Gemini Embedding 001 vs Gemini 4 Argon
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Gemini Embedding 001 by Google against Gemini 4 Argon by Google across real-world coding, reasoning, token economics, and latency.
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Gemini Embedding 001
Hochleistungs-KI-ModellGemini Embedding 001 von Google ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 2K Token zu API-Preisen von $0.15/1M Input- und $0/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$0.15 / 1M
$0.00 / 1M
2K tokens
N/A
Gemini 4 Argon
Deep-Reasoning-ModellGemini 4 Argon von Google ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 1M Token zu API-Preisen von $2/1M Input- und $10/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$2.00 / 1M
$10.00 / 1M
1M tokens
81.9%
Wichtigste Unterschiede auf einen Blick
- Gemini Embedding 001 is 54.0x cheaper for API usage (input + output tokens combined).
- Gemini 4 Argon has a larger context window (1M vs 2K tokens).
- Gemini Embedding 001 is faster at 400 tok/s vs 115 tok/s.
| Feature | Gemini Embedding 001 | Gemini 4 Argon |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 70 | 97 |
Provider | ||
Input Price Cost per million input tokens | $0.15 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.03 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 2K tokens~3 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 4K | 1000K |
Throughput Speed Tokens generated per second | 400 tok/s | 115 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 81.9% |
GPQA Diamond Graduate-level reasoning | N/A | 81.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.10 / 1M |
Wer gewinnt in welcher Disziplin?
At-a-glance decision frameworkEvaluations ongoing
Geprüfte Benchmark-Ergebnisse im Vergleich
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
Latenz & Antwortzeit
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Interaktiver Token-Kosten-Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Gemini Embedding 001 · Strengths & Key Advantages
- Fortschrittliche Dense semantic text embedding transformer-Architektur von Google mit 2K Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($0.15/1M Input, $0/1M Output) bei hoher Durchsatzrate
- Hohe Präzision bei Code-Generierung, logischem Schließen und mehrsprachigen Benchmarks
Considerations & Trade-offs
- Bei sehr großen Kontextmengen ist eine sorgfältige Token-Budgetierung ratsam
- Spezialisierte Fachaufgaben profitieren von gezieltem Prompt-Engineering
Gemini 4 Argon · Strengths & Key Advantages
- Fortschrittliche Pathways Multimodal Sparse Mixture-of-Experts-Architektur von Google mit 1M Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($2/1M Input, $10/1M Output) bei hoher Durchsatzrate
- Hohe Präzision bei Code-Generierung, logischem Schließen und mehrsprachigen Benchmarks
Considerations & Trade-offs
- Bei sehr großen Kontextmengen ist eine sorgfältige Token-Budgetierung ratsam
- Spezialisierte Fachaufgaben profitieren von gezieltem Prompt-Engineering
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Gemini Embedding 001 | Gemini 4 Argon |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.05 | $2.45 |
Output Tokens / Task Tokens generated per task | 0K | 75K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 36K |
Technische Spezifikationen
Architecture, release timeline, capabilities, and modalities.
| Feature | Gemini Embedding 001 | Gemini 4 Argon |
|---|---|---|
| Official API Model ID (SDK) | models/gemini-embedding-001 | models/gemini-4-argon |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2025-05 | 2026-09 |
| Knowledge Cutoff | March 2025 | June 2026 |
| Architecture | Dense semantic text embedding transformer | Pathways Multimodal Sparse Mixture-of-Experts |
| 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) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.15/1M in) across full 2K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Implicit + Explicit Context Caching at $0/1M with configurable TTL | Implicit + Explicit Context Caching at $0.1/1M with configurable TTL |
| Computer Use / GUI Agent | Text-only completion (No GUI action loop) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| MCP & Parallel Tool Calling | Prompt-based JSON extraction only | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| 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) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) |
| 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 | text, vision, audio, video |
| Output Modalities | embedding | text |
Gemini Embedding 001 vs Gemini 4 Argon: Schnelle Antworten (FAQ)
Which is more intelligent, Gemini Embedding 001 or Gemini 4 Argon?
Gemini 4 Argon is more intelligent. It scores 97 compared with 70 on the composite Intelligence Index.
Which model is faster?
Gemini Embedding 001 is faster, generating 400 tokens per second compared with 115 tokens per second.
Which model is cheaper to run?
Gemini Embedding 001 is cheaper at $0.03 per 1M tokens blended, compared with $1.47 (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 0.85s.
Which model has a larger context window?
Gemini 4 Argon has a larger context window, supporting 1M tokens compared with 2K tokens.
Fazit
Choose Gemini Embedding 001 for unternehmensautomatisierung, software-engineering, autonome ki-agenten und google-produktionssysteme, or choose Gemini 4 Argon for unternehmensautomatisierung, software-engineering, autonome ki-agenten und google-produktionssysteme.
Häufig gestellte Fragen (FAQ)
Which is cheaper: Gemini Embedding 001 or Gemini 4 Argon?+
Gemini Embedding 001 costs $0.15/1M input and $0/1M output tokens, while Gemini 4 Argon costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Gemini Embedding 001 or Gemini 4 Argon?+
Gemini Embedding 001 supports 2K tokens whereas Gemini 4 Argon supports 1M tokens.
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.
Offizielle Quellen: Google & Google Official Documentation