Gemini Embedding 001 vs Llama Nemotron Embed VL 1B v2
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Gemini Embedding 001 by Google and Llama Nemotron Embed VL 1B v2 by NVIDIA 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, Llama Nemotron Embed VL 1B v2 holds a notable pricing advantage for token ingestion. On output generation, Gemini Embedding 001 is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Llama Nemotron Embed VL 1B v2 provides an expanded memory horizon with 32K tokens. In latency-sensitive production environments, Gemini Embedding 001 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.
Changez et Comparez les Modèles
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Gemini Embedding 001
Plongementd'entreprise semantic texte embedding modèle powering récupération, semantic recherche, et RAG pipelines.
$0.15 / 1M
$0.00 / 1M
2K tokens
N/A
Llama Nemotron Embed VL 1B v2
Plongementcompact 1B multimodal embedding modèle pour joint texte et image vector recherche, RAG récupération, et cross-modal clustering.
$0.02 / 1M
$0.02 / 1M
32K tokens
N/A
Différences Clés en un Coup d’Œil
- 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.
| Caractéristique | Gemini Embedding 001 | Llama Nemotron Embed VL 1B v2 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 70 | 32 |
Provider | NVIDIA | |
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 text | 32K tokens~48 A4 pages of text |
Max Output Tokens Per single request | 4K | 2K |
Throughput Speed Tokens generated per second | 400 tok/s | 280 tok/s |
SWE-bench Verified Real-world software engineering | N/A | N/A |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.01 / 1M |
Qui gagne où ?
At-a-glance decision frameworkEvaluations ongoing
Scores de Benchmarks Vérifiés
Official evaluation metrics verified by independent third-party research labs.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
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.
Gemini Embedding 001 · Strengths & Key Advantages
- élevé MTEB récupération leaderboard ranking
- faible coût par millions tokens
- rapide vector génération
Considerations & Trade-offs
- 2K fenêtre de contexte limit
- texte-seulement — no visuel embeddings
Llama Nemotron Embed VL 1B v2 · Strengths & Key Advantages
- Joint multimodal embedding space uniting texte queries avec diagram et document images
- ultra-rapide vector encoding vitesse exceeding 280 tokens/sec
- élevé MTEB benchmark ranking pour récupération et cross-modal recherche
Considerations & Trade-offs
- Outputs vector embeddings seulement, pas conversationnel generative texte
- 32K fenêtre de contexte limité par rapport à larger document modèles
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Gemini Embedding 001 | Llama 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 | 0K | 1K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | Gemini Embedding 001 | Llama Nemotron Embed VL 1B v2 |
|---|---|---|
| Official API Model ID (SDK) | models/gemini-embedding-001 | llama-nemotron-embed-vl-1b-v2 |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-05 | 2026-02 |
| Knowledge Cutoff | March 2025 | December 2025 |
| Architecture | Dense semantic text embedding transformer | Dense Multimodal Embedding Transformer |
| Parameters | Confidential | 1B |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM | Unlimited (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 window | Flat rate ($0.02/1M in) across full 32K window |
| Prompt Cache Policy & TTL | Implicit + Explicit Context Caching at $0/1M with configurable TTL | Cached input at $0.005/1M tokens |
| Computer Use / GUI Agent | Text-only completion (No GUI action loop) | Text-only completion (No GUI action loop) |
| MCP & Parallel Tool Calling | Prompt-based JSON extraction only | Prompt-based JSON extraction only |
| Web Search Grounding & Sandbox | Built-in Google Search Grounding + Managed Python Code Execution Sandbox | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Vertex AI Supervised Adapter Tuning & Context Distillation | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| Quantization & Min GPU VRAM | Cloud-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 Classification | Google DeepMind Frontier Safety Framework Verified | Open-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 Tier | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text | text, vision |
| Output Modalities | embedding | embedding |
Gemini Embedding 001 vs Llama Nemotron Embed VL 1B v2: Réponses Rapides (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.
Verdict
Verdict & Model Selection Recommendation: Choose Gemini Embedding 001 if your priority is Enterprise vector search, document indexing, and semantic clustering. Its performance in demanding tasks justifies its operational token cost. Choose Llama Nemotron Embed VL 1B v2 if your workflow prioritizes Enterprise vector databases, multimodal RAG retrieval, and semantic image search. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Questions fréquentes
How does Gemini Embedding 001 compare to Llama Nemotron Embed VL 1B v2 in API token pricing and cost per token?+
Gemini Embedding 001 costs $0.15 per 1M input tokens and $0.00 per 1M output tokens, whereas Llama Nemotron Embed VL 1B v2 costs $0.02 input and $0.02 output. Overall, Llama Nemotron Embed VL 1B v2 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: Gemini Embedding 001 or Llama Nemotron Embed VL 1B v2?+
Gemini Embedding 001 supports a context window of 2K tokens (~2k words), while Llama Nemotron Embed VL 1B v2 supports 32K tokens (~24k words). Therefore, Llama Nemotron Embed VL 1B v2 can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Gemini Embedding 001 or Llama Nemotron Embed VL 1B v2?+
Gemini Embedding 001 scores at the frontier of coding benchmarks, while Llama Nemotron Embed VL 1B v2 scores competitively for software generation. For complex autonomous debugging and agentic engineering, Gemini Embedding 001 provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Gemini Embedding 001 and Llama Nemotron Embed VL 1B v2?+
Gemini Embedding 001 is optimized for low-latency direct generation. In contrast, Llama Nemotron Embed VL 1B v2 focuses on standard fast inference.
How does inference speed (tokens per second) compare between Gemini Embedding 001 and Llama Nemotron Embed VL 1B v2?+
Gemini Embedding 001 generates approximately 400 tokens per second, while Llama Nemotron Embed VL 1B v2 delivers 280 tokens per second. In latency-sensitive production environments, Gemini Embedding 001 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Gemini Embedding 001 and Llama Nemotron Embed VL 1B v2 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Llama Nemotron Embed VL 1B v2 to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Gemini Embedding 001. 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.
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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: GetInfoUs Independent AI Benchmarks & Official API Specs