Llama Nemotron Rerank VL 1B v2 vs Llama Nemotron Embed VL 1B v2
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Llama Nemotron Rerank VL 1B v2 by NVIDIA 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, Llama Nemotron Embed VL 1B v2 is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Llama Nemotron Rerank VL 1B v2 provides an expanded memory horizon with 128K tokens. In latency-sensitive production environments, Llama Nemotron Embed VL 1B v2 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.
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Llama Nemotron Rerank VL 1B v2
오픈 웨이트 모델NVIDIA의 Llama Nemotron Rerank VL 1B v2 모델은 128K 토큰 컨텍스트 윈도우와 입력 $0.03/1M 토큰, 출력 $0.03/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$0.03 / 1M
$0.03 / 1M
128K tokens
N/A
Llama Nemotron Embed VL 1B v2
오픈 웨이트 모델NVIDIA의 Llama Nemotron Embed VL 1B v2 모델은 32K 토큰 컨텍스트 윈도우와 입력 $0.02/1M 토큰, 출력 $0.02/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$0.02 / 1M
$0.02 / 1M
32K tokens
N/A
한눈에 보는 핵심 차이점
- Llama Nemotron Embed VL 1B v2 is 1.5x cheaper for API usage (input + output tokens combined).
- Llama Nemotron Rerank VL 1B v2 has a larger context window (128K vs 32K tokens).
- Llama Nemotron Embed VL 1B v2 is faster at 280 tok/s vs 240 tok/s.
| Feature | Llama Nemotron Rerank VL 1B v2 | Llama Nemotron Embed VL 1B v2 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 33 | 32 |
Provider | NVIDIA | NVIDIA |
Input Price Cost per million input tokens | $0.03 / 1M | $0.02 / 1M |
Output Price Cost per million output tokens | $0.03 / 1M | $0.02 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.01 / 1M | $0.01 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 32K tokens~48 A4 pages of text |
Max Output Tokens Per single request | 2K | 2K |
Throughput Speed Tokens generated per second | 240 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 | $0.01 / 1M | $0.01 / 1M |
부문별 우위 모델
At-a-glance decision frameworkEvaluations ongoing
검증된 벤치마크 점수 비교
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.
지연 시간(Latency) 및 응답 속도
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
실시간 API 토큰 비용 시뮬레이터
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Llama Nemotron Rerank VL 1B v2 · Strengths & Key Advantages
- NVIDIA의 첨단 Cross-Encoder Multimodal Reranking Transformer 아키텍처와 최대 128K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0.03/1M, 출력 $0.03/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
Llama Nemotron Embed VL 1B v2 · Strengths & Key Advantages
- NVIDIA의 첨단 Dense Multimodal Embedding Transformer 아키텍처와 최대 32K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0.02/1M, 출력 $0.02/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Llama Nemotron Rerank VL 1B v2 | Llama Nemotron Embed VL 1B v2 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.01 | $0.01 |
Output Tokens / Task Tokens generated per task | 1K | 1K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
기술 사양 (Specifications)
Architecture, release timeline, capabilities, and modalities.
| Feature | Llama Nemotron Rerank VL 1B v2 | Llama Nemotron Embed VL 1B v2 |
|---|---|---|
| Official API Model ID (SDK) | llama-nemotron-rerank-vl-1b-v2 | llama-nemotron-embed-vl-1b-v2 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-03 | 2026-02 |
| Knowledge Cutoff | January 2026 | December 2025 |
| Architecture | Cross-Encoder Multimodal Reranking Transformer | Dense Multimodal Embedding Transformer |
| Parameters | 1B | 1B |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | 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 ($0.03/1M in) across full 128K window | Flat rate ($0.02/1M in) across full 32K window |
| Prompt Cache Policy & TTL | Cached input at $0.008/1M tokens | 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 | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | 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) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| 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) | 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 | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision | text, vision |
| Output Modalities | score | embedding |
Llama Nemotron Rerank VL 1B v2 vs Llama Nemotron Embed VL 1B v2: 핵심 요약 문답 (FAQ)
Which is more intelligent, Llama Nemotron Rerank VL 1B v2 or Llama Nemotron Embed VL 1B v2?
Llama Nemotron Rerank VL 1B v2 is more intelligent. It scores 33 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 240 tokens per second.
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 0.09s.
Which model has a larger context window?
Llama Nemotron Rerank VL 1B v2 has a larger context window, supporting 128K tokens compared with 32K tokens.
종합 평가
Verdict & Model Selection Recommendation: Choose Llama Nemotron Rerank VL 1B v2 if your priority is RAG precision reranking, document search optimization, and recommendation engines. 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.
자주 묻는 질문 (FAQ)
How does Llama Nemotron Rerank VL 1B v2 compare to Llama Nemotron Embed VL 1B v2 in API token pricing and cost per token?+
Llama Nemotron Rerank VL 1B v2 costs $0.03 per 1M input tokens and $0.03 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: Llama Nemotron Rerank VL 1B v2 or Llama Nemotron Embed VL 1B v2?+
Llama Nemotron Rerank VL 1B v2 supports a context window of 128K tokens (~96k words), while Llama Nemotron Embed VL 1B v2 supports 32K tokens (~24k words). Therefore, Llama Nemotron Rerank 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: Llama Nemotron Rerank VL 1B v2 or Llama Nemotron Embed VL 1B v2?+
Llama Nemotron Rerank VL 1B v2 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, Llama Nemotron Rerank VL 1B v2 provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Llama Nemotron Rerank VL 1B v2 and Llama Nemotron Embed VL 1B v2?+
Llama Nemotron Rerank VL 1B v2 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 Llama Nemotron Rerank VL 1B v2 and Llama Nemotron Embed VL 1B v2?+
Llama Nemotron Rerank VL 1B v2 generates approximately 240 tokens per second, while Llama Nemotron Embed VL 1B v2 delivers 280 tokens per second. In latency-sensitive production environments, Llama Nemotron Embed VL 1B v2 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Llama Nemotron Rerank VL 1B v2 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 Llama Nemotron Rerank VL 1B v2. 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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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.
공식 출처: GetInfoUs Independent AI Benchmarks & Official API Specs