o3-mini
OpenAI • Reasoning
Context Window200K
Knowledge CutoffUnknown
o3-mini vs GPT-4o
o3-mini for reasoning-optimized tasks at lower cost, GPT-4o for general-purpose and when reasoning is not the bottleneck.
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GPT-4o
OpenAI • Multimodal
Context Window128K
Knowledge CutoffUnknown
Editorial Verdict
o3-mini is OpenAI's reasoning specialist; GPT-4o is OpenAI's versatile general-purpose flagship.
o3-mini and GPT-4o are both OpenAI models but with different specializations. o3-mini is optimized for hard reasoning tasks; GPT-4o is a general-purpose multimodal model. For math, science, and hard reasoning, o3-mini outperforms GPT-4o. For general-purpose tasks, coding, and multimodal applications, GPT-4o is the more versatile choice.
o3-mini
- Significantly outperforms GPT-4o on math, science, and hard reasoning benchmarks
- Configurable reasoning depth for task-appropriate cost and latency
- Lower cost than GPT-4o for comparable general capability
Best for: Hard reasoning tasks, math, and competitive programming
GPT-4o
- Multimodal capabilities across text, images, audio, and video in one model
- Versatile general-purpose model for diverse production applications
- Mature API ecosystem with function calling, JSON mode, and enterprise support
Best for: General-purpose production applications and multimodal workflows
Pricing sourced from OpenRouter — updates as their catalog changes.
Pricing Comparison
All values pull from OpenRouter and update as their catalog changes.
| Metric | o3-mini | GPT-4o | Winner |
|---|---|---|---|
| Input (per 1M tokens) | Custom | Custom | N/A |
| Output (per 1M tokens) | Custom | Custom | N/A |
| Request fee | N/A | N/A | N/A |
| Image fee | N/A | N/A | N/A |
Cost Estimator
Estimate monthly billing using real OpenRouter prices.
o3-mini Total
VariableGPT-4o Total
VariableSavings unavailable (pricing not published for one model).
Capability Signals
Scores are directional estimates from model metadata — not official benchmark results.
o3-miniMMLU Signal (Reasoning)GPT-4o
76%
69%
o3-miniHumanEval Signal (Coding)GPT-4o
69%
69%
o3-miniAgentic Tooling SignalGPT-4o
69%
69%
o3-miniMultimodal SignalGPT-4o
69%
69%
Capabilities Matrix
Feature highlights for architecture and production fit.
o3-mini Strengths
- Large context window for long documents and codebases.
- Balanced general-purpose profile for chat, extraction, and automation tasks.
GPT-4o Strengths
- Balanced general-purpose profile for chat, extraction, and automation tasks.
API Implementation
Quick start snippets for each provider style.
OpenAI (Python)
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
model="openai/o3-mini",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)OpenAI (Python)
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
model="openai/gpt-4o",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Choose o3-mini when...
- You process large documents or code repositories in a single prompt.
- You want a balanced default for mixed chat and workflow automation workloads.
- You can measure quality with your own benchmark and prompt set.
Choose GPT-4o when...
- You want a balanced default for mixed chat and workflow automation workloads.
- You can measure quality with your own benchmark and prompt set.
Frequently Asked Questions
Practical checks before selecting a production model.
Which model is better for coding?
Coding preference depends on your stack and tool-calling needs. Compare the coding signal row, test with your repository tasks, and validate latency in your target region.
Which model is cheaper at scale?
Input and output token pricing can diverge by workload profile. Use the estimator with your monthly request count and token mix to get a realistic cost difference.
Are these official benchmark numbers?
Pricing is live from OpenRouter. Benchmark rows are ModelsAtlas metadata-based signals and should be treated as directional guidance, not official leaderboard scores.
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