Gemini 2.5 Flash vs GPT-5.4
GPT-5.4 is the better pick for high‑assurance, strategic, and math-heavy workloads — it wins 5 benchmarks including safety, faithfulness, and strategic analysis. Gemini 2.5 Flash is the pragmatic choice when cost and broader multimodal input (audio/video) matter — it wins tool calling and costs about one‑sixth as much per token.
Gemini 2.5 Flash
Benchmark Scores
External Benchmarks
Pricing
Input
$0.300/MTok
Output
$2.50/MTok
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GPT-5.4
Benchmark Scores
External Benchmarks
Pricing
Input
$2.50/MTok
Output
$15.00/MTok
modelpicker.net
Benchmark Analysis
Across our 12-test suite (internal 1–5 scale) the models split as follows: GPT-5.4 wins 5 tests (structured_output 5 vs 4, strategic_analysis 5 vs 3, faithfulness 5 vs 4, safety_calibration 5 vs 4, agentic_planning 5 vs 4), Gemini 2.5 Flash wins 1 test (tool_calling 5 vs 4), and they tie on 6 tests (constrained_rewriting 4/4, creative_problem_solving 4/4, classification 3/3, long_context 5/5, persona_consistency 5/5, multilingual 5/5). What the scores mean in practice: - Safety and faithfulness: GPT-5.4’s safety_calibration 5 vs Gemini’s 4 (GPT ranks tied for 1st on safety; Gemini ranks 6th) indicates GPT-5.4 is more likely to refuse harmful prompts and stick to sources in our testing. - Strategic analysis and agentic planning: GPT-5.4 scores 5 vs 3 (strategic) and 5 vs 4 (agentic), and ranks tied for 1st on strategic analysis and agentic planning — useful for nuanced tradeoffs and multi-step goal decomposition. - Structured output: GPT-5.4 scored 5 vs Gemini’s 4 and ranks tied for 1st on structured_output; expect fewer schema/format errors with GPT-5.4 in our tests. - Tool calling: Gemini 2.5 Flash wins (5 vs 4) and is tied for 1st on tool_calling in our rankings, so it picked functions and arguments more accurately in our tasks. - Long context, persona consistency, multilingual, creative problem solving, constrained rewriting and classification were ties; both models performed equally (e.g., long_context 5/5 tied for 1st). External benchmarks (supplementary): GPT-5.4 scores 76.9% on SWE-bench Verified and 95.3% on AIME 2025 (Epoch AI), placing it rank 2/12 and rank 3/23 respectively on those external tests — useful additional evidence for coding/math strength. Note: internal 1–5 scores and external %-scores are different systems and are not averaged.
Pricing Analysis
Per‑million token prices from the payload: Gemini 2.5 Flash input $0.30 / mTok and output $2.50 / mTok; GPT-5.4 input $2.50 / mTok and output $15.00 / mTok. Using a simple 50/50 input/output split: at 1M tokens/month Gemini costs $1.40 vs GPT-5.4 $8.75; at 10M: Gemini $14 vs GPT-5.4 $87.50; at 100M: Gemini $140 vs GPT-5.4 $875. If your workload is output-heavy (all output tokens), costs scale to $2.50 vs $15.00 per 1M tokens. Startups, consumer apps, and high-volume pipelines will feel the gap: Gemini reduces token spend by ~83% (price ratio 0.1667) versus GPT-5.4, while GPT-5.4 trades that cost for stronger safety, faithfulness, and strategic capabilities.
Real-World Cost Comparison
Bottom Line
Choose Gemini 2.5 Flash if: you need multimodal ingestion including audio and video (payload lists text+image+file+audio+video->text), you operate at high token volumes and must minimize cost (Gemini input $0.30/m, output $2.50/m — ~6× cheaper), or your workflows prioritize tool calling accuracy. Choose GPT-5.4 if: you prioritize safety, faithfulness, strategic analysis, or strict structured output (GPT wins those benchmarks and ranks tied for 1st in several), or you rely on external coding/math benchmarks (GPT-5.4: SWE-bench 76.9% and AIME 95.3% per Epoch AI).
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.