Gemini Embedding 2 vs GPT-5.4 nano
Gemini Embedding 2 vs GPT-5.4 nano: Gemini Embedding 2 is cheaper for input-heavy usage ($0.20/M vs $0.20/M input tokens), while GPT-5.4 nano is better for long-context tasks (128,000 tokens).
Direct answer: choose Gemini Embedding 2 for lower token spend and choose GPT-5.4 nano when your workload needs longer context.
Compare input and output token pricing, context windows, and monthly cost estimates on one page so you can pick the cheaper model fast.
Cost Comparison (1000 input + 500 output tokens, 100 requests/day)
Gemini Embedding 2
GPT-5.4 nano
Cost Differences
GPT-5.4 nano 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 $1.575/month ($19.1625/year) versus GPT-5.4 nano.
Feature Comparison
| Feature | Gemini Embedding 2 | GPT-5.4 nano |
|---|---|---|
| Provider | OpenAI | |
| Input Price | $0.20/1M tokens | $0.20/1M tokens |
| Output Price | $0.20/1M tokens | $1.25/1M tokens |
| Context Window | 8,192 tokens | 128,000 tokens |
| Max Output | 3,072 tokens | 8,192 tokens |
| Category | embedding | efficient |
| Capabilities | textvisionaudiovideoembeddings | text |
| Release Date | 3/10/2026 | 3/6/2026 |
Gemini Embedding 2 vs GPT-5.4 nano: Which Should You Choose?
Choosing between Gemini Embedding 2 and GPT-5.4 nano 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. Meanwhile, GPT-5.4 nano offers a significantly larger context window at 128,000 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-5.4 nano is efficient. This means they're optimized for different workloads. GPT-5.4 nano 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 $1.25 for GPT-5.4 nano. 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: Gemini Embedding 2 supports vision (image inputs) while GPT-5.4 nano is text-only. If your application needs image understanding, this narrows your choice.
Best Use Cases
Choose Gemini Embedding 2 when:
- • You need more capabilities (vision, audio, video, embeddings)
- • You're already using Google's API ecosystem
Choose GPT-5.4 nano when:
- • You need a larger context window (128,000 tokens)
- • You need longer outputs (up to 8,192 tokens)
- • You're already using OpenAI's API ecosystem
- • You're running high-volume, latency-sensitive workloads
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-5.4 nano
GPT-5.4 nano
- • Input pricing: $0.20/M tokens
- • Output pricing: $1.25/M tokens
- • Context window: 128,000 tokens
- • Max output: 8,192 tokens
Watch out for
- • 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-5.4 nano (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-5.4 nano?▼
What is the context window difference between Gemini Embedding 2 and GPT-5.4 nano?▼
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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