o3-mini
OpenAI • Reasoning
Context Window200K
Knowledge CutoffUnknown
o3-mini vs DeepSeek V3
DeepSeek V3 for general open-weight language tasks, o3-mini for structured reasoning under cost constraints.
Open model page
DeepSeek V3
DeepSeek • Text
Context Window128K
Knowledge CutoffUnknown
Editorial Verdict
o3-mini is OpenAI's reasoning specialist; DeepSeek V3 is the versatile open model.
o3-mini and DeepSeek V3 are both efficient for their capability level. o3-mini leads on hard reasoning tasks; DeepSeek V3 leads on coding and general versatility. For math and science reasoning, choose o3-mini. For coding and general-purpose tasks, DeepSeek V3 is compelling at a lower cost.
o3-mini
- Superior performance on hard math and science reasoning benchmarks
- Configurable reasoning depth for task-appropriate inference
- OpenAI's ecosystem integration and mature API
Best for: Hard reasoning tasks integrated with OpenAI ecosystem
DeepSeek V3
- Versatile general-purpose model with strong coding performance
- Open weights available for self-hosting
- Lower cost per token than o3-mini for general tasks
Best for: General-purpose coding and self-hosted deployments
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 | DeepSeek V3 | 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
VariableDeepSeek V3 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)DeepSeek V3
76%
69%
o3-miniHumanEval Signal (Coding)DeepSeek V3
69%
69%
o3-miniAgentic Tooling SignalDeepSeek V3
69%
69%
o3-miniMultimodal SignalDeepSeek V3
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.
DeepSeek V3 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)DeepSeek (Python)
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
model="deepseek/deepseek-v3",
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 DeepSeek V3 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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