💰 Unit Cost Winner Gemini 2.0 Flash $0.10/1M in · $0.40/1M out
⚡ Latency & Speed Winner Gemini 2.0 Flash 280ms TTFT · 185 tok/s
💻 Coding (SWE) Winner Gemini 2.0 Flash 51.8% SWE-bench · 90.7% HumanEval
🧠 STEM Reasoning Winner o1 78% GPQA · 96.4% MATH-500

Which model is better: Gemini 2.0 Flash or o1?

Gemini 2.0 Flash is 99.3% cheaper for input tokens ($0.10 vs. $15.00 per 1M tokens) and $0.40 vs. $60.00 for output tokens (150.0x cost advantage). In terms of operational performance, Gemini 2.0 Flash delivers lower response latency at 280ms TTFT (2320ms faster than o1) with 185 vs. 48 tok/s throughput. Gemini 2.0 Flash leads coding evaluations at 51.8% SWE-bench Verified vs. 48.9% for o1 and 63.6% 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 2.0 Flash o1 Net Delta / Advantage
Input Cost / 1M Tokens $0.10 $15.00 Gemini 2.0 Flash is 150.0x cheaper
Output Cost / 1M Tokens $0.40 $60.00 Gemini 2.0 Flash (99.3% lower)
Prompt Cache Read / 1M $0.025 $7.500 Gemini 2.0 Flash is 300.0x cheaper
Prompt Cache Write / 1M $0.10 $15.00 Gemini 2.0 Flash is 150.0x cheaper
Context Window (Max Output) 1,073k (8k out) 205k (100k out) Gemini 2.0 Flash (5.2x larger context)
Avg TTFT Response Latency 280ms 2600ms Gemini 2.0 Flash (2320ms faster)
Generation Speed (Throughput) 185 tok/s 48 tok/s Gemini 2.0 Flash (+137 tok/s faster)
SWE-bench Verified (Coding) 51.8% 48.9% Gemini 2.0 Flash (+2.9% lead)
GPQA Diamond (PhD STEM) 63.6% 78% o1 (+14.4% lead)
MMLU-Pro (Multi-Discipline) 78.2% 83.3% o1 (+5.1% lead)
MATH-500 (Quantitative Logic) 91.1% 96.4% o1 (+5.3% lead)
Inference Value Score (IVR) 77.7 / 100 37.2 / 100 Gemini 2.0 Flash (+40.5 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 2.0 Flash $3.56

$0.10/1M in · $0.40/1M out

Prompt Cache: $0.025/1M read · $0.10/1M 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
$577.69 / mo
(99.4% lower cost with Gemini 2.0 Flash)
Open Full Matchup Analysis →
A

When to Choose Gemini 2.0 Flash

Choose Gemini 2.0 Flash (Google DeepMind) for legacy high-throughput multimodal pipelines migrating to Gemini 3.8 Flash — offering a 1,073k context window, 280ms TTFT, and $0.10/1M input pricing ($0.025/1M 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 2.0 Flash or o1?

Gemini 2.0 Flash is cheaper at $0.10/1M input tokens ($0.40/1M 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 2.0 Flash or o1?

Gemini 2.0 Flash delivers faster first-token latency at 280ms TTFT (185 tok/s throughput) compared to 2600ms TTFT (48 tok/s) for o1.

How do Gemini 2.0 Flash and o1 compare on SWE-bench coding and GPQA Diamond reasoning?

On SWE-bench Verified software engineering, Gemini 2.0 Flash scores 51.8% versus 48.9% for o1. On GPQA Diamond graduate-level STEM reasoning, Gemini 2.0 Flash scores 63.6% versus 78% for o1 (and 91.1% vs 96.4% on MATH-500).

When should you choose Gemini 2.0 Flash?

Choose Gemini 2.0 Flash (Google DeepMind) for legacy high-throughput multimodal pipelines migrating to Gemini 3.8 Flash — offering a 1,073k context window, 280ms TTFT, and $0.10/1M input pricing ($0.025/1M 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