OpenAI o4-mini and o3-pro in 2026: Complete Reasoning Model Guide — When to Use Which

TokenMix Research Lab · 2026-04-07

OpenAI o4-mini and o3-pro in 2026: Complete Reasoning Model Guide — When to Use Which

OpenAI o4-mini, o3, and o3-pro: Complete Guide to OpenAI Reasoning Models in 2026

OpenAI's reasoning model lineup in April 2026 spans four price points: [o3-mini](https://tokenmix.ai/blog/openai-o3-pricing) at $1.10/$4.40, o3 at $2.00/$8.00, o3-pro at $20.00/$80.00, and o4-mini at $0.55/$2.20. Each model uses chain-of-thought reasoning to solve problems that standard language models cannot handle reliably -- complex math, multi-step coding, scientific analysis, and adversarial logic. This guide covers exact pricing, when to use which model, how they compare to [DeepSeek R1](https://tokenmix.ai/blog/deepseek-r1-pricing), and what open-source thinking model alternatives exist. All data verified by [TokenMix.ai](https://tokenmix.ai) as of April 2026.

Table of Contents

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Quick OpenAI Reasoning Model Pricing Overview

All prices per 1M tokens, OpenAI API direct, April 2026:

| Model | Input | Cached Input | Output | Context | Reasoning Depth | Best For | | --- | --- | --- | --- | --- | --- | --- | | **o4-mini** | $0.55 | $0.14 | $2.20 | 200K | Medium | Budget reasoning, high volume | | **o3-mini** | $1.10 | $0.28 | $4.40 | 200K | Medium | Proven reasoning workhorse | | **o3** | $2.00 | $0.50 | $8.00 | 200K | High | Complex multi-step problems | | **o3-pro** | $20.00 | $5.00 | $80.00 | 200K | Maximum | Hardest problems, research |

**The headline:** o4-mini at $0.55/$2.20 delivers comparable reasoning quality to o3-mini at half the price. It is the default choice for most reasoning workloads in 2026. o3-pro at $20/$80 is reserved for problems where cost is irrelevant and accuracy is everything.

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What Are OpenAI Reasoning Models?

OpenAI reasoning models (the "o-series") differ from standard GPT models in one fundamental way: they think before answering. When you send a prompt to o3 or o4-mini, the model generates an internal chain-of-thought -- a multi-step reasoning process that is not visible in the final output but significantly improves accuracy on complex tasks.

This thinking process consumes additional tokens. A question that [GPT-5.4](https://tokenmix.ai/blog/gpt-5-api-pricing) answers in 200 output tokens might cost 800-2,000 output tokens through a reasoning model because of the hidden reasoning chain. This is why reasoning model output pricing is higher -- you are paying for the thinking tokens as well as the answer tokens.

**When reasoning models matter:** - Multi-step math and logic problems - Complex code generation requiring planning - Scientific analysis with multiple variables - Tasks where GPT-5.4 gives inconsistent or incorrect answers - Any problem that benefits from "thinking step by step"

**When reasoning models are overkill:** - Simple text generation and summarization - Straightforward Q&A - Content creation - Tasks where GPT-5.4 or GPT-5.4 Mini already performs well

TokenMix.ai data shows that reasoning models improve accuracy by 15-30% on hard problems compared to GPT-5.4, but provide zero benefit on simple tasks while costing 2-5x more. The key is routing: send hard problems to reasoning models, easy problems to standard models.

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o4-mini: The New Budget Reasoning Champion

o4-mini is OpenAI's newest reasoning model, released in early 2026. It halves the price of o3-mini while maintaining comparable performance on most reasoning benchmarks.

