Gemini Embedding 001 vs GPT-6.1 Sol
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Gemini Embedding 001 by Google against GPT-6.1 Sol by OpenAI across real-world coding, reasoning, token economics, and latency.
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Gemini Embedding 001
EmbeddingEnterprise semantic text embedding model powering retrieval, semantic search, and RAG pipelines.
$0.15 / 1M
$0.00 / 1M
2K tokens
N/A
GPT-6.1 Sol
Near-Astra ValueDelivers results close to GPT-6 Astra at roughly one fifth of the price — the latest 2026 release.
$2.00 / 1M
$10.00 / 1M
1M tokens
82.3%
Key Differences at a Glance
- Gemini Embedding 001 is 54.0x cheaper for API usage (input + output tokens combined).
- GPT-6.1 Sol has a larger context window (1M vs 2K tokens).
- Gemini Embedding 001 is faster at 400 tok/s vs 120 tok/s.
| Feature | Gemini Embedding 001 | GPT-6.1 Sol |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 70 | 94 |
Provider | OpenAI | |
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 | 128K |
Throughput Speed Tokens generated per second | 400 tok/s | 120 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 82.3% |
GPQA Diamond Graduate-level reasoning | N/A | 80.8% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.10 / 1M |
Who Wins Where?
At-a-glance decision frameworkEvaluations ongoing
Empirical Head-to-Head Benchmark Scores
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.
Latency & Response Time
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Real-Time Interactive Token Cost Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Gemini Embedding 001 · Strengths & Key Advantages
- High MTEB retrieval leaderboard ranking
- Low cost per million tokens
- Fast vector generation
Considerations & Trade-offs
- 2K context window limit
- Text-only — no visual embeddings
GPT-6.1 Sol · Strengths & Key Advantages
- Near-Astra results at roughly a fifth of the cost
- Excellent coding and long-horizon agent performance
- Strong reliability with far lower latency than Astra
- Near-flagship results at roughly a fifth of the price
Considerations & Trade-offs
- Still marginally behind Astra on the very hardest reasoning
- Same price as GPT-6 Sol, so the upgrade decision is easy
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Gemini Embedding 001 | GPT-6.1 Sol |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.05 | $2.05 |
Output Tokens / Task Tokens generated per task | 0K | 62K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 36K |
Technical Specifications
Architecture, release timeline, capabilities, and modalities.
| Feature | Gemini Embedding 001 | GPT-6.1 Sol |
|---|---|---|
| Official API Model ID (SDK) | models/gemini-embedding-001 | gpt-6-1-sol |
| 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 | April 2026 |
| Architecture | Dense semantic text embedding transformer | Near-Astra unified reasoning model |
| 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 | Flat rate ($2/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Implicit + Explicit Context Caching at $0/1M with configurable TTL | Automatic prefix caching at $0.1/1M (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) |
| 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 |
| Output Modalities | embedding | text |
Gemini Embedding 001 vs GPT-6.1 Sol: Quick Answers
Which is more intelligent, Gemini Embedding 001 or GPT-6.1 Sol?
GPT-6.1 Sol is more intelligent. It scores 94 compared with 70 on the composite Intelligence Index.
Which model is faster?
Gemini Embedding 001 is faster, generating 400 tokens per second compared with 120 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 1.12s.
Which model has a larger context window?
GPT-6.1 Sol has a larger context window, supporting 1M tokens compared with 2K tokens.
Verdict
Choose Gemini Embedding 001 for enterprise vector search, document indexing, and semantic clustering, or choose GPT-6.1 Sol for the best value frontier model for professional and agentic work.
Frequently asked questions
Which is cheaper: Gemini Embedding 001 or GPT-6.1 Sol?+
Gemini Embedding 001 costs $0.15/1M input and $0/1M output tokens, while GPT-6.1 Sol costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Gemini Embedding 001 or GPT-6.1 Sol?+
Gemini Embedding 001 supports 2K tokens whereas GPT-6.1 Sol 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.
Sources: Google & OpenAI Official Documentation