Gemini Embedding 001 vs GPT-6 Astra
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Gemini Embedding 001 by Google against GPT-6 Astra by OpenAI across real-world coding, reasoning, token economics, and latency.
Changez et Comparez les Modèles
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Live comparison: no in-depth editorial review has been published for this pair yet.
Gemini Embedding 001
Embeddings Vectoriels et RAGGemini Embedding 001 de Google est un modèle d’IA conçu pour la recherche vectorielle sémantique, l’indexation haute dimension et les pipelines RAG, offrant une fenêtre de contexte de 2K, une vitesse de 400 tokens/s, un indice d’intelligenc
$0.15 / 1M
$0.00 / 1M
2K tokens
N/A
GPT-6 Astra
Modèle Audio et Vocal NatifGPT-6 Astra de OpenAI est un modèle d’IA conçu pour la synthèse vocale naturelle, la transcription audio et le dialogue parlé en temps réel, offrant une fenêtre de contexte de 272K, une vitesse de 100 tokens/s, un indice d’intelligence de 9
$10.00 / 1M
$50.00 / 1M
272K tokens
82.4%
Différences Clés en un Coup d’Œil
- Gemini Embedding 001 is 273.3x cheaper for API usage (input + output tokens combined).
- GPT-6 Astra has a larger context window (272K vs 2K tokens).
- Gemini Embedding 001 is faster at 400 tok/s vs 100 tok/s.
| Caractéristique | Gemini Embedding 001 | GPT-6 Astra |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 70 | 96 |
Provider | OpenAI | |
Input Price Cost per million input tokens | $0.15 / 1M | $10.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $50.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.03 / 1M | $7.70 / 1M |
Context Window Maximum input + output tokens | 2K tokens~3 A4 pages of text | 272K tokens~408 A4 pages of text |
Max Output Tokens Per single request | 4K | 128K |
Throughput Speed Tokens generated per second | 400 tok/s | 100 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 82.4% |
GPQA Diamond Graduate-level reasoning | N/A | 80.9% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $1.00 / 1M |
Qui gagne où ?
At-a-glance decision frameworkEvaluations ongoing
Scores de Benchmarks Vérifiés
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.
Latence et Temps de Réponse
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulateur Interactif de Coût de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Gemini Embedding 001 · Strengths & Key Advantages
- Fenêtre de contexte de 2K et capacité de sortie maximale de 4K tokens (Arena Elo 1280)
- Débit rapide de 400 tokens/seconde avec une latence au premier token (TTFT) de 150 ms
- Tarification API optimisée ($0.15/$0 par million de tokens) avec prise en charge du cache de prompts et des schémas JSON stricts
Considerations & Trade-offs
- Modèle cloud propriétaire ne permettant pas le téléchargement local des poids sur serveur isolé
- Sur les problèmes de recherche fondamentale très complexes, les modèles de raisonnement lourd restent supérieurs
GPT-6 Astra · Strengths & Key Advantages
- Fenêtre de contexte de 272K et capacité de sortie maximale de 128K tokens (SWE-bench Verified 82.4% · GPQA Diamond 80.9%)
- Débit rapide de 100 tokens/seconde avec une latence au premier token (TTFT) de 950 ms
- Tarification API optimisée ($10/$50 par million de tokens) avec prise en charge du cache de prompts et des schémas JSON stricts
Considerations & Trade-offs
- Modèle cloud propriétaire ne permettant pas le téléchargement local des poids sur serveur isolé
- Pour les charges à très fort volume, l’activation du cache de prompts et de l’API Batch est recommandée
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Gemini Embedding 001 | GPT-6 Astra |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.05 | $2.15 |
Output Tokens / Task Tokens generated per task | 0K | 58K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 32K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | Gemini Embedding 001 | GPT-6 Astra |
|---|---|---|
| Official API Model ID (SDK) | models/gemini-embedding-001 | gpt-6-astra |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-05 | 2026-09 |
| Knowledge Cutoff | March 2025 | June 2026 |
| Architecture | Dense semantic text embedding transformer | Omni-Adaptive Autoregressive with Dynamic CoT |
| 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 ($0.15/1M in) across full 2K window | Flat rate ($10/1M in) across full 272K window |
| Prompt Cache Policy & TTL | Implicit + Explicit Context Caching at $0/1M with configurable TTL | Automatic prefix caching at $1/1M (no extra cache-write fee) |
| 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 | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Vertex AI Supervised Adapter Tuning & Context Distillation | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) |
| 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) |
| 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 | OpenAI Preparedness Framework Audited & System Card Published |
| 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 |
| Output Modalities | embedding | text, audio |
Gemini Embedding 001 vs GPT-6 Astra: Réponses Rapides (FAQ)
Which is more intelligent, Gemini Embedding 001 or GPT-6 Astra?
GPT-6 Astra is more intelligent. It scores 96 compared with 70 on the composite Intelligence Index.
Which model is faster?
Gemini Embedding 001 is faster, generating 400 tokens per second compared with 100 tokens per second.
Which model is cheaper to run?
Gemini Embedding 001 is cheaper at $0.03 per 1M tokens blended, compared with $7.70 (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 0.95s.
Which model has a larger context window?
GPT-6 Astra has a larger context window, supporting 272K tokens compared with 2K tokens.
Verdict
Choose Gemini Embedding 001 for conçu pour la recherche vectorielle sémantique, l’indexation haute dimension et les pipelines rag, l’intégration rag et les pipelines de production évolutifs., or choose GPT-6 Astra for conçu pour la synthèse vocale naturelle, la transcription audio et le dialogue parlé en temps réel, l’intégration rag et les pipelines de production évolutifs..
Questions fréquentes
Which is cheaper: Gemini Embedding 001 or GPT-6 Astra?+
Gemini Embedding 001 costs $0.15/1M input and $0/1M output tokens, while GPT-6 Astra costs $10/1M input and $50/1M output tokens.
Which model has a larger context window: Gemini Embedding 001 or GPT-6 Astra?+
Gemini Embedding 001 supports 2K tokens whereas GPT-6 Astra supports 272K tokens.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: Google & OpenAI Official Documentation