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Gemini Embedding 001 vs Llama Nemotron Embed VL 1B v2

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Verificato dal team di ricerca di GetInfoUsUltima verifica: October 2026Politica editoriale
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Gemini Embedding 001 vs Llama Nemotron Embed VL 1B v2: Benchmarks, Pricing & Speed Comparison

Gemini Embedding 001 di Google e Llama Nemotron Embed VL 1B v2 di NVIDIA sono due modelli di IA di riferimento. Gemini Embedding 001 offre una finestra di contesto di 2K token a $0.15/$0.00 per milione di token, mentre Llama Nemotron Embed VL 1B v2 mette a disposizione 32K token di contesto a $0.02/$0.02 per milione di token. Di seguito analizziamo benchmark, latenza e costi.

Confronto Diretto (VS)

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Google

Gemini Embedding 001

Embedding Vettoriali e RAG

Gemini Embedding 001 di Google è un modello di IA progettato per la ricerca vettoriale semantica, l’indicizzazione documentale e le pipeline RAG, con finestra di contesto da 2K, velocità di 400 token/s, indice di intelligenza di 70/100 e ta

Intelligence Index70 / 100
Input Token:

$0.15 / 1M

Output Token:

$0.00 / 1M

Context Window:

2K tokens

SWE-bench Verified:

N/A

NVIDIA

Llama Nemotron Embed VL 1B v2

Embedding Vettoriali e RAG

Llama 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

Intelligence Index32 / 100
Input Token:

$0.02 / 1M

Output Token:

$0.02 / 1M

Context Window:

32K tokens

SWE-bench Verified:

N/A

Differenze Chiave a Colpo d’Occhio

  • Llama Nemotron Embed VL 1B v2 is 4.1x cheaper for API usage (input + output tokens combined).
  • Llama Nemotron Embed VL 1B v2 has a larger context window (32K vs 2K tokens).
  • Gemini Embedding 001 is faster at 400 tok/s vs 280 tok/s.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGemini Embedding 001Llama Nemotron Embed VL 1B v2
Intelligence Index
Composite reasoning & knowledge score (0-100)
7032
Provider
GoogleNVIDIA
Input Price
Cost per million input tokens
$0.15 / 1M$0.02 / 1M
Output Price
Cost per million output tokens
$0.00 / 1M$0.02 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.03 / 1M$0.01 / 1M
Context Window
Maximum input + output tokens
2K tokens~3 A4 pages of text32K tokens~48 A4 pages of text
Max Output Tokens
Per single request
4K2K
Throughput Speed
Tokens generated per second
400 tok/s280 tok/s
SWE-bench Verified
Real-world software engineering
N/AN/A
Cached Input Price
Prompt caching discount per 1M tokens
$0.00 / 1M$0.01 / 1M

Chi vince in ogni categoria?

At-a-glance decision framework
Coding

Evaluations ongoing

Cost
Llama Nemotron Embed VL 1B v2
4.1x cheaper monthly runtime
Speed
Gemini Embedding 001
+43% faster generation
Context
Llama Nemotron Embed VL 1B v2
32K tokens window

Punteggi Benchmark Verificati

Official evaluation metrics verified by independent third-party research labs.

Gemini Embedding 001Llama Nemotron Embed VL 1B v2
LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1280ptsvs1210pts
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

400 tok/s vs280 tok/s

Latenza e Tempo di Risposta

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better
Gemini Embedding 0010.15s
Llama Nemotron Embed VL 1B v20.08s
Est. End-to-End Response (500 tokens)TTFT + generation time
Gemini Embedding 0011.40s
Llama Nemotron Embed VL 1B v21.87s

Simulatore Interattivo dei Costi Token

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
Gemini Embedding 001 Monthly API Bill
$0.22 / month
$0.15/1M in · $0/1M out
Llama Nemotron Embed VL 1B v2 Monthly API Bill
$0.06 / month
$0.02/1M in · $0.02/1M out
Llama Nemotron Embed VL 1B v2 saves approximately $0.17 / month (75%) under this workload.4.1x Savings

