Nemotron 3.5 Content Safety vs Gemini 4 Argon
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Nemotron 3.5 Content Safety by NVIDIA against Gemini 4 Argon by Google across real-world coding, reasoning, token economics, and latency.
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Nemotron 3.5 Content Safety
Safety ModelMultimodal content moderation engine designed to detect toxic imagery, illegal text, and cross-modal prompt injection attempts.
$0.08 / 1M
$0.16 / 1M
128K tokens
N/A
Gemini 4 Argon
1M Output Multimodal KingGoogle DeepMind's flagship frontier reasoning engine featuring 1-million-token output limits, autonomous cybersecurity defense, and real-time audio/vision ingestion.
$2.00 / 1M
$10.00 / 1M
1M tokens
81.9%
Key Differences at a Glance
- Nemotron 3.5 Content Safety is 41.3x cheaper for API usage (input + output tokens combined).
- Gemini 4 Argon has a larger context window (1M vs 128K tokens).
- Nemotron 3.5 Content Safety is faster at 160 tok/s vs 115 tok/s.
| Feature | Nemotron 3.5 Content Safety | Gemini 4 Argon |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 39 | 97 |
Provider | NVIDIA | |
Input Price Cost per million input tokens | $0.08 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $0.16 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.05 / 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 | 2K | 1000K |
Throughput Speed Tokens generated per second | 160 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 | $0.02 / 1M | $0.10 / 1M |
Who Wins Where?
At-a-glance decision frameworkEvaluations ongoing
Empirical Head-to-Head Benchmark Scores
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.
Latency & Response Time
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Real-Time Interactive Token Cost Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Nemotron 3.5 Content Safety · Strengths & Key Advantages
- Simultaneous moderation across visual image feeds and conversational text
- Chain-of-thought safety reasoning explaining policy violations
- Engineered for live video streaming and gaming moderation pipelines
Considerations & Trade-offs
- Moderation-only model; not intended for general conversation or code writing
- Higher compute requirements than lightweight text-only safety guards
Gemini 4 Argon · Strengths & Key Advantages
- Groundbreaking 1,000,000 token maximum output generation capacity via Long Decode Continuation
- 1M-token input window with 99.7% GraphWalks recall (84.2% in the 256K-1M range)
- Native processing of full audio, video streams, and visual data (91.7% on LVBench)
- Half the token price of Anthropic's Opus 5.5 and #1 on Chatbot Arena (1525 Elo)
Considerations & Trade-offs
- Massive token outputs can incur high absolute costs if unconstrained (~62K output tokens per index task)
- $2/$10 pricing is an introductory 50% discount; standard rates revert to $4/$20 once the promotion ends
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Nemotron 3.5 Content Safety | Gemini 4 Argon |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.02 | $2.45 |
Output Tokens / Task Tokens generated per task | 2K | 75K |
Reasoning Tokens / Task Thinking tokens burned per task | 1K | 36K |
Technical Specifications
Architecture, release timeline, capabilities, and modalities.
| Feature | Nemotron 3.5 Content Safety | Gemini 4 Argon |
|---|---|---|
| Official API Model ID (SDK) | nemotron-3-5-content-safety | 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 | April 2026 | June 2026 |
| Architecture | Multimodal Safety & Compliance Classifier | Pathways Multimodal Sparse Mixture-of-Experts |
| Parameters | 12B | 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 ($0.08/1M 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 $0.02/1M 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) · Vision (~258–765 tokens per image tile) | 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) · 7 GB VRAM (4-bit Quantized) · 26 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, vision | text, vision, audio, video |
| Output Modalities | text | text |
Nemotron 3.5 Content Safety vs Gemini 4 Argon: Quick Answers
Which is more intelligent, Nemotron 3.5 Content Safety or Gemini 4 Argon?
Gemini 4 Argon is more intelligent. It scores 97 compared with 39 on the composite Intelligence Index.
Which model is faster?
Nemotron 3.5 Content Safety is faster, generating 160 tokens per second compared with 115 tokens per second.
Which model is cheaper to run?
Nemotron 3.5 Content Safety is cheaper at $0.05 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Nemotron 3.5 Content Safety has lower latency, with a time to first token of 0.22s 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 3.5 Content Safety for live streaming moderation, social media content review, and multimodal safety guardrails, or choose Gemini 4 Argon for whole-repository code generation, full-book synthesis, video understanding, and multi-hour audio processing.
Frequently asked questions
Which is cheaper: Nemotron 3.5 Content Safety or Gemini 4 Argon?+
Nemotron 3.5 Content Safety costs $0.08/1M input and $0.16/1M output tokens, while Gemini 4 Argon costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Nemotron 3.5 Content Safety or Gemini 4 Argon?+
Nemotron 3.5 Content Safety 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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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.
Sources: NVIDIA & Google Official Documentation