Pricing and Specs

| Spec | o4-mini | | --- | --- | | Input/M | $0.55 | | Cached Input/M | $0.14 | | Output/M | $2.20 | | Context Window | 200K tokens | | Max Output | 100K tokens | | Reasoning Mode | Always-on chain-of-thought | | Image Input | Supported | | Function Calling | Supported |

Benchmark Performance

| Benchmark | o4-mini | o3-mini | o3 | GPT-5.4 | | --- | --- | --- | --- | --- | | AIME 2025 | 92.7% | 87.3% | 96.7% | 78.0% | | GPQA Diamond | 77.8% | 76.0% | 82.3% | 76.2% | | SWE-bench Verified | 68.1% | 61.0% | 69.1% | 81.5% | | Codeforces Rating | 2070 | 1900 | 2230 | 1850 | | MATH-500 | 97.3% | 95.0% | 98.0% | 97.1% |

**o4-mini outperforms o3-mini on every benchmark while costing 50% less.** The 5.4-point improvement on AIME, 7.1-point improvement on SWE-bench, and 170-point Codeforces rating gain are not marginal. o4-mini has effectively made o3-mini obsolete for new deployments.

Against o3: o4-mini trails by 4 points on AIME, 4.5 points on GPQA, and 1 point on SWE-bench. That gap costs 3.6x more to close ($2.20 vs $8.00 output). For most teams, o4-mini is the better value.

**Best for:** Any reasoning workload where budget matters. Code generation, math problems, logic puzzles, structured analysis. Default choice for reasoning tasks in 2026.

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o3-mini: The Established Workhorse

o3-mini was the go-to budget reasoning model before o4-mini arrived. It remains relevant for teams with existing deployments and established performance baselines.

Pricing and Specs

| Spec | o3-mini | | --- | --- | | Input/M | $1.10 | | Cached Input/M | $0.28 | | Output/M | $4.40 | | Context Window | 200K tokens | | Reasoning Effort | Low / Medium / High (configurable) |

Why o3-mini Still Exists

The primary reason to use o3-mini over o4-mini is the configurable reasoning effort parameter. o3-mini allows you to set reasoning depth to low, medium, or high, which directly controls the number of thinking tokens generated. Low effort is faster and cheaper. High effort is slower and more accurate.

o4-mini does not expose this parameter in the same way. If you need fine-grained control over the reasoning-cost tradeoff per request, o3-mini gives you that lever.

For new projects with no existing o3-mini dependency, o4-mini is the better choice in almost every scenario.

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o3: Mid-Tier Reasoning Power

o3 is OpenAI's general-purpose reasoning model -- stronger than o4-mini, significantly cheaper than o3-pro.

Pricing and Specs

| Spec | o3 | | --- | --- | | Input/M | $2.00 | | Cached Input/M | $0.50 | | Output/M | $8.00 | | Context Window | 200K tokens | | Max Output | 100K tokens |

When o3 Justifies Its Premium

o3 costs 3.6x more than o4-mini on output. The benchmark gap is 4 points on AIME and 160 Codeforces rating points. Is that worth 3.6x the cost?

**Yes, in specific scenarios:** - Competitive programming problems where the difficulty threshold is above o4-mini's reliable ceiling - Multi-step planning tasks with 10+ sequential reasoning steps - Mathematical proofs and formal verification - Tasks where you have measured o4-mini's failure rate and need higher reliability

**No, for most production workloads.** If o4-mini solves your problem 90% of the time, paying 3.6x more for o3 to get to 94% rarely makes economic sense. The exception is when errors are very expensive -- medical analysis, legal reasoning, financial modeling.

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o3-pro: Maximum Reasoning Depth

o3-pro is OpenAI's most powerful reasoning model. At $20/$80, it is by far the most expensive model in OpenAI's lineup -- and arguably the most expensive API model from any provider.

