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Gemini Embedding 001 vs Llama Nemotron Embed VL 1B v2

Benchmarks, API Token Pricing, Context Window & Speed Comparison

由 GetInfoUs 研究团队审核最后审核日期：October 2026编辑政策
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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

Google 的 Gemini Embedding 001 与 NVIDIA 的 Llama Nemotron Embed VL 1B v2 是当前备受关注的两款主力 AI 模型。Gemini Embedding 001 提供 2K Token 上下文窗口，API 定价为每百万 Token 输入 $0.15 / 输出 $0.00；而 Llama Nemotron Embed VL 1B v2 提供 32K Token 上下文窗口，定价为每百万 Token 输入 $0.02 / 输出 $0.02。以下为两者的基准测试、延迟与成本全面对比。

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Google

Gemini Embedding 001

高维向量嵌入与检索

Google 推出的 Gemini Embedding 001 是一款专为高维向量表征、语义检索与企业级 RAG 知识库构建的大模型，具备 2K 上下文窗口、400 Token/秒生成速度与 70/100 综合智能指数（每百万 Token $0.15 输入 / $0 输出）。

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

高维向量嵌入与检索

NVIDIA 推出的 Llama Nemotron Embed VL 1B v2 是一款专为高维向量表征、语义检索与企业级 RAG 知识库构建的大模型，具备 32K 上下文窗口、280 Token/秒生成速度与 32/100 综合智能指数（每百万 Token $0.02 输入 / $0.02 输出）。

Intelligence Index32 / 100
Input Token:

$0.02 / 1M

Output Token:

$0.02 / 1M

Context Window:

32K tokens

SWE-bench Verified:

N/A

核心差异一览

  • 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

各项指标谁更胜一筹？

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

权威基准测试得分对比

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

延迟与响应速度

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

实时 Token 成本模拟器

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

  • 提供 2K 上下文窗口与最高 4K 输出容量，核心基准表现优异（Arena Elo 1280）
  • 高达 400 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 150 毫秒
  • 极具竞争力的 API 定价（每百万 Token $0.15 输入 / $0 输出），原生支持提示词缓存、函数调用与结构化 JSON 输出
Considerations & Trade-offs
  • 闭源专有云端模型，不支持下载权重进行离线本地物理隔离部署
  • 在超长链路的前沿科研推导任务上，深度略逊于顶配重型旗舰推理模型

Llama Nemotron Embed VL 1B v2 · Strengths & Key Advantages

  • 提供 32K 上下文窗口与最高 2K 输出容量，核心基准表现优异（Arena Elo 1210）
  • 高达 280 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 80 毫秒
  • 开放模型权重 (1B)，支持通过 vLLM、SGLang 或 Ollama 在私有 GPU 集群上本地部署与微调
Considerations & Trade-offs
  • 在本地以满血精度运行需要较高的 GPU 显存 (VRAM) 与运维基础设施
  • 在超长链路的前沿科研推导任务上，深度略逊于顶配重型旗舰推理模型

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

技术规格参数

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: 常见问题解答 (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.

综合结论

综合来看，Gemini Embedding 001（适合：专为高维向量表征、语义检索与企业级 RAG 知识库构建的任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。）与 Llama Nemotron Embed VL 1B v2（适合：专为高维向量表征、语义检索与企业级 RAG 知识库构建的任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。）各具优势：Gemini Embedding 001 在综合智能与编程基准测试中表现更强，Gemini Embedding 001 拥有更快的 Token 生成速度，而 Llama Nemotron Embed VL 1B v2 则在规模化 API 部署中具备更高的成本效益。

常见问题 (FAQ)

在编程和深度推理方面，Gemini Embedding 001 和 Llama Nemotron Embed VL 1B v2 哪个更强？+

在综合智能指数与编程基准评测中，Gemini Embedding 001 表现占优。建议结合上方的 SWE-bench Verified 与 GPQA Diamond 跑分根据具体场景选择。

Gemini Embedding 001 与 Llama Nemotron Embed VL 1B v2 的 API 价格哪个更便宜？+

Gemini Embedding 001 的 API 价格为输入 $0.15 / 输出 $0.00（每百万 Token），Llama Nemotron Embed VL 1B v2 为输入 $0.02 / 输出 $0.02。综合混合成本来看，Llama Nemotron Embed VL 1B v2 更具性价比。

哪款模型拥有更大的上下文窗口和更快的响应速度？+

Gemini Embedding 001 支持 2K Token 上下文窗口，而 Llama Nemotron Embed VL 1B v2 支持 32K Token。在流式生成速度方面，Gemini Embedding 001 的吞吐表现更快。

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.

数据来源: Google 与 NVIDIA 官方 API 文档、模型发布说明及独立基准测试报告 (2026)。