Gemini 3.1 Pro vs GPT-5.4: Google's flagship against OpenAI's workhorse
Google's 2M-token flagship vs OpenAI's cost-efficient workhorse.
Gemini 3.1 Pro is Google's answer to GPT-5.4: competitive pricing, a massive 2M token context window, and native multimodal input. GPT-5.4 counters with broader ecosystem support, mature tooling, and proven reliability at scale. Both are excellent general-purpose models, and the choice often comes down to ecosystem fit and specific capability needs rather than raw price.
Cost Comparison
Based on 100,000 input tokens (50% cached), 5,000 output tokens, and 100 requests.
Side-by-side specs
| Spec | Gemini 3.1 Pro | GPT-5.4 |
|---|---|---|
| Input cost (per M, short) | $1.25 (better on this spec) | $2.50 |
| Output cost (per M) | $5.00 (better on this spec) | $15.00 |
| Context window | 2,000,000 tokens (better on this spec) | 256,000 tokens |
| Caching discount | 75% | 90% (better on this spec) |
| Batch discount | N/A | 50% (better on this spec) |
| Native multimodality | Yes (image, audio, video) (better on this spec) | Image only |
| Ecosystem maturity | Growing rapidly | Most mature (better on this spec) |
How they differ
Gemini 3.1 Pro: $1.25/M input (up to 128K), $2.50/M input (above 128K), $5/M output (up to 128K), $10/M output (above 128K). Context caching: 75% discount. GPT-5.4: $2.50/M input, $15/M output, 90% caching discount, 50% batch discount. Gemini is cheaper for short-context use. GPT-5.4 is cheaper with aggressive caching. Gemini's 2M context window dwarfs GPT-5.4's 256K. For long-document analysis, RAG with massive context, and multimodal tasks, Gemini's architecture has native advantages. GPT-5.4's ecosystem (Assistants API, function calling, structured outputs) is more mature and better documented. For multilingual workloads, Gemini's tokenizer is more efficient for non-English text, potentially lowering effective cost further.
Verdict
Gemini 3.1 Pro for workloads that need massive context windows, native multimodality, or multilingual efficiency. GPT-5.4 for workloads that need mature tooling, structured outputs, batch processing, and the broadest ecosystem support. Both are excellent. Try your specific task on both and benchmark.
Which should you pick?
Choose Gemini 3.1 Pro
Long-document analysis needing 200K+ context. Multilingual applications. Native image/audio/video input. You're already in the Google Cloud ecosystem and want tight Vertex AI integration.
Choose GPT-5.4
Structured output with strict JSON schemas. Batch processing with 50% discount. Mature function calling and Assistants API. Broadest third-party tooling and community support.
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