Gemini Embedding 2 vs Llama Nemotron Rerank VL 1B v2
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Google 的 Gemini Embedding 2 与 NVIDIA 的 Llama Nemotron Rerank VL 1B v2 是当前备受关注的两款主力 AI 模型。Gemini Embedding 2 提供 8.2K Token 上下文窗口，API 定价为每百万 Token 输入 $0.20 / 输出 $0.00；而 Llama Nemotron Rerank VL 1B v2 提供 128K Token 上下文窗口，定价为每百万 Token 输入 $0.03 / 输出 $0.03。以下为两者的基准测试、延迟与成本全面对比。
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Gemini Embedding 2
高维向量嵌入与检索Google 推出的 Gemini Embedding 2 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 8K 上下文窗口、350 Token/秒生成速度与 78/100 综合智能指数（每百万 Token $0.2 输入 / $0 输出）。
$0.20 / 1M
$0.00 / 1M
8.2K tokens
N/A
Llama Nemotron Rerank VL 1B v2
轻量级高性价比模型NVIDIA 推出的 Llama Nemotron Rerank VL 1B v2 是一款兼顾极低 API 调用成本与高吞吐效率的轻量级大模型，具备 128K 上下文窗口、240 Token/秒生成速度与 33/100 综合智能指数（每百万 Token $0.03 输入 / $0.03 输出）。
$0.03 / 1M
$0.03 / 1M
128K tokens
N/A
核心差异一览
- Llama Nemotron Rerank VL 1B v2 is 3.6x cheaper for API usage (input + output tokens combined).
- Llama Nemotron Rerank VL 1B v2 has a larger context window (128K vs 8K tokens).
- Gemini Embedding 2 is faster at 350 tok/s vs 240 tok/s.
| Feature | Gemini Embedding 2 | Llama Nemotron Rerank VL 1B v2 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 78 | 33 |
Provider | NVIDIA | |
Input Price Cost per million input tokens | $0.20 / 1M | $0.03 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $0.03 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.04 / 1M | $0.01 / 1M |
Context Window Maximum input + output tokens | 8.2K tokens~12 A4 pages of text | 128K tokens~192 A4 pages of text |
Max Output Tokens Per single request | 4K | 2K |
Throughput Speed Tokens generated per second | 350 tok/s | 240 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 |
各项指标谁更胜一筹？
At-a-glance decision frameworkEvaluations ongoing
权威基准测试得分对比
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.
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.
实时 Token 成本模拟器
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Gemini Embedding 2 · Strengths & Key Advantages
- 提供 8K 上下文窗口与最高 4K 输出容量，核心基准表现优异（Arena Elo 1340）
- 高达 350 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 200 毫秒
- 极具竞争力的 API 定价（每百万 Token $0.2 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 Rerank VL 1B v2 · Strengths & Key Advantages
- 提供 128K 上下文窗口与最高 2K 输出容量，核心基准表现优异（Arena Elo 1220）
- 高达 240 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 90 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.
| Metric | Gemini Embedding 2 | Llama Nemotron Rerank VL 1B v2 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.08 | $0.01 |
Output Tokens / Task Tokens generated per task | 0K | 1K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Gemini Embedding 2 | Llama Nemotron Rerank VL 1B v2 |
|---|---|---|
| Official API Model ID (SDK) | models/gemini-embedding-2 | llama-nemotron-rerank-vl-1b-v2 |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-04 | 2026-03 |
| Knowledge Cutoff | February 2026 | January 2026 |
| Architecture | Unified multimodal joint-embedding transformer | Cross-Encoder Multimodal Reranking 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 ($0.2/1M in) across full 8K window | Flat rate ($0.03/1M in) across full 128K window |
| Prompt Cache Policy & TTL | Implicit + Explicit Context Caching at $0/1M with configurable TTL | Cached input at $0.008/1M tokens |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | 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) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) | 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, vision, audio, video | text, vision |
| Output Modalities | embedding | score |
Gemini Embedding 2 vs Llama Nemotron Rerank VL 1B v2: 常见问题解答 (FAQ)
Which is more intelligent, Gemini Embedding 2 or Llama Nemotron Rerank VL 1B v2?
Gemini Embedding 2 is more intelligent. It scores 78 compared with 33 on the composite Intelligence Index.
Which model is faster?
Gemini Embedding 2 is faster, generating 350 tokens per second compared with 240 tokens per second.
Which model is cheaper to run?
Llama Nemotron Rerank VL 1B v2 is cheaper at $0.01 per 1M tokens blended, compared with $0.04 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Llama Nemotron Rerank VL 1B v2 has lower latency, with a time to first token of 0.09s compared with 0.20s.
Which model has a larger context window?
Llama Nemotron Rerank VL 1B v2 has a larger context window, supporting 128K tokens compared with 8.2K tokens.
综合结论
综合来看，Gemini Embedding 2（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 Rerank VL 1B v2（适合：兼顾极低 API 调用成本与高吞吐效率的轻量级任务、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 2 在综合智能与编程基准测试中表现更强，Gemini Embedding 2 拥有更快的 Token 生成速度，而 Llama Nemotron Rerank 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 2 和 Llama Nemotron Rerank VL 1B v2 哪个更强？+
在综合智能指数与编程基准评测中，Gemini Embedding 2 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 2 与 Llama Nemotron Rerank VL 1B v2 的 API 价格哪个更便宜？+
Gemini Embedding 2 的 API 价格为输入 $0.20 / 输出 $0.00（每百万 Token），Llama Nemotron Rerank VL 1B v2 为输入 $0.03 / 输出 $0.03。综合混合成本来看，Llama Nemotron Rerank 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 2 支持 8.2K Token 上下文窗口，而 Llama Nemotron Rerank VL 1B v2 支持 128K Token。在流式生成速度方面，Gemini Embedding 2 的吞吐表现更快。
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
- Gemini Embedding 2 vs Gemini Embedding 001：基准测试、API 价格与速度全面对比 (2026)对比
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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.
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)。