OpenAI's O4-Mini: Tests, Features, O3 Comparison, and More

There were a few things I wanted to change. I sent this follow-up prompt:

1. Draw a more convincing dinosaur—that blob doesn't look at all like a dinosaur.

2. Give the user the option to start the game with the press of a key—don't start the game immediately. Make sure to still keep all instructions on the screen.

3. When there's game over, give the user the opportunity to try again.

The second result:

That dinosaur looks more like an old film camera to me, but the rest is great.

Multimodal reasoning with large input

Stanford recently released The 2025 AI Index Report, a 450-page (117,649 tokens) overview of the current state of the AI industry. I asked o4-mini-high to read it and predict 10 trends for 2026. It handled the task well and completed it in just 9 seconds.

Here are the trends it predicted for 2026:

1. Near‑Human Performance on New Reasoning Benchmarks
2. Ultra‑Low‑Cost Inference for Production LLMs
3. Synthetic‑Plus‑Real Data Training Pipelines
4. Widespread Adoption of AI‑Native Hardware
5. Multimodal Foundation Models Standardized
6. Responsible‑AI Certification and Auditing Regimes
7. AI‑Driven Scientific Discovery Accelerates Nobel‑Winning Work
8. Localized AI Governance Frameworks with Global Alignment
9. Democratization of AI Education and Workforce‑Readiness
10. AI Agents as Collaborative Knowledge Workers

Most of these made sense, though a few predictions lean toward optimism. Take number two—yes, inference costs are dropping, but “ultra-low” is a stretch, especially for larger models and less-optimized deployments.

Number four is similar: AI-native hardware is progressing, but widespread adoption depends on supply chains, software integration, and ecosystem support, which all take time.

As for number nine, we’re still quite far from anything that could be called full democratization of AI education, but to be fair, the model didn’t say “complete.”

O4-Mini Benchmarks

O4-mini was designed to be a fast, cost-efficient model, but it still performs well across a broad set of reasoning benchmarks. In some cases, it even surpasses larger models like o3.

Math and logic

These tests focus on structured reasoning, often requiring multiple steps to solve math problems correctly. The AIME benchmarks, for instance, are drawn from competitive high school math contests.

• AIME 2024: o4-mini (no tools) scores 93.4%, outperforming o3 (91.6%), o3-mini (87.3%), and o1 (74.3%).
• AIME 2025: o4-mini (no tools) scores 92.7%, again beating o3 (88.9%), o3-mini (86.5%), and o1 (79.2%)

Source: OpenAI

Coding

These benchmarks test the model’s ability to write, debug, and understand code. Codeforces is a competitive programming dataset, while SWE-Bench and Aider Polyglot simulate real-world engineering tasks.

• Codeforces: o4-mini (with terminal) scores an ELO of 2719, slightly above o3 (2706). o3-mini scores 2073, and o1 lags far behind at 1891.
• SWE-Bench Verified: o4-mini scores 68.1%, just behind o3 at 69.1%, and ahead of o1 (48.9%) and o3-mini (49.3%).
• Aider Polyglot (code editing): o4-mini-high scores 68.9% (whole) and 58.2% (diff). For comparison, o3-high scores 81.3% (whole) and 79.6% (diff).

Source: OpenAI

Multimodal reasoning

These benchmarks measure how well the model processes visual inputs like diagrams, charts, and figures. The tests combine image understanding with logical reasoning.

• MMMU: o4-mini scores 81.6%, close to o3 (82.9%) and better than o1 (77.6%).
• MathVista: o4-mini scores 84.3%, trailing o3 (86.8%) but significantly above o1 (71.8%).
• CharXiv (scientific figures): o4-mini reaches 72.0%, behind o3 (78.6%) but above o1 (55.1%).

Source: OpenAI

General QA and reasoning

These datasets test broad question-answering ability, including PhD-level science questions and open-ended reasoning across subjects. Tool use is often required to score well.

• GPQA Diamond: o4-mini scores 81.4%, behind o3 (83.3%) but above o1 (78.0%) and o3-mini (77.0%).
• Humanity’s Last Exam:
• o4-mini (no tools): 14.28%
• o4-mini (with Python + browsing): 17.70%
• For context, o3 (with tools) scored 24.90%, and Deep Research scored 26.60%.

Source: OpenAI

O4-Mini vs O3: Which One Should You Use?

O4-mini and o3 are based on the same underlying design philosophy: models that can reason with tools, think step-by-step, and work with text, code, and images. But they’re built for different trade-offs. In short:

• Pick o3 if precision is your top priority and cost is less of a concern.
• Pick o4-mini if speed, volume, or cost-efficiency matter more than squeezing out a few extra benchmark points.

O3 is stronger overall

O3 scores higher than o4-mini on benchmarks like CharXiv, MathVista, and Humanity’s Last Exam. It also shows better consistency on complex tasks that require longer reasoning chains or precise code generation. If you’re working on research problems, scientific workflows, or anything where accuracy matters more than speed or cost, o3 is likely the safer pick.

O4-mini is much cheaper and faster

The difference in price is significant: o4-mini costs $1.10 for input and $4.40 for output, compared to o3 at $10 and $40 respectively. That’s nearly a 10x difference across the board. You also get faster response times and higher throughput, which makes it a better fit for production environments, real-time tools, or anything running at scale.

Multimodality is not a deciding factor

Both models are multimodal and tool-capable. Both support Python, browsing, and vision. If you’re just looking for that feature set, either one will work.

How to Access o4-mini in ChatGPT and API

You can use o4-mini in both ChatGPT and the OpenAI API, depending on your plan.

In ChatGPT

If you’re using ChatGPT with a Plus, Pro, or Team subscription, o4-mini is already available. You’ll see it listed as “o4-mini” and “o4-mini-high” in the model selector.

These replace the older o3-mini and o1 options. Free users can also try o4-mini in a limited form by selecting “Think” mode in the composer before sending a prompt. Note that:

• o4-mini is the default fast version.
• o4-mini-high runs with increased inference effort for better quality at the cost of speed.

In the API

o4-mini is also available through the OpenAI API under the Chat Completions and Responses endpoints. Both versions (standard and high) support tool use, including browsing, Python, and image inputs, depending on how you’ve configured your toolset.

The model ID for the current snapshot is o4-mini-2025-04-16 . Pricing is set at $1.10 per million input tokens and $4.40 per million output tokens, with caching and batching supported. If you’re working with the Responses API, you also get reasoning token visibility and more control over tool execution within multi-step chains.

Conclusion

o4-mini offers solid performance across math, code, and multimodal tasks, while cutting inference costs by an order of magnitude compared to o3. The benchmarks show that it holds up well, even in tasks where models typically struggle, like multimodal reasoning or coding.

It’s also the first time a mini model from OpenAI includes full tool support and native multimodality. That alone puts it ahead of o3-mini and o1, both of which it replaces in most ChatGPT tiers. And with the addition of o4-mini-high, users get a bit more control over the balance between speed and output quality.

Whether o4-mini is the right model depends on the task. It won’t beat o3 in terms of raw capability, but for anything that needs to run quickly, scale easily, or stay within a tighter budget, it’s a strong option—and in many cases, good enough.