💰 Unit Cost Winner Gemini 2.5 Flash $0.30/1M in · $2.50/1M out
⚡ Latency & Speed Winner Gemini 2.5 Flash 110ms TTFT · 180 tok/s
💻 Coding (SWE) Winner Gemini 2.5 Flash 58% SWE-bench · 91.5% HumanEval
🧠 STEM Reasoning Winner o1 78% GPQA · 96.4% MATH-500

Which model is better: Gemini 2.5 Flash or o1?

Gemini 2.5 Flash is 98.0% cheaper for input tokens ($0.30 vs. $15.00 per 1M tokens) and $2.50 vs. $60.00 for output tokens (50.0x cost advantage). In terms of operational performance, Gemini 2.5 Flash delivers lower response latency at 110ms TTFT (2490ms faster than o1) with 180 vs. 48 tok/s throughput. Gemini 2.5 Flash leads coding evaluations at 58.0% SWE-bench Verified vs. 48.9% for o1 and 70.5% vs. 78.0% on GPQA Diamond.
Verified daily via automated API latency tests and official documentation.

Verified Performance & Unit Economics Delta (12 Dimensions)

Daily synchronized metrics across token pricing, prompt cache economics, latency, throughput, and 5 capability benchmarks.

Metric / Dimension Gemini 2.5 Flash o1 Net Delta / Advantage
Input Cost / 1M Tokens $0.30 $15.00 Gemini 2.5 Flash is 50.0x cheaper
Output Cost / 1M Tokens $2.50 $60.00 Gemini 2.5 Flash (95.8% lower)
Prompt Cache Read / 1M $0.030 $7.500 Gemini 2.5 Flash is 250.0x cheaper
Prompt Cache Write / 1M $0.30 $15.00 Gemini 2.5 Flash is 50.0x cheaper
Context Window (Max Output) 1,049k (66k out) 205k (100k out) Gemini 2.5 Flash (5.1x larger context)
Avg TTFT Response Latency 110ms 2600ms Gemini 2.5 Flash (2490ms faster)
Generation Speed (Throughput) 180 tok/s 48 tok/s Gemini 2.5 Flash (+132 tok/s faster)
SWE-bench Verified (Coding) 58% 48.9% Gemini 2.5 Flash (+9.1% lead)
GPQA Diamond (PhD STEM) 70.5% 78% o1 (+7.5% lead)
MMLU-Pro (Multi-Discipline) 82% 83.3% o1 (+1.3% lead)
MATH-500 (Quantitative Logic) 89% 96.4% o1 (+7.4% lead)
Inference Value Score (IVR) 66.6 / 100 37.2 / 100 Gemini 2.5 Flash (+29.4 pts)

Interactive Monthly Token Economics & ROI Forecaster

Model production workloads across prompt, completion, and cached context tokens, or switch models on the fly.

25.0M Tokens
500K 50M 250M 500M+
5.0M Tokens
100K 25M 75M 150M+
50% Cached
0% (Uncached) 25% 50% (Typical Agent) 95% Max

Estimated Monthly Spend

Gemini 2.5 Flash $16.63

$0.30/1M in · $2.50/1M out

Prompt Cache: $0.030/1M read · $0.30/1M write

o1 $581.25

$15.00/1M in · $60.00/1M out

Prompt Cache: $7.500/1M read · $15.00/1M write

Projected Monthly Cost Reduction
$564.63 / mo
(97.1% lower cost with Gemini 2.5 Flash)
Open Full Matchup Analysis →
A

When to Choose Gemini 2.5 Flash

Choose Gemini 2.5 Flash (Google) for cost-efficient 1M-context summarization, multimodal extraction, and interactive assistants — offering a 1,048k context window, 110ms TTFT, and $0.30/1M input pricing ($0.030/1M cached reads).

B

When to Choose o1

Choose o1 (OpenAI) for legacy reasoning benchmarks, scientific literature review, and complex mathematical proofs — offering a 204k context window, 2600ms TTFT, and $15.00/1M input pricing ($7.500/1M cached reads).

Related Questions & Decision Factors

Which is cheaper: Gemini 2.5 Flash or o1?

Gemini 2.5 Flash is cheaper at $0.30/1M input tokens ($2.50/1M output) compared to $15.00/1M input ($60.00/1M output) for o1.

Which model has lower TTFT latency and higher tok/s throughput: Gemini 2.5 Flash or o1?

Gemini 2.5 Flash delivers faster first-token latency at 110ms TTFT (180 tok/s throughput) compared to 2600ms TTFT (48 tok/s) for o1.

How do Gemini 2.5 Flash and o1 compare on SWE-bench coding and GPQA Diamond reasoning?

On SWE-bench Verified software engineering, Gemini 2.5 Flash scores 58% versus 48.9% for o1. On GPQA Diamond graduate-level STEM reasoning, Gemini 2.5 Flash scores 70.5% versus 78% for o1 (and 89% vs 96.4% on MATH-500).

When should you choose Gemini 2.5 Flash?

Choose Gemini 2.5 Flash (Google) for cost-efficient 1M-context summarization, multimodal extraction, and interactive assistants — offering a 1,048k context window, 110ms TTFT, and $0.30/1M input pricing ($0.030/1M cached reads).

When should you choose o1?

Choose o1 (OpenAI) for legacy reasoning benchmarks, scientific literature review, and complex mathematical proofs — offering a 204k context window, 2600ms TTFT, and $15.00/1M input pricing ($7.500/1M cached reads).

Explore High-Priority Related Model Matchups