Pricing and Specs

| Spec | o3-pro | | --- | --- | | Input/M | $20.00 | | Cached Input/M | $5.00 | | Output/M | $80.00 | | Context Window | 200K tokens |

Benchmark Performance

| Benchmark | o3-pro | o3 | o4-mini | Gap (pro vs o4-mini) | | --- | --- | --- | --- | --- | | AIME 2025 | 98.0% | 96.7% | 92.7% | +5.3% | | GPQA Diamond | 86.0% | 82.3% | 77.8% | +8.2% | | SWE-bench | 73.5% | 69.1% | 68.1% | +5.4% | | Codeforces | 2550 | 2230 | 2070 | +480 |

o3-pro is the only model that scores 98% on AIME 2025 and 86% on GPQA Diamond. For the absolute hardest problems -- PhD-level science, competitive mathematics, complex formal reasoning -- there is no substitute.

The cost math is stark: o3-pro output costs 36x more than o4-mini ($80 vs $2.20). That premium buys 5-8 percentage points on hard benchmarks. Only justified when solving the hardest 5% of problems that no other model can handle.

**Best for:** Research labs, competitive programming, PhD-level analysis, formal verification, and any scenario where getting the right answer on a hard problem is worth $80/M output tokens.

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Full Comparison: All OpenAI Reasoning Models

| Dimension | o4-mini | o3-mini | o3 | o3-pro | | --- | --- | --- | --- | --- | | Input/M | $0.55 | $1.10 | $2.00 | $20.00 | | Output/M | $2.20 | $4.40 | $8.00 | $80.00 | | Cached Input/M | $0.14 | $0.28 | $0.50 | $5.00 | | Context | 200K | 200K | 200K | 200K | | AIME 2025 | 92.7% | 87.3% | 96.7% | 98.0% | | GPQA Diamond | 77.8% | 76.0% | 82.3% | 86.0% | | SWE-bench | 68.1% | 61.0% | 69.1% | 73.5% | | Codeforces | 2070 | 1900 | 2230 | 2550 | | MATH-500 | 97.3% | 95.0% | 98.0% | 98.5% | | Image Input | Yes | Yes | Yes | Yes | | Reasoning Control | Fixed | Low/Med/High | Fixed | Extended | | Latency | ~5-15s | ~5-20s | ~10-30s | ~30-120s |

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OpenAI Reasoning Models vs DeepSeek R1

DeepSeek R1 is the primary competitor to OpenAI's reasoning models, and it costs significantly less.

| Spec | o4-mini | o3 | DeepSeek R1 | | --- | --- | --- | --- | | Input/M | $0.55 | $2.00 | $0.55 | | Output/M | $2.20 | $8.00 | $2.19 | | Cached Input/M | $0.14 | $0.50 | $0.14 | | Context | 200K | 200K | 128K | | AIME 2025 | 92.7% | 96.7% | 79.8% | | GPQA Diamond | 77.8% | 82.3% | 71.5% | | SWE-bench | 68.1% | 69.1% | 66.0% | | Codeforces | 2070 | 2230 | 1800 | | Open-source | No | No | Yes (weights available) |

**o4-mini and DeepSeek R1 are priced almost identically** ($0.55/$2.20 vs $0.55/$2.19). But o4-mini outperforms R1 by 12.9 points on AIME, 6.3 points on GPQA, and 270 Codeforces rating points. At the same price, o4-mini is the stronger reasoning model.

DeepSeek R1's advantage is availability: the weights are open-source, so you can self-host and eliminate per-token costs entirely. For teams with GPU infrastructure, R1 at self-hosted pricing (just compute cost) undercuts even o4-mini by a large margin. R1 also has active distilled variants (R1-7B, R1-14B, R1-32B, R1-70B) that run on consumer hardware.

