Gemini Embedding 2 vs Gemini Embedding 001
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Gemini Embedding 2 dari Google dan Gemini Embedding 001 dari Google 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 Gemini Embedding 001 menyediakan konteks 2K token seharga $0.15/$0.00 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.
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Gemini Embedding 2
Embedding Vektor & RAGGemini 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
$0.20 / 1M
$0.00 / 1M
8.2K tokens
N/A
Gemini Embedding 001
Embedding Vektor & RAGGemini Embedding 001 dari Google adalah model AI yang dirancang untuk pencarian vektor semantik, pengindeksan dokumen, dan pipeline RAG perusahaan, menghadirkan jendela konteks 2K, kecepatan 400 token/detik, indeks kecerdasan 70/100, serta
$0.15 / 1M
$0.00 / 1M
2K tokens
N/A
Perbedaan Utama Sekilas
- Gemini Embedding 001 is 1.3x cheaper for API usage (input + output tokens combined).
- Gemini Embedding 2 has a larger context window (8K vs 2K tokens).
- Gemini Embedding 001 is faster at 400 tok/s vs 350 tok/s.
| Feature | Gemini Embedding 2 | Gemini Embedding 001 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 78 | 70 |
Provider | ||
Input Price Cost per million input tokens | $0.20 / 1M | $0.15 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.04 / 1M | $0.03 / 1M |
Context Window Maximum input + output tokens | 8.2K tokens~12 A4 pages of text | 2K tokens~3 A4 pages of text |
Max Output Tokens Per single request | 4K | 4K |
Throughput Speed Tokens generated per second | 350 tok/s | 400 tok/s |
SWE-bench Verified Real-world software engineering | N/A | N/A |
Siapa Unggul di Mana?
At-a-glance decision frameworkEvaluations ongoing
Skor Benchmark Terverifikasi
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.
Latensi & Waktu Respons
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulator Biaya Token Interaktif
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
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
Gemini Embedding 001 · Strengths & Key Advantages
- Jendela konteks 2K dengan kapasitas output maksimum hingga 4K token (Arena Elo 1280)
- Kecepatan generasi tinggi 400 token/detik dengan latensi token pertama (TTFT) hanya 150 ms
- Skema harga API kompetitif (tarif $0.15/$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
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Gemini Embedding 2 | Gemini Embedding 001 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.08 | $0.05 |
Output Tokens / Task Tokens generated per task | 0K | 0K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Spesifikasi Teknis
Architecture, release timeline, capabilities, and modalities.
| Feature | Gemini Embedding 2 | Gemini Embedding 001 |
|---|---|---|
| Official API Model ID (SDK) | models/gemini-embedding-2 | models/gemini-embedding-001 |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2026-04 | 2025-05 |
| Knowledge Cutoff | February 2026 | March 2025 |
| Architecture | Unified multimodal joint-embedding transformer | Dense semantic text embedding transformer |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.2/1M in) across full 8K window | Flat rate ($0.15/1M in) across full 2K window |
| Prompt Cache Policy & TTL | Implicit + Explicit Context Caching at $0/1M with configurable TTL | Implicit + Explicit Context Caching at $0/1M with configurable TTL |
| 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 | Built-in Google Search Grounding + Managed Python Code Execution Sandbox |
| Official Fine-Tuning Support | Vertex AI Supervised Adapter Tuning & Context Distillation | Vertex AI Supervised Adapter Tuning & Context Distillation |
| 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) |
| Quantization & Min GPU VRAM | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Google DeepMind Frontier Safety Framework Verified | Google DeepMind Frontier Safety Framework Verified |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text, vision, audio, video | text |
| Output Modalities | embedding | embedding |
Gemini Embedding 2 vs Gemini Embedding 001: Jawaban Cepat (FAQ)
Which is more intelligent, Gemini Embedding 2 or Gemini Embedding 001?
Gemini Embedding 2 is more intelligent. It scores 78 compared with 70 on the composite Intelligence Index.
Which model is faster?
Gemini Embedding 001 is faster, generating 400 tokens per second compared with 350 tokens per second.
Which model is cheaper to run?
Gemini Embedding 001 is cheaper at $0.03 per 1M tokens blended, compared with $0.04 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Gemini Embedding 001 has lower latency, with a time to first token of 0.15s compared with 0.20s.
Which model has a larger context window?
Gemini Embedding 2 has a larger context window, supporting 8.2K tokens compared with 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 Gemini Embedding 001 (Sangat cocok untuk pencarian vektor semantik, pengindeksan dokumen, dan pipeline RAG perusahaan, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Gemini Embedding 2 unggul dalam penalaran dan pemrograman, Gemini Embedding 001 memberikan kecepatan generasi token lebih tinggi, dan Gemini Embedding 001 lebih hemat biaya untuk penggunaan API skala besar.
Pertanyaan Umum (FAQ)
Mana yang lebih baik untuk coding dan penalaran, Gemini Embedding 2 atau Gemini Embedding 001?+
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 Gemini Embedding 001?+
Gemini Embedding 2 mengenakan biaya $0.20 / $0.00 per 1M token input/output, sedangkan Gemini Embedding 001 sebesar $0.15 / $0.00. Secara keseluruhan, Gemini Embedding 001 lebih hemat biaya.
Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+
Gemini Embedding 2 mendukung konteks 8.2K token dibandingkan 2K token pada Gemini Embedding 001, sementara Gemini Embedding 001 unggul dalam kecepatan output token per detik.
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.
Sumber resmi: Dokumentasi API resmi Google & Google serta evaluasi benchmark independen (2026).