Codex Mini vs Gemini Embedding 2
Codex Mini vs Gemini Embedding 2: Gemini Embedding 2 is cheaper for input-heavy usage ($0.20/M vs $1.50/M input tokens), while Codex Mini is better for long-context tasks (200,000 tokens).
Direct answer: choose Gemini Embedding 2 for lower token spend and choose Codex Mini 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)
Codex Mini
Gemini Embedding 2
Cost Differences
Gemini Embedding 2 costs less than Codex Mini
Quick Recommendation
Winner for direct API pricing: Gemini Embedding 2. At the default workload, Gemini Embedding 2 saves about $12.60/month ($153.30/year) versus Codex Mini.
Feature Comparison
| Feature | Codex Mini | Gemini Embedding 2 |
|---|---|---|
| Provider | OpenAI | |
| Input Price | $1.50/1M tokens | $0.20/1M tokens |
| Output Price | $6.00/1M tokens | $0.20/1M tokens |
| Context Window | 200,000 tokens | 8,192 tokens |
| Max Output | 32,768 tokens | 3,072 tokens |
| Category | efficient | embedding |
| Capabilities | textcodereasoning | textvisionaudiovideoembeddings |
| Release Date | 2/2/2026 | 3/10/2026 |
Codex Mini vs Gemini Embedding 2: Which Should You Choose?
Choosing between Codex Mini and Gemini Embedding 2 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 — 87% cheaper than Codex Mini. Meanwhile, Codex Mini offers a significantly larger context window at 200,000 tokens vs 8,192 for Gemini Embedding 2.
These models come from different providers — OpenAI and Google — which means different API ecosystems, SDKs, rate limits, and terms of service. If you're already integrated with OpenAI, switching to Googleinvolves migration effort beyond just pricing. Factor in your existing infrastructure when deciding.
These models target different tiers: Codex Mini is a efficient model while Gemini Embedding 2 is embedding. This means they're optimized for different workloads. Gemini Embedding 2 targets more demanding workloads, while Codex Mini provides a cost-effective option for everyday tasks.
Output costs matter too. Codex Mini charges $6.00/1M output tokens vs $0.20 for Gemini Embedding 2. 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 Codex Mini is text-only. If your application needs image understanding, this narrows your choice.
Best Use Cases
Choose Codex Mini when:
- • You need a larger context window (200,000 tokens)
- • You need longer outputs (up to 32,768 tokens)
- • You're already using OpenAI's API ecosystem
- • You're running high-volume, latency-sensitive workloads
Choose Gemini Embedding 2 when:
- • Budget is a primary concern
- • You need more capabilities (vision, audio, video, embeddings)
- • You're already using Google's API ecosystem
Pros and Caveats at a Glance
Codex Mini
- • Input pricing: $1.50/M tokens
- • Output pricing: $6.00/M tokens
- • Context window: 200,000 tokens
- • Max output: 32,768 tokens
Watch out for
- • Higher input cost than Gemini Embedding 2
- • Higher output cost than Gemini Embedding 2
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 Codex Mini
Try Different Scenarios
Use the calculator below to see how costs change with different usage patterns
Codex Mini (OpenAI)
Gemini Embedding 2 (Google)
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Which is cheaper, Codex Mini or Gemini Embedding 2?▼
What is the context window difference between Codex Mini and Gemini Embedding 2?▼
Which model is better for AI Chatbot?▼
Which model has better overall pricing for heavy usage?▼
Where can I compare OpenAI and Google API pricing beyond this model matchup?▼
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