TLDR
Choose o1 if you need: Absolute best performance on general knowledge (MMLU) and graduate-level reasoning (GPQA), regardless of cost.
Choose o3-mini if you need: 14x lower cost, larger context window (200K vs 128K), superior AIME performance (87.3% vs 79.2%), flexible reasoning levels, and function calling support.
Budget: o3-mini ($1.10/$4.40 per million tokens) is 14x cheaper than o1 ($15/$60 per million tokens).
Performance: o3-mini outperforms o1 on mathematics (AIME) and coding. o1 has an edge in general knowledge (MMLU) and graduate-level reasoning (GPQA).
Recommendation: For most use cases, o3-mini offers better value. It delivers 85-90% of o3's capability (which surpasses o1) at a fraction of o1's cost.
Overview
OpenAI released o1 on December 5, 2024, as their flagship reasoning model designed to solve complex problems in math, science, and coding through chain-of-thought reasoning.
Just under two months later, on January 31, 2025, OpenAI released o3-mini as their most cost-efficient reasoning model. Surprisingly, o3-mini doesn't just undercut o1 on price. It actually outperforms o1 on several key benchmarks while costing 14x less.
This comparison reveals an interesting evolution: OpenAI's newer budget model represents a significant upgrade over their previous flagship, making o1 a harder sell for most use cases.
Basics: Model Specifications
| Feature | o1 | o3-mini |
|---|---|---|
| Release Date | December 5, 2024 | January 31, 2025 |
| Parameters | Undisclosed | Undisclosed |
| Architecture | Reasoning (chain-of-thought) | Reasoning (chain-of-thought) |
| Context Window | 128K tokens | 200K tokens ✓ |
| Max Output | 32K tokens | 100K tokens ✓ |
| Modalities | Text only | Text only |
| Reasoning Levels | Single level | Low, Medium, High ✓ |
| Function Calling | No | Yes ✓ |
| API Access | Tier 1+ ($5+ spend) | Tier 3+ ($100+ spend) |
o3-mini offers more flexibility with larger context windows, higher output limits, three reasoning levels, and function calling support.
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Pricing: Cost Comparison
| Model | Input (per 1M tokens) | Output (per 1M tokens) | Cost Difference |
|---|---|---|---|
| o1 | $15.00 | $60.00 | Baseline |
| o3-mini | $1.10 | $4.40 | 14x cheaper |
For a typical reasoning task using 50,000 input tokens and generating 10,000 output tokens:
- o1: $1.35 per request
- o3-mini: $0.099 per request
The cost difference is dramatic. o3-mini makes advanced reasoning accessible to developers who previously couldn't afford o1's premium pricing.
Performance: Benchmark Comparison
Mathematical Reasoning
| Benchmark | o1 | o3-mini (high) | Winner |
|---|---|---|---|
| AIME 2024 | 79.2% | 87.3% | o3-mini |
| AIME 2025 | 79.2% | 86.5% | o3-mini |
This is remarkable: o3-mini outperforms o1 on the American Invitational Mathematics Examination by over 7 percentage points. The budget model is objectively better at competition-level mathematics.
General Knowledge
| Benchmark | o1 | o3-mini | Winner |
|---|---|---|---|
| MMLU | 91.8% | Not disclosed | o1 (likely) |
| GPQA Diamond | 75.7% | Higher than DeepSeek R1 | o1 (likely) |
o1 maintains an advantage in general knowledge and graduate-level reasoning, though o3-mini's exact scores aren't publicly available.
Coding Performance
| Benchmark | o1 | o3-mini (high) | Winner |
|---|---|---|---|
| Codeforces Rating | 89th percentile | 2,029 Elo | o3-mini |
| SWE-Bench Verified | Not disclosed | 49.3% | o3-mini |
o3-mini achieves the highest Codeforces rating of any OpenAI model and sets a new standard on SWE-Bench, making it the best coding reasoning model from OpenAI.
o3-mini Reasoning Levels: Unique Advantage
o3-mini's three reasoning effort levels give you cost-performance control that o1 doesn't offer:
| Reasoning Level | Speed | Accuracy | Cost | Best For |
|---|---|---|---|---|
| Low | Fastest | Good | Lowest | Simple reasoning, drafts |
| Medium | Balanced | Better | Standard | General reasoning (free tier) |
| High | Slowest | Best | Highest | Complex problems, competitions |
You can optimize for speed when you need quick answers, or dial up reasoning effort for maximum accuracy on hard problems. o1 only offers a single (expensive) reasoning level.
Context & Output: o3-mini Wins
| Feature | o1 | o3-mini | Difference |
|---|---|---|---|
| Context Window | 128K tokens | 200K tokens | +56% larger |
| Max Output | 32K tokens | 100K tokens | +213% larger |
o3-mini's larger context window handles longer documents and more complex multi-turn conversations. The 100K max output is particularly valuable for generating long-form content or comprehensive code.
