Gemini Embedding 2 vs GPT-5.5
Gemini Embedding 2 vs GPT-5.5: Gemini Embedding 2 is cheaper for input-heavy usage ($0.20/M vs $5.00/M input tokens), while GPT-5.5 is better for long-context tasks (1,050,000 tokens).
Direct answer: choose Gemini Embedding 2 for lower token spend and choose GPT-5.5 when your workload needs longer context.
Common pricing searches covered on this page: Gemini Embedding 2 vs GPT-5.5 • Gemini Embedding 2 vs GPT-5.5 pricing • Gemini Embedding 2 vs GPT-5.5 API pricing and embedding 2 vs 5 5 pricing.
Cost Comparison (1000 input + 500 output tokens, 100 requests/day)
Gemini Embedding 2
GPT-5.5
Cost Differences
GPT-5.5 costs more than Gemini Embedding 2
Quick Recommendation
Winner for direct API pricing: Gemini Embedding 2. At the default workload, Gemini Embedding 2 saves about $59.10/month ($719.05/year) versus GPT-5.5.
Feature Comparison
| Feature | Gemini Embedding 2 | GPT-5.5 |
|---|---|---|
| Provider | OpenAI | |
| Input Price | $0.20/1M tokens | $5.00/1M tokens |
| Output Price | $0.20/1M tokens | $30.00/1M tokens |
| Context Window | 8,192 tokens | 1,050,000 tokens |
| Max Output | 3,072 tokens | 128,000 tokens |
| Category | embedding | flagship |
| Capabilities | textvisionaudiovideoembeddings | textvisioncodereasoning |
| Release Date | 3/10/2026 | 4/24/2026 |
Gemini Embedding 2 vs GPT-5.5: Which Should You Choose?
Choosing between Gemini Embedding 2 and GPT-5.5 depends on your priorities: cost efficiency, context length, or raw capability. Gemini Embedding 2 is the more affordable option at $0.20/1M input tokens — 96% cheaper than GPT-5.5. Meanwhile, GPT-5.5 offers a significantly larger context window at 1,050,000 tokens vs 8,192 for Gemini Embedding 2.
These models come from different providers — Google and OpenAI — which means different API ecosystems, SDKs, rate limits, and terms of service. If you're already integrated with Google, switching to OpenAIinvolves migration effort beyond just pricing. Factor in your existing infrastructure when deciding.
These models target different tiers: Gemini Embedding 2 is a embedding model while GPT-5.5 is flagship. This means they're optimized for different workloads. GPT-5.5 targets more demanding workloads, while Gemini Embedding 2 provides a cost-effective option for everyday tasks.
Output costs matter too. Gemini Embedding 2 charges $0.20/1M output tokens vs $30.00 for GPT-5.5. For generation-heavy workloads (content creation, code generation, summarization), output pricing often dominates your bill. Gemini Embedding 2 has the edge here at $0.20/1M output tokens.
Multimodal capabilities: Both models support vision (image understanding), so you can send images alongside text prompts with either option.
Best Use Cases
Choose Gemini Embedding 2 when:
- • Budget is a primary concern
- • You need more capabilities (audio, video, embeddings)
- • You're already using Google's API ecosystem
Choose GPT-5.5 when:
- • You need a larger context window (1,050,000 tokens)
- • You need longer outputs (up to 128,000 tokens)
- • You're already using OpenAI's API ecosystem
Pros and Caveats at a Glance
Gemini Embedding 2
- • Input pricing: $0.20/M tokens
- • Output pricing: $0.20/M tokens
- • Context window: 8,192 tokens
- • Max output: 3,072 tokens
Watch out for
- • Smaller context window than GPT-5.5
GPT-5.5
- • Input pricing: $5.00/M tokens
- • Output pricing: $30.00/M tokens
- • Context window: 1,050,000 tokens
- • Max output: 128,000 tokens
Watch out for
- • Higher input cost than Gemini Embedding 2
- • Higher output cost than Gemini Embedding 2
Try Different Scenarios
Use the calculator below to see how costs change with different usage patterns
Gemini Embedding 2 (Google)
GPT-5.5 (OpenAI)
Start using Gemini Embedding 2 today
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Sign Up for OpenAI →Frequently Asked Questions
Which is cheaper, Gemini Embedding 2 or GPT-5.5?▼
What is the context window difference between Gemini Embedding 2 and GPT-5.5?▼
Which model is better for AI Chatbot?▼
Which model has better overall pricing for heavy usage?▼
Where can I compare Google and OpenAI API pricing beyond this model matchup?▼
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