Llama Nemotron Embed VL 1B v2 vs Gemini 4 Argon
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Llama Nemotron Embed VL 1B v2 by NVIDIA against Gemini 4 Argon by Google across real-world coding, reasoning, token economics, and latency.
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Llama Nemotron Embed VL 1B v2
Embedding Vettoriali e RAGLlama Nemotron Embed VL 1B v2 di NVIDIA è un modello di IA progettato per la ricerca vettoriale semantica, l’indicizzazione documentale e le pipeline RAG, con finestra di contesto da 32K, velocità di 280 token/s, indice di intelligenza di 3
$0.02 / 1M
$0.02 / 1M
32K tokens
N/A
Gemini 4 Argon
IA Multimodale NativaGemini 4 Argon di Google è un modello di IA progettato per la comprensione multimodale nativa di testo e immagini e l’analisi visiva dei documenti, con finestra di contesto da 1M, velocità di 115 token/s, indice di intelligenza di 97/100 e
$2.00 / 1M
$10.00 / 1M
1M tokens
81.9%
Differenze Chiave a Colpo d’Occhio
- Llama Nemotron Embed VL 1B v2 is 218.9x cheaper for API usage (input + output tokens combined).
- Gemini 4 Argon has a larger context window (1M vs 32K tokens).
- Llama Nemotron Embed VL 1B v2 is faster at 280 tok/s vs 115 tok/s.
| Feature | Llama Nemotron Embed VL 1B v2 | Gemini 4 Argon |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 32 | 97 |
Provider | NVIDIA | |
Input Price Cost per million input tokens | $0.02 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $0.02 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.01 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 32K tokens~48 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 | 280 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 |
Chi vince in ogni categoria?
At-a-glance decision frameworkEvaluations ongoing
Punteggi Benchmark Verificati
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.
Latenza e Tempo di Risposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulatore Interattivo dei Costi Token
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Llama Nemotron Embed VL 1B v2 · Strengths & Key Advantages
- Finestra di contesto da 32K con capacità massima di output fino a 2K token (Arena Elo 1210)
- Throughput elevato di 280 token/secondo con latenza al primo token (TTFT) di soli 80 ms
- Pesi aperti (1B) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama
Considerations & Trade-offs
- Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
- Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità
Gemini 4 Argon · Strengths & Key Advantages
- Finestra di contesto da 1M con capacità massima di output fino a 1M token (SWE-bench Verified 81.9% · GPQA Diamond 81.5%)
- Throughput elevato di 115 token/secondo con latenza al primo token (TTFT) di soli 850 ms
- Economia API competitiva ($2/$10 per 1M di token) con supporto per prompt caching, tool calling e output JSON strutturati
Considerations & Trade-offs
- Modello cloud proprietario chiuso; non consente il download locale dei pesi per l’esecuzione offline
- Per volumi di traffico elevati in produzione si consiglia di attivare il prompt caching e la Batch API
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Llama Nemotron Embed VL 1B v2 | Gemini 4 Argon |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.01 | $2.45 |
Output Tokens / Task Tokens generated per task | 1K | 75K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 36K |
Specifiche Tecniche
Architecture, release timeline, capabilities, and modalities.
| Feature | Llama Nemotron Embed VL 1B v2 | Gemini 4 Argon |
|---|---|---|
| Official API Model ID (SDK) | llama-nemotron-embed-vl-1b-v2 | models/gemini-4-argon |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2026-02 | 2026-09 |
| Knowledge Cutoff | December 2025 | June 2026 |
| Architecture | Dense Multimodal Embedding Transformer | Pathways Multimodal Sparse Mixture-of-Experts |
| Parameters | 1B | 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 ($0.02/1M in) across full 32K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Cached input at $0.005/1M 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) · 2 GB VRAM (4-bit Quantized) · 4 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 | embedding | text |
Llama Nemotron Embed VL 1B v2 vs Gemini 4 Argon: Risposte Rapide (FAQ)
Which is more intelligent, Llama Nemotron Embed VL 1B v2 or Gemini 4 Argon?
Gemini 4 Argon is more intelligent. It scores 97 compared with 32 on the composite Intelligence Index.
Which model is faster?
Llama Nemotron Embed VL 1B v2 is faster, generating 280 tokens per second compared with 115 tokens per second.
Which model is cheaper to run?
Llama Nemotron Embed VL 1B v2 is cheaper at $0.01 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Llama Nemotron Embed VL 1B v2 has lower latency, with a time to first token of 0.08s compared with 0.85s.
Which model has a larger context window?
Gemini 4 Argon has a larger context window, supporting 1M tokens compared with 32K tokens.
Verdetto
Choose Llama Nemotron Embed VL 1B v2 for ideale per la ricerca vettoriale semantica, l’indicizzazione documentale e le pipeline rag, sistemi rag aziendali e integrazioni api scalabili., or choose Gemini 4 Argon for ideale per la comprensione multimodale nativa di testo e immagini e l’analisi visiva dei documenti, sistemi rag aziendali e integrazioni api scalabili..
Domande Frequenti (FAQ)
Which is cheaper: Llama Nemotron Embed VL 1B v2 or Gemini 4 Argon?+
Llama Nemotron Embed VL 1B v2 costs $0.02/1M input and $0.02/1M output tokens, while Gemini 4 Argon costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Llama Nemotron Embed VL 1B v2 or Gemini 4 Argon?+
Llama Nemotron Embed VL 1B v2 supports 32K 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.
Fonti ufficiali: NVIDIA & Google Official Documentation