Gemini 3.1 Pro vs GPT-5.4: Google's flagship against OpenAI's workhorse
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.
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.
By TechCompare · Updated
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: $2.00/M input (up to 200K), $4.00/M input (above 200K), $12.00/M output (up to 200K), $18.00/M output (above 200K). 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 wins the price rows at short context ($1.25/M input against $2.50, $5/M output against $15) and the context-window row dramatically (2,000,000 tokens against 256,000), plus native image/audio/video multimodality. GPT-5.4 wins the discount rows: 90% caching against 75% and 50% batch against Gemini's none, plus the most-mature tooling ecosystem (Assistants API, strict JSON schemas, broad SDK support). The cache row is interesting because at short context Gemini's $0.20/M cached still beats GPT-5.4's $0.25/M cached, but GPT-5.4 wins long-context workloads once Gemini's above-200K pricing kicks in. The call is workload-shape: huge-context multimodal goes Gemini, structured-batch goes GPT-5.4.
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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Frequently asked questions
Is Gemini 3.1 Pro or GPT-5.4 cheaper for short-context workloads?
When does GPT-5.4 beat Gemini 3.1 Pro despite higher base price?
What's the context window difference and why does it matter?
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