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Gemini Embedding 2 vs Llama Nemotron Rerank VL 1B v2

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Gemini Embedding 2 vs Llama Nemotron Rerank VL 1B v2: Benchmarks, Pricing & Speed Comparison

Gemini Embedding 2 dari Google dan Llama Nemotron Rerank VL 1B v2 dari NVIDIA adalah dua model AI unggulan. Gemini Embedding 2 menawarkan jendela konteks 8.2K token dengan harga $0.20/$0.00 per 1 juta token, sedangkan Llama Nemotron Rerank VL 1B v2 menyediakan konteks 128K token seharga $0.03/$0.03 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.

Perbandingan Langsung (VS)

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Gemini Embedding 2

Embedding Vektor & RAG

Gemini Embedding 2 dari Google adalah model AI yang dirancang untuk pencarian vektor semantik, pengindeksan dokumen, dan pipeline RAG perusahaan, menghadirkan jendela konteks 8K, kecepatan 350 token/detik, indeks kecerdasan 78/100, serta ta

Intelligence Index78 / 100
Input Token:

$0.20 / 1M

Output Token:

$0.00 / 1M

Context Window:

8.2K tokens

SWE-bench Verified:

N/A

NVIDIA

Llama Nemotron Rerank VL 1B v2

Model Ringan & Hemat Biaya

Llama Nemotron Rerank VL 1B v2 dari NVIDIA adalah model AI yang dirancang untuk efisiensi biaya API maksimal per token dan throughput produksi yang ringan, menghadirkan jendela konteks 128K, kecepatan 240 token/detik, indeks kecerdasan 33/1

Intelligence Index33 / 100
Input Token:

$0.03 / 1M

Output Token:

$0.03 / 1M

Context Window:

128K tokens

SWE-bench Verified:

N/A

Perbedaan Utama Sekilas

  • 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.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGemini Embedding 2Llama Nemotron Rerank VL 1B v2
Intelligence Index
Composite reasoning & knowledge score (0-100)
7833
Provider
GoogleNVIDIA
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 text128K tokens~192 A4 pages of text
Max Output Tokens
Per single request
4K2K
Throughput Speed
Tokens generated per second
350 tok/s240 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

Siapa Unggul di Mana?

At-a-glance decision framework
Coding

Evaluations ongoing

Cost
Llama Nemotron Rerank VL 1B v2
3.6x cheaper monthly runtime
Speed
Gemini Embedding 2
+46% faster generation
Context
Llama Nemotron Rerank VL 1B v2
128K tokens window

Skor Benchmark Terverifikasi

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

Gemini Embedding 2Llama Nemotron Rerank VL 1B v2
LMSYS Chatbot Arena Elo

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

1340ptsvs1220pts
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

350 tok/s vs240 tok/s

Latensi & Waktu Respons

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 20.20s
Llama Nemotron Rerank VL 1B v20.09s
Est. End-to-End Response (500 tokens)TTFT + generation time
Gemini Embedding 21.63s
Llama Nemotron Rerank VL 1B v22.17s

Simulator Biaya Token Interaktif

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 2 Monthly API Bill
$0.30 / month
$0.2/1M in · $0/1M out
Llama Nemotron Rerank VL 1B v2 Monthly API Bill
$0.08 / month
$0.03/1M in · $0.03/1M out
Llama Nemotron Rerank VL 1B v2 saves approximately $0.22 / month (72%) under this workload.3.6x Savings

Gemini Embedding 2 · Strengths & Key Advantages

  • Jendela konteks 8K dengan kapasitas output maksimum hingga 4K token (Arena Elo 1340)
  • Kecepatan generasi tinggi 350 token/detik dengan latensi token pertama (TTFT) hanya 200 ms
  • Skema harga API kompetitif (tarif $0.2/$0 per 1M token) dengan dukungan cache prompt dan output JSON terstruktur
Considerations & Trade-offs
  • Model cloud proprietari tertutup; bobot model tidak dapat diunduh untuk penggunaan lokal offline
  • Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

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

  • Jendela konteks 128K dengan kapasitas output maksimum hingga 2K token (Arena Elo 1220)
  • Kecepatan generasi tinggi 240 token/detik dengan latensi token pertama (TTFT) hanya 90 ms
  • Bobot terbuka (1B) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama
Considerations & Trade-offs
  • Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
  • Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

Token Use & Task Economics

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

MetricGemini Embedding 2Llama 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
0K1K
Reasoning Tokens / Task
Thinking tokens burned per task
0K0K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

FeatureGemini Embedding 2Llama Nemotron Rerank VL 1B v2
Official API Model ID (SDK)models/gemini-embedding-2llama-nemotron-rerank-vl-1b-v2
API ProtocolGoogle Gemini API (generateContent) + OpenAI-Compatible SDKOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2026-042026-03
Knowledge CutoffFebruary 2026January 2026
ArchitectureUnified multimodal joint-embedding transformerCross-Encoder Multimodal Reranking 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 ($0.2/1M in) across full 8K windowFlat rate ($0.03/1M in) across full 128K window
Prompt Cache Policy & TTLImplicit + Explicit Context Caching at $0/1M with configurable TTLCached input at $0.008/1M tokens
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)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) · 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 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 Modalitiestext, vision, audio, videotext, vision
Output Modalitiesembeddingscore

Gemini Embedding 2 vs Llama Nemotron Rerank VL 1B v2: Jawaban Cepat (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.

Kesimpulan

Baik Gemini Embedding 2 (Sangat cocok untuk pencarian vektor semantik, pengindeksan dokumen, dan pipeline RAG perusahaan, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun Llama Nemotron Rerank VL 1B v2 (Sangat cocok untuk efisiensi biaya API maksimal per token dan throughput produksi yang ringan, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Gemini Embedding 2 unggul dalam penalaran dan pemrograman, Gemini Embedding 2 memberikan kecepatan generasi token lebih tinggi, dan Llama Nemotron Rerank VL 1B v2 lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, Gemini Embedding 2 atau Llama Nemotron Rerank VL 1B v2?+

Dalam indeks kecerdasan gabungan dan benchmark coding, Gemini Embedding 2 memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API Gemini Embedding 2 vs Llama Nemotron Rerank VL 1B v2?+

Gemini Embedding 2 mengenakan biaya $0.20 / $0.00 per 1M token input/output, sedangkan Llama Nemotron Rerank VL 1B v2 sebesar $0.03 / $0.03. Secara keseluruhan, Llama Nemotron Rerank VL 1B v2 lebih hemat biaya.

Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+

Gemini Embedding 2 mendukung konteks 8.2K token dibandingkan 128K token pada Llama Nemotron Rerank VL 1B v2, sementara Gemini Embedding 2 unggul dalam kecepatan output token per detik.

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.

Sumber resmi: Dokumentasi API resmi Google & NVIDIA serta evaluasi benchmark independen (2026).