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Open-Source Thinking Model Alternatives

For teams that cannot or prefer not to use OpenAI's API, several open-source reasoning models provide competitive alternatives.

| Model | Parameters | AIME 2025 | MATH-500 | Self-Host Cost (est.) | API Price | | --- | --- | --- | --- | --- | --- | | **DeepSeek R1** | 671B (MoE) | 79.8% | 95.0% | ~$0.10/M output | $0.55/$2.19 | | **DeepSeek R1-70B (distilled)** | 70B | 70.0% | 90.0% | ~$0.05/M output | ~$0.40/$0.80 | | **Qwen QwQ-32B** | 32B | 68.0% | 88.0% | ~$0.03/M output | ~$0.20/$0.60 | | **DeepSeek R1-32B (distilled)** | 32B | 65.0% | 87.0% | ~$0.03/M output | ~$0.15/$0.50 | | **DeepSeek R1-14B (distilled)** | 14B | 55.0% | 82.0% | ~$0.01/M output | ~$0.08/$0.30 |

Qwen QwQ-32B

Alibaba's QwQ-32B is a purpose-built reasoning model that punches well above its parameter count. At 32B parameters, it scores 68% on AIME 2025 -- competitive with DeepSeek R1-70B's distilled variant despite being less than half the size. Self-hosting cost is minimal, and API access through Alibaba Cloud is among the cheapest reasoning model options.

DeepSeek R1 Distilled Variants

DeepSeek released distilled versions of R1 at 7B, 14B, 32B, and 70B sizes. These are trained to mimic R1's reasoning behavior at smaller scales. The 70B variant retains about 88% of the full R1's reasoning ability. The 14B variant is practical for edge deployment and costs almost nothing to run.

**The open-source reasoning model ecosystem has matured dramatically.** For teams with self-hosting capability, these models offer reasoning performance at 10-50x lower cost than OpenAI's API pricing. TokenMix.ai provides unified API access to all these models, making it easy to benchmark open-source alternatives against OpenAI's o-series.

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Cost Breakdown: Real-World Reasoning Workloads

Reasoning models consume significantly more output tokens than standard models due to the thinking process. These calculations account for typical thinking token overhead.

Scenario 1: Code Review Pipeline (10K reviews/month, avg 2K input + 3K output tokens including thinking)

| Model | Input Cost | Output Cost | Total/Month | | --- | --- | --- | --- | | o4-mini | $11.00 | $66.00 | **$77.00** | | o3-mini | $22.00 | $132.00 | **$154.00** | | o3 | $40.00 | $240.00 | **$280.00** | | o3-pro | $400.00 | $2,400.00 | **$2,800.00** | | DeepSeek R1 | $11.00 | $65.70 | **$76.70** |

o4-mini and DeepSeek R1 are virtually identical in cost. o3-pro costs 36x more than o4-mini for the same workload.

Scenario 2: Math Tutoring Platform (100K questions/month, avg 500 input + 2K output tokens)

| Model | Input Cost | Output Cost | Total/Month | | --- | --- | --- | --- | | o4-mini | $27.50 | $440.00 | **$467.50** | | o3 | $100.00 | $1,600.00 | **$1,700.00** | | o3-pro | $1,000.00 | $16,000.00 | **$17,000.00** | | DeepSeek R1 | $27.50 | $438.00 | **$465.50** |

Scenario 3: Research Lab (1K complex problems/month, avg 5K input + 10K output tokens)

| Model | Monthly Cost | AIME Score | Cost per AIME % Point | | --- | --- | --- | --- | | o4-mini | $24.75 | 92.7% | $0.27 | | o3 | $90.00 | 96.7% | $0.93 | | o3-pro | $900.00 | 98.0% | $9.18 |

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How to Choose the Right OpenAI Reasoning Model

| Your Situation | Recommended Model | Why | | --- | --- | --- | | General reasoning on a budget | **o4-mini** | Best price/performance ratio, half the cost of o3-mini | | Need maximum reasoning accuracy, cost irrelevant | **o3-pro** | 98% AIME, 86% GPQA, unmatched on hardest problems | | Complex reasoning, moderate budget | **o3** | 4-point AIME improvement over o4-mini at 3.6x cost | | Existing o3-mini deployment, need reasoning effort control | **o3-mini** | Configurable low/med/high reasoning depth | | Want open-source reasoning, can self-host | **DeepSeek R1** | Open weights, comparable API pricing to o4-mini | | Lightweight reasoning, edge deployment | **Qwen QwQ-32B** or R1 distilled | Small models, self-hostable, minimal cost | | Mixed workload: some reasoning, some simple | o4-mini + GPT-5.4 via TokenMix.ai routing | Route hard tasks to o4-mini, simple to GPT-5.4 |

