💰 Unit Cost Winner OpenAI o4-mini $1.10/1M in · $4.40/1M out
⚡ Latency & Speed Winner OpenAI o4-mini 680ms TTFT · 125 tok/s
💻 Coding (SWE) Winner OpenAI o4-mini 68.1% SWE-bench · 94% HumanEval
🧠 STEM Reasoning Winner OpenAI o4-mini 81.4% GPQA · 97.8% MATH-500

Which model is better: o1 or OpenAI o4-mini?

OpenAI o4-mini is 92.7% cheaper for input tokens ($1.10 vs. $15.00 per 1M tokens) and $4.40 vs. $60.00 for output tokens (13.6x cost advantage). In terms of operational performance, OpenAI o4-mini delivers lower response latency at 680ms TTFT (1920ms faster than o1) with 125 vs. 48 tok/s throughput. OpenAI o4-mini leads coding evaluations at 68.1% SWE-bench Verified vs. 48.9% for o1 and 81.4% 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 o1 OpenAI o4-mini Net Delta / Advantage
Input Cost / 1M Tokens $15.00 $1.10 OpenAI o4-mini is 13.6x cheaper
Output Cost / 1M Tokens $60.00 $4.40 OpenAI o4-mini (92.7% lower)
Prompt Cache Read / 1M $7.500 $0.275 OpenAI o4-mini is 27.3x cheaper
Prompt Cache Write / 1M $15.00 $1.10 OpenAI o4-mini is 13.6x cheaper
Context Window (Max Output) 205k (100k out) 205k (100k out) Equal Context Window
Avg TTFT Response Latency 2600ms 680ms OpenAI o4-mini (1920ms faster)
Generation Speed (Throughput) 48 tok/s 125 tok/s OpenAI o4-mini (+77 tok/s faster)
SWE-bench Verified (Coding) 48.9% 68.1% OpenAI o4-mini (+19.2% lead)
GPQA Diamond (PhD STEM) 78% 81.4% OpenAI o4-mini (+3.4% lead)
MMLU-Pro (Multi-Discipline) 83.3% 85.6% OpenAI o4-mini (+2.3% lead)
MATH-500 (Quantitative Logic) 96.4% 97.8% OpenAI o4-mini (+1.4% lead)
Inference Value Score (IVR) 37.2 / 100 63.7 / 100 OpenAI o4-mini (+26.5 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

o1 $581.25

$15.00/1M in · $60.00/1M out

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

OpenAI o4-mini $39.19

$1.10/1M in · $4.40/1M out

Prompt Cache: $0.275/1M read · $1.10/1M write

Projected Monthly Cost Reduction
$542.06 / mo
(93.3% lower cost with OpenAI o4-mini)
Open Full Matchup Analysis →
A

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).

B

When to Choose OpenAI o4-mini

Choose OpenAI o4-mini (OpenAI) for automated PR review, high-volume agentic coding loops, SQL/data analysis, and visual document reasoning — offering a 204k context window, 680ms TTFT, and $1.10/1M input pricing ($0.275/1M cached reads).

Related Questions & Decision Factors

Which is cheaper: o1 or OpenAI o4-mini?

OpenAI o4-mini is cheaper at $1.10/1M input tokens ($4.40/1M output) compared to $15.00/1M input ($60.00/1M output) for o1.

Which model has lower TTFT latency and higher tok/s throughput: o1 or OpenAI o4-mini?

OpenAI o4-mini delivers faster first-token latency at 680ms TTFT (125 tok/s throughput) compared to 2600ms TTFT (48 tok/s) for o1.

How do o1 and OpenAI o4-mini compare on SWE-bench coding and GPQA Diamond reasoning?

On SWE-bench Verified software engineering, o1 scores 48.9% versus 68.1% for OpenAI o4-mini. On GPQA Diamond graduate-level STEM reasoning, o1 scores 78% versus 81.4% for OpenAI o4-mini (and 96.4% vs 97.8% on MATH-500).

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).

When should you choose OpenAI o4-mini?

Choose OpenAI o4-mini (OpenAI) for automated PR review, high-volume agentic coding loops, SQL/data analysis, and visual document reasoning — offering a 204k context window, 680ms TTFT, and $1.10/1M input pricing ($0.275/1M cached reads).

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