Gemini 3.1 Pro vs GPT-5.4 mini
Gemini 3.1 Pro vs GPT-5.4 mini: GPT-5.4 mini is cheaper for input-heavy usage ($0.75/M vs $2.00/M input tokens), while GPT-5.4 mini is better for long-context tasks (1,050,000 tokens).
Direct answer: choose GPT-5.4 mini for lower token spend and choose GPT-5.4 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)
Gemini 3.1 Pro
GPT-5.4 mini
Cost Differences
GPT-5.4 mini costs less than Gemini 3.1 Pro
Quick Recommendation
Winner for direct API pricing: GPT-5.4 mini. At the default workload, GPT-5.4 mini saves about $15.00/month ($182.50/year) versus Gemini 3.1 Pro.
Feature Comparison
| Feature | Gemini 3.1 Pro | GPT-5.4 mini |
|---|---|---|
| Provider | OpenAI | |
| Input Price | $2.00/1M tokens | $0.75/1M tokens |
| Output Price | $12.00/1M tokens | $4.50/1M tokens |
| Context Window | 1,000,000 tokens | 1,050,000 tokens |
| Max Output | 65,536 tokens | 65,536 tokens |
| Category | flagship | efficient |
| Capabilities | textvisionaudiovideocodereasoning | textvisioncode |
| Release Date | 2/19/2026 | 3/6/2026 |
Gemini 3.1 Pro vs GPT-5.4 mini: Which Should You Choose?
Choosing between Gemini 3.1 Pro and GPT-5.4 mini depends on your priorities: cost efficiency, context length, or raw capability. GPT-5.4 mini is the more affordable option at $0.75/1M input tokens — 63% cheaper than Gemini 3.1 Pro. Meanwhile, GPT-5.4 mini offers a significantly larger context window at 1,050,000 tokens vs 1,000,000 for Gemini 3.1 Pro.
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 3.1 Pro is a flagship model while GPT-5.4 mini is efficient. This means they're optimized for different workloads. Gemini 3.1 Pro is built for complex tasks that require deeper reasoning, while GPT-5.4 mini offers better value for routine operations.
Output costs matter too. Gemini 3.1 Pro charges $12.00/1M output tokens vs $4.50 for GPT-5.4 mini. For generation-heavy workloads (content creation, code generation, summarization), output pricing often dominates your bill. GPT-5.4 mini has the edge here at $4.50/1M 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 3.1 Pro when:
- • You need more capabilities (audio, video, reasoning)
- • You're already using Google's API ecosystem
Choose GPT-5.4 mini when:
- • Budget is a primary concern
- • You need a larger context window (1,050,000 tokens)
- • You're already using OpenAI's API ecosystem
- • You're running high-volume, latency-sensitive workloads
Pros and Caveats at a Glance
Gemini 3.1 Pro
- • Input pricing: $2.00/M tokens
- • Output pricing: $12.00/M tokens
- • Context window: 1,000,000 tokens
- • Max output: 65,536 tokens
Watch out for
- • Higher input cost than GPT-5.4 mini
- • Higher output cost than GPT-5.4 mini
- • Smaller context window than GPT-5.4 mini
GPT-5.4 mini
- • Input pricing: $0.75/M tokens
- • Output pricing: $4.50/M tokens
- • Context window: 1,050,000 tokens
- • Max output: 65,536 tokens
Watch out for
- • Trade-offs are minor in this matchup.
Try Different Scenarios
Use the calculator below to see how costs change with different usage patterns
Gemini 3.1 Pro (Google)
GPT-5.4 mini (OpenAI)
Start using Gemini 3.1 Pro today
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Sign Up for OpenAI →Frequently Asked Questions
Which is cheaper, Gemini 3.1 Pro or GPT-5.4 mini?▼
What is the context window difference between Gemini 3.1 Pro and GPT-5.4 mini?▼
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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