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Conclusion

The OpenAI reasoning model lineup in April 2026 is straightforward to navigate. o4-mini is the default choice for 90% of reasoning workloads. At $0.55/$2.20, it outperforms o3-mini on every benchmark while costing half as much. o3 justifies its 3.6x premium only when o4-mini's accuracy ceiling is demonstrably insufficient for your specific task. o3-pro is a specialized tool for the hardest 5% of problems.

The competitive landscape has shifted since DeepSeek R1 and open-source alternatives matured. o4-mini and DeepSeek R1 are priced identically, but o4-mini leads significantly on benchmarks. The open-source option becomes compelling when you factor in self-hosting -- R1's distilled 32B and 70B variants deliver usable reasoning at a fraction of any API cost.

For production teams, the most efficient approach is a multi-model strategy: route reasoning tasks to o4-mini and standard tasks to GPT-5.4 or GPT-5.4 Mini. TokenMix.ai's unified API makes this routing trivial -- one endpoint, automatic model selection based on task complexity, and consolidated billing across all providers.

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FAQ

What is the difference between o4-mini and o3-mini?

o4-mini is OpenAI's newer budget reasoning model, released in 2026. It costs 50% less than o3-mini ($0.55/$2.20 vs $1.10/$4.40) while scoring higher on every major benchmark -- 92.7% vs 87.3% on AIME, 68.1% vs 61.0% on SWE-bench. o3-mini retains a configurable reasoning effort parameter that o4-mini lacks, but for most use cases o4-mini is the better choice.

How much does o3-pro cost per million tokens?

o3-pro costs $20.00 per million input tokens and $80.00 per million output tokens. Cached input is $5.00/M. It is OpenAI's most expensive model and is designed exclusively for the hardest reasoning problems -- competitive mathematics, PhD-level science, formal verification.

Is DeepSeek R1 better than OpenAI o4-mini?

At the same price point ($0.55 input, ~$2.20 output), o4-mini significantly outperforms DeepSeek R1 on benchmarks: 92.7% vs 79.8% on AIME, 77.8% vs 71.5% on GPQA Diamond. DeepSeek R1's advantage is that it is open-source -- you can self-host the model weights, eliminating per-token API costs entirely.

When should I use a reasoning model instead of GPT-5.4?

Use reasoning models for tasks where GPT-5.4 gives inconsistent or incorrect answers: complex math, multi-step coding problems, scientific analysis, and adversarial logic. TokenMix.ai testing shows reasoning models improve accuracy 15-30% on hard problems. For simple text generation, summarization, and straightforward Q&A, GPT-5.4 is cheaper and equally accurate.

What open-source thinking models can replace OpenAI o-series?

The strongest open-source alternatives are DeepSeek R1 (671B MoE, 79.8% AIME), its distilled variants (R1-70B, R1-32B, R1-14B), and Qwen QwQ-32B (68% AIME). These can be self-hosted at significantly lower cost than API pricing, though they do not match o4-mini's benchmark scores.

Can I use o4-mini with 200K context?

Yes. All OpenAI reasoning models -- o4-mini, o3-mini, o3, and o3-pro -- support a 200K token context window. This is larger than DeepSeek R1's 128K context. o4-mini also supports up to 100K output tokens, which is important for reasoning tasks that generate long thinking chains.

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*Author: TokenMix Research Lab | Last Updated: April 2026 | Data Source: [OpenAI Official Docs](https://platform.openai.com/docs), [TokenMix.ai](https://tokenmix.ai), [AIME Leaderboard](https://artofproblemsolving.com), [Chatbot Arena](https://chat.lmsys.org)*