Gemini Embedding 001 · Strengths & Key Advantages

  • Finestra di contesto da 2K con capacità massima di output fino a 4K token (Arena Elo 1280)
  • Throughput elevato di 400 token/secondo con latenza al primo token (TTFT) di soli 150 ms
  • Economia API competitiva ($0.15/$0 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
  • Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità

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

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

MetricGemini Embedding 001Llama Nemotron Embed VL 1B v2
Cost per Task
Estimated USD to finish one task
$0.05$0.01
Output Tokens / Task
Tokens generated per task
0K1K
Reasoning Tokens / Task
Thinking tokens burned per task
0K0K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

FeatureGemini Embedding 001Llama Nemotron Embed VL 1B v2
Official API Model ID (SDK)models/gemini-embedding-001llama-nemotron-embed-vl-1b-v2
API ProtocolGoogle Gemini API (generateContent) + OpenAI-Compatible SDKOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2025-052026-02
Knowledge CutoffMarch 2025December 2025
ArchitectureDense semantic text embedding transformerDense Multimodal Embedding Transformer
ParametersConfidential1B
Enterprise Rate Limits (RPM / TPM)10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPMUnlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)Flat rate ($0.15/1M in) across full 2K windowFlat rate ($0.02/1M in) across full 32K window
Prompt Cache Policy & TTLImplicit + Explicit Context Caching at $0/1M with configurable TTLCached input at $0.005/1M tokens
Computer Use / GUI AgentText-only completion (No GUI action loop)Text-only completion (No GUI action loop)
MCP & Parallel Tool CallingPrompt-based JSON extraction onlyPrompt-based JSON extraction only
Web Search Grounding & SandboxBuilt-in Google Search Grounding + Managed Python Code Execution SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportVertex AI Supervised Adapter Tuning & Context DistillationFull Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
Multimodal Token BillingText BPE (~4 chars/token)Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
Quantization & Min GPU VRAMCloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 2 GB VRAM (4-bit Quantized) · 4 GB VRAM (Native BF16)
Safety & Frontier ClassificationGoogle DeepMind Frontier Safety Framework VerifiedOpen-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
Enterprise Compliance (ZDR/HIPAA/SOC2)SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestexttext, vision
Output Modalitiesembeddingembedding

Gemini Embedding 001 vs Llama Nemotron Embed VL 1B v2: Risposte Rapide (FAQ)

Which is more intelligent, Gemini Embedding 001 or Llama Nemotron Embed VL 1B v2?

Gemini Embedding 001 is more intelligent. It scores 70 compared with 32 on the composite Intelligence Index.

Which model is faster?

Gemini Embedding 001 is faster, generating 400 tokens per second compared with 280 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 $0.03 (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 0.15s.

Which model has a larger context window?

Llama Nemotron Embed VL 1B v2 has a larger context window, supporting 32K tokens compared with 2K tokens.

Verdetto

Sia Gemini Embedding 001 (Ideale per la ricerca vettoriale semantica, l’indicizzazione documentale e le pipeline RAG, sistemi RAG aziendali e integrazioni API scalabili.) sia Llama Nemotron Embed VL 1B v2 (Ideale per la ricerca vettoriale semantica, l’indicizzazione documentale e le pipeline RAG, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: Gemini Embedding 001 guida nei benchmark di ragionamento e programmazione, Gemini Embedding 001 garantisce una generazione più rapida e Llama Nemotron Embed VL 1B v2 offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, Gemini Embedding 001 o Llama Nemotron Embed VL 1B v2?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Gemini Embedding 001. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra Gemini Embedding 001 e Llama Nemotron Embed VL 1B v2?+

Gemini Embedding 001 costa $0.15 / $0.00 per milione di token di input/output, mentre Llama Nemotron Embed VL 1B v2 costa $0.02 / $0.02. Nel costo combinato, Llama Nemotron Embed VL 1B v2 risulta più economico.

Quale modello ha la finestra di contesto più ampia e la velocità maggiore?+

Gemini Embedding 001 supporta 2K token di contesto contro 32K token di Llama Nemotron Embed VL 1B v2, mentre Gemini Embedding 001 offre una velocità di generazione superiore.

AI Terminology & Guide
Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

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: Documentazione API ufficiale di Google e NVIDIA, schede di sistema e benchmark indipendenti (2026).