Function Calling: o3-mini Only
o3-mini is OpenAI's first reasoning model with official function calling support. This enables:
- Tool use: Call external APIs mid-reasoning
- Structured outputs: Generate JSON, XML, or other formats reliably
- Agentic workflows: Build AI agents that reason and act
- Multi-step automation: Chain reasoning with real-world actions
o1 doesn't support function calling, limiting its use in agentic and tool-based workflows.
When to Use Each Model
Use o1 when you need:
- Absolute best general knowledge: Top MMLU and GPQA scores
- Simpler API access: Available at Tier 1 ($5+ spend) vs Tier 3 ($100+)
- Maximum reasoning on all tasks: Single high-effort reasoning level
- Established track record: Launched 2 months earlier with more real-world testing
Use o3-mini when you need:
- Cost efficiency: 14x cheaper for high-volume applications
- Superior math performance: Highest AIME scores (87.3% vs 79.2%)
- Best coding performance: Top Codeforces and SWE-Bench results
- Flexible reasoning: Three effort levels to optimize cost vs accuracy
- Larger context: 200K vs 128K token window
- Function calling: Build agentic workflows with tool use
- Long outputs: Up to 100K tokens vs 32K
For most developers, o3-mini is the clear choice. It delivers better mathematical and coding performance at 1/14th the cost, with more flexibility and features.
The o3 Context
o3-mini delivers 85-90% of the full o3 model's capability at just 11% of the cost. While o3 itself isn't widely available yet, early benchmarks suggest o3 significantly outperforms o1.
This means o3-mini effectively represents:
- Better than o1 on math and coding
- Cheaper than o1 by 14x
- More flexible than o1 with reasoning levels and function calling
- Based on a stronger foundation (o3) than o1
Unless you specifically need o1's general knowledge edge, o3-mini is objectively the better reasoning model.
API Access Requirements
| Model | Minimum Tier | Minimum Spend | Availability |
|---|---|---|---|
| o1 | Tier 1 | $5+ | ChatGPT Plus, Team, API |
| o3-mini | Tier 3 | $100+ | ChatGPT Plus, Team, API |
o3-mini requires higher API tier access ($100+ spend vs $5+), which may be a barrier for new developers. However, both models are available to ChatGPT Plus and Team subscribers.
Orchestrate Multiple Reasoning Models with Miniloop
The choice between o1 and o3-mini doesn't have to be binary. Different tasks within a workflow may benefit from different reasoning approaches.
With Miniloop, you can build AI workflows that dynamically select between o1 and o3-mini based on task requirements. Use o3-mini's high reasoning level for complex math, switch to low level for simple validation, or route general knowledge queries to o1.
Miniloop lets you:
- Mix different reasoning models in a single workflow
- Use o3-mini's reasoning levels strategically (low for speed, high for accuracy)
- Route math and coding to o3-mini, general knowledge to o1
- A/B test reasoning models to optimize performance
- Control costs by using o3-mini's cheaper tiers when possible
Stop overpaying for reasoning you don't need. Start building cost-optimized multi-model reasoning workflows with Miniloop.
Sources
- Introducing OpenAI o3 and o4-mini
- o3-mini Model Specs - Galaxy.ai
- OpenAI Pricing
- OpenAI o1 - Wikipedia
- o3-mini Performance Analysis
Related Reading
- OpenAI o1 vs DeepSeek R1: Complete Reasoning Model Comparison
- DeepSeek V3 vs DeepSeek R1: Standard vs Reasoning Model Comparison
- DeepSeek V3 vs GPT-4o: Open Source vs Closed Flagship Comparison
- Best Open Source LLMs: 25+ Models Compared for 2026
Related Resources
- Try Miniloop - Start automating your GTM busywork with Miniloop
- Integrations - Connect Miniloop to the tools you already use
- Get in touch - Start a low-pressure conversation with the Miniloop team
- Agentic Workflows - Workflows that combine AI reasoning with automated execution
Frequently Asked Questions
Should I use o1 or o3-mini?
For most use cases, o3-mini is the better choice. It's 14x cheaper ($1.10 vs $15 input), has a larger context window (200K vs 128K), outperforms o1 on AIME (87.3% vs 79.2%), and offers flexible reasoning levels. Use o1 only if you need the absolute best performance on general knowledge (MMLU: 91.8% vs undisclosed).
Is o3-mini better than o1?
o3-mini outperforms o1 on AIME mathematics (87.3% vs 79.2%) and offers a larger context window (200K vs 128K). o1 has a slight edge in general knowledge (MMLU: 91.8%) and graduate-level reasoning (GPQA: 75.7%). For most tasks, o3-mini offers better value.
How much cheaper is o3-mini than o1?
o3-mini costs $1.10 per million input tokens and $4.40 per million output tokens. o1 costs $15 per million input tokens and $60 per million output tokens. o3-mini is approximately 14x cheaper on input and 14x cheaper on output.
What are o3-mini reasoning levels?
o3-mini offers three reasoning effort levels: low (fastest, cheapest), medium (balanced, free tier default), and high (slowest, most accurate). This lets you optimize for speed vs accuracy based on task complexity.