05 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $0.15 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.03 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 4K | 1000K |
Throughput Speed Tokens generated per second | 150 tok/s | 115 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 81.9% |
GPQA Diamond Graduate-level reasoning | N/A | 81.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.01 / 1M | $0.10 / 1M |
Qui gagne où ?
At-a-glance decision frameworkEvaluations ongoing
Scores de Benchmarks Vérifiés
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.
Latence et Temps de Réponse
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulateur Interactif de Coût de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Nemotron Content Safety Reasoning 4B · Strengths & Key Advantages
- Fenêtre de contexte de 128K et capacité de sortie maximale de 4K tokens (Arena Elo 1190)
- Débit rapide de 150 tokens/seconde avec une latence au premier token (TTFT) de 240 ms
- Poids ouverts (4B) permettant un déploiement sur site ou cloud privé via vLLM, SGLang ou Ollama
Considerations & Trade-offs
- Nécessite des serveurs GPU dotés de suffisamment de mémoire VRAM pour l’inférence locale à pleine précision
- Sur les problèmes de recherche fondamentale très complexes, les modèles de raisonnement lourd restent supérieurs
Gemini 4 Argon · Strengths & Key Advantages
- Fenêtre de contexte de 1M et capacité de sortie maximale de 1M tokens (SWE-bench Verified 81.9% · GPQA Diamond 81.5%)
- Débit rapide de 115 tokens/seconde avec une latence au premier token (TTFT) de 850 ms
- Tarification API optimisée ($2/$10 par million de tokens) avec prise en charge du cache de prompts et des schémas JSON stricts
Considerations & Trade-offs
- Modèle cloud propriétaire ne permettant pas le téléchargement local des poids sur serveur isolé
- Pour les charges à très fort volume, l’activation du cache de prompts et de l’API Batch est recommandée
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Nemotron Content Safety Reasoning 4B | Gemini 4 Argon |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.02 | $2.45 |
Output Tokens / Task Tokens generated per task | 4K | 75K |
Reasoning Tokens / Task Thinking tokens burned per task | 2K | 36K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | Nemotron Content Safety Reasoning 4B | Gemini 4 Argon |
|---|---|---|
| Official API Model ID (SDK) | nemotron-content-safety-reasoning-4b | models/gemini-4-argon |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2026-01 | 2026-09 |
| Knowledge Cutoff | November 2025 | June 2026 |
| Architecture | Reasoning-Augmented Safety Classifier | Pathways Multimodal Sparse Mixture-of-Experts |
| Parameters | 4B | 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 ($0.05/1M in) across full 128K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Cached input at $0.01/1M tokens | Implicit + Explicit Context Caching at $0.1/1M with configurable TTL |
| Computer Use / GUI Agent | Text-only completion (No GUI action loop) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| MCP & Parallel Tool Calling | Prompt-based JSON extraction only | 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) · 2 GB VRAM (4-bit Quantized) · 9 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 Content Safety Reasoning 4B vs Gemini 4 Argon: Réponses Rapides (FAQ)
Which is more intelligent, Nemotron Content Safety Reasoning 4B or Gemini 4 Argon?
Gemini 4 Argon is more intelligent. It scores 97 compared with 38 on the composite Intelligence Index.
Which model is faster?
Nemotron Content Safety Reasoning 4B is faster, generating 150 tokens per second compared with 115 tokens per second.
Which model is cheaper to run?
Nemotron Content Safety Reasoning 4B is cheaper at $0.03 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Nemotron Content Safety Reasoning 4B has lower latency, with a time to first token of 0.24s compared with 0.85s.
Which model has a larger context window?
Gemini 4 Argon has a larger context window, supporting 1M tokens compared with 128K tokens.
Verdict
Choose Nemotron Content Safety Reasoning 4B for conçu pour le raisonnement par chaîne de pensée à poids ouverts et haute efficacité, l’intégration rag et les pipelines de production évolutifs., or choose Gemini 4 Argon for conçu pour la compréhension multimodale native (texte, vision) et l’analyse de documents complexes, l’intégration rag et les pipelines de production évolutifs..
Questions fréquentes
Which is cheaper: Nemotron Content Safety Reasoning 4B or Gemini 4 Argon?+
Nemotron Content Safety Reasoning 4B costs $0.05/1M input and $0.15/1M output tokens, while Gemini 4 Argon costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Nemotron Content Safety Reasoning 4B or Gemini 4 Argon?+
Nemotron Content Safety Reasoning 4B supports 128K tokens whereas Gemini 4 Argon supports 1M tokens.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: NVIDIA & Google Official Documentation