Gemini 2.5 Pro vs GPT-5.4 Mini
GPT-5.4 Mini wins more benchmarks in our testing (3 outright wins vs. 2 for Gemini 2.5 Pro) and costs less — $4.50/M output tokens vs. $10.00/M — making it the stronger default for most production workloads. Gemini 2.5 Pro pulls ahead on creative problem solving and tool calling, and its 1M-token context window dwarfs GPT-5.4 Mini's 400K, making it the better fit for document-heavy pipelines. For the majority of analytical and writing tasks, GPT-5.4 Mini delivers equal or better results at less than half the output cost.
Gemini 2.5 Pro
Benchmark Scores
External Benchmarks
Pricing
Input
$1.25/MTok
Output
$10.00/MTok
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GPT-5.4 Mini
Benchmark Scores
External Benchmarks
Pricing
Input
$0.750/MTok
Output
$4.50/MTok
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Benchmark Analysis
Across our 12-test internal benchmark suite, Gemini 2.5 Pro wins 2 tests outright, GPT-5.4 Mini wins 3, and the two models tie on 7.
Where Gemini 2.5 Pro wins:
- Creative problem solving: 5/5 vs. 4/5. Gemini 2.5 Pro ties for 1st among 8 models; GPT-5.4 Mini ranks 9th of 54. In practice, this gap matters for brainstorming, ideation, and open-ended research tasks where originality and feasibility both count.
- Tool calling: 5/5 vs. 4/5. Gemini 2.5 Pro ties for 1st among 17 models; GPT-5.4 Mini ranks 18th of 54. Tool calling covers function selection, argument accuracy, and sequencing — the backbone of agentic and API-integration workflows. A one-point gap here is a meaningful reliability difference for developers building multi-step agents.
Where GPT-5.4 Mini wins:
- Strategic analysis: 5/5 vs. 4/5. GPT-5.4 Mini ties for 1st among 26 models; Gemini 2.5 Pro ranks 27th of 54. This test covers nuanced tradeoff reasoning with real numbers — the kind of analysis needed in business planning, financial modeling, and decision frameworks.
- Constrained rewriting: 4/5 vs. 3/5. GPT-5.4 Mini ranks 6th of 53; Gemini 2.5 Pro ranks 31st of 53. This tests compression within hard character limits — important for ad copy, headlines, UI microcopy, and any workflow with strict output constraints.
- Safety calibration: 2/5 vs. 1/5. GPT-5.4 Mini ranks 12th of 55; Gemini 2.5 Pro ranks 32nd of 55. Neither model excels here — both score below the field median of 2 — but Gemini 2.5 Pro's 1/5 places it in the bottom tier of the 55 models tested. This test measures whether a model correctly refuses harmful requests while permitting legitimate ones; a low score can mean either over-refusal or under-refusal.
Ties (7 of 12 tests): Both models score identically on structured output (5/5), faithfulness (5/5), classification (4/5), long context (5/5), persona consistency (5/5), agentic planning (4/5), and multilingual (5/5). These are shared strengths — neither model is a differentiator here.
External benchmarks (Epoch AI): Gemini 2.5 Pro scores 57.6% on SWE-bench Verified (real GitHub issue resolution), ranking 10th of 12 models with this score in our dataset — below the field median of 70.8%. It also scores 84.2% on AIME 2025 (math olympiad), ranking 11th of 23 models — just above the field median of 83.9%. GPT-5.4 Mini does not have external benchmark scores in our dataset. These Epoch AI figures suggest Gemini 2.5 Pro sits mid-pack on autonomous software engineering tasks despite its strong internal tool calling score.
Pricing Analysis
Gemini 2.5 Pro costs $1.25/M input tokens and $10.00/M output tokens. GPT-5.4 Mini costs $0.75/M input and $4.50/M output — a 2.2x gap on output pricing that compounds fast at scale.
At 1M output tokens/month: Gemini 2.5 Pro costs ~$10.00; GPT-5.4 Mini costs ~$4.50 — a $5.50 difference. At 10M output tokens/month: $100 vs. $45 — you save $55 with GPT-5.4 Mini. At 100M output tokens/month: $1,000 vs. $450 — the $550/month gap is material for any production system.
The input cost gap is smaller ($1.25 vs. $0.75/M), but still meaningful for read-heavy workloads with large prompts. Teams running high-throughput pipelines — classification, summarization, routing — should weigh the output cost difference carefully. Gemini 2.5 Pro's premium is defensible if your workflow depends on its 1M-token context window, superior tool calling (5/5 vs. 4/5), or creative problem solving (5/5 vs. 4/5). Otherwise, GPT-5.4 Mini gives more benchmark wins per dollar.
Real-World Cost Comparison
Bottom Line
Choose Gemini 2.5 Pro if:
- Your workflow requires a context window larger than 400K tokens — its 1M-token window is 2.5x GPT-5.4 Mini's limit, enabling full-book analysis, large codebases, or lengthy document ingestion in a single call.
- You're building agentic systems where tool calling reliability is critical; its 5/5 score (tied for 1st among 17 models) outperforms GPT-5.4 Mini's 4/5.
- Your tasks demand creative problem solving — product ideation, research exploration, non-obvious solutions (5/5 vs. 4/5).
- You accept the 2.2x output cost premium in exchange for those specific capabilities.
- You need audio or video input handling — Gemini 2.5 Pro supports text+image+file+audio+video inputs; GPT-5.4 Mini handles text+image+file only.
Choose GPT-5.4 Mini if:
- Cost efficiency matters: at 100M output tokens/month, you save ~$550 vs. Gemini 2.5 Pro.
- Your tasks center on strategic analysis or business reasoning (5/5 vs. 4/5, tied for 1st among 26 models).
- You work heavily with constrained writing — ad copy, headlines, character-limited outputs (4/5 vs. 3/5, ranking 6th vs. 31st of 53).
- Safety calibration is important to your deployment — GPT-5.4 Mini scores 2/5 vs. Gemini 2.5 Pro's 1/5.
- You need a higher max output token limit per call: GPT-5.4 Mini supports 128K output tokens vs. Gemini 2.5 Pro's 65,536.
- Your context needs fit within 400K tokens and you'd rather not pay for headroom you won't use.
How We Test
We test every model against our 12-benchmark suite covering tool calling, agentic planning, creative problem solving, safety calibration, and more. Each test is scored 1–5 by an LLM judge. Read our full methodology.