o1
OpenAI • Reasoning
Context Window200K
Knowledge CutoffUnknown
o1 vs o3-mini
o1 for maximum reasoning depth on hard problems, o3-mini for cost-efficient reasoning in production pipelines.
Open model page
o3-mini
OpenAI • Reasoning
Context Window200K
Knowledge CutoffUnknown
Editorial Verdict
o3-mini is the optimized, faster successor to o1; o1 retains an edge on the hardest reasoning tasks.
o1 and o3-mini are both OpenAI reasoning models, but o3-mini is the optimized, faster successor with configurable reasoning depth. o3-mini matches or exceeds o1 on most benchmarks while being faster and cheaper. o1 retains advantages in extremely hard reasoning tasks that benefit from its longer internal reasoning chain. For most use cases, o3-mini is the better choice.
o1
- Longer internal reasoning chain for extremely complex multi-step problems
- Battle-tested production reasoning model with established reliability
- Better at tasks requiring very deep, deliberate reasoning chains
Best for: Extremely hard reasoning tasks requiring maximum deliberation
o3-mini
- Matches or exceeds o1 on most benchmarks at significantly lower cost
- Configurable reasoning effort (low/medium/high) for task-appropriate speed
- Significantly faster inference than o1 — better for latency-sensitive reasoning applications
Best for: Most reasoning tasks — faster, cheaper, and better value than o1
Pricing sourced from OpenRouter — updates as their catalog changes.
Pricing Comparison
All values pull from OpenRouter and update as their catalog changes.
| Metric | o1 | o3-mini | 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.
o1 Total
Variableo3-mini Total
VariableSavings unavailable (pricing not published for one model).
Capability Signals
Scores are directional estimates from model metadata — not official benchmark results.
o1MMLU Signal (Reasoning)o3-mini
76%
76%
o1HumanEval Signal (Coding)o3-mini
69%
69%
o1Agentic Tooling Signalo3-mini
69%
69%
o1Multimodal Signalo3-mini
69%
69%
Capabilities Matrix
Feature highlights for architecture and production fit.
o1 Strengths
- Large context window for long documents and codebases.
- Balanced general-purpose profile for chat, extraction, and automation tasks.
o3-mini Strengths
- Large context window for long documents and codebases.
- 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/o1",
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/o3-mini",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Choose o1 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 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.
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.
Related Comparisons
Explore adjacent model matchups.