Comparing OpenAI's cost-efficient reasoning engine against the open-weight reasoning champion. Analyze token unit economics, reasoning effort controls (low/medium/high), AIME math performance, and coding benchmarks.
Calculate monthly bills across OpenAI o3-mini and DeepSeek-R1 for automated STEM problem solving and coding.
Evaluating architecture specifications, benchmark rates, throughput latency, and pricing tiers.
| Dimension | OpenAI o3-mini | DeepSeek-R1 | Key Takeaway / Advantage |
|---|---|---|---|
| Input Cost / 1M Tokens | $1.10 | $0.55 | DeepSeek-R1 (2x cheaper) |
| Output Cost / 1M Tokens | $4.40 | $2.19 | DeepSeek-R1 (2x cheaper) |
| AIME 2024 Math Score | 87.3% (effort: high) | 79.8% | OpenAI o3-mini (+7.5%) |
| SWE-bench Verified Coding | 49.3% | 49.2% | Tie (~49.2%) |
| Reasoning Effort Modulation | Native low / medium / high control | Fixed dynamic RL generation | OpenAI o3-mini |
| Thought Process Transparency | Redacted / encrypted reasoning tokens | 100% visible in API response | DeepSeek-R1 |
| Structured JSON & Tool Calling | Native function calling & schemas | Text prompting & wrapper parsing | OpenAI o3-mini |
OpenAI o3-mini is an exceptional engineering achievement that democratizes frontier reasoning. By introducing the reasoning_effort parameter (low, medium, high), o3-mini gives developers granular control over latency and token consumption. On 'high' reasoning effort, o3-mini achieves an astonishing 87.3% on AIME 2024 math—surpassing OpenAI's original $15/$60 o1 model—while costing just $1.10 input and $4.40 output.
However, DeepSeek-R1 is 50% cheaper on both inputs ($0.55/M) and outputs ($2.19/M), and provides something OpenAI strictly redacts: 100% transparent access to the internal chain-of-thought tokens. For research teams, educational platforms, and audit workflows where seeing the model's step-by-step logic is critical, DeepSeek-R1 is unmatched. Both models represent the pinnacle of cost-effective AI reasoning.
Evaluating real monthly unit economics across practical engineering architectures.
Solving collegiate calculus and olympiad physics problems. o3-mini's 87.3% AIME math score delivers top-tier academic accuracy.
Auditing automated financial risk assessments where regulators require seeing the internal reasoning steps. DeepSeek-R1 exposes the full
Generating 20M tokens of unit tests. DeepSeek-R1 costs $43.80 versus o3-mini's $88.00 with identical 49.2% SWE-bench performance.
import os
from openai import OpenAI
client = OpenAI()
def solve_with_o3_mini(problem: str, effort: str = "medium"):
"""Run OpenAI o3-mini with adjustable reasoning effort (low|medium|high)."""
response = client.chat.completions.create(
model="o3-mini",
messages=[{"role": "user", "content": problem}],
reasoning_effort=effort # "low" for speed, "high" for hard math
)
return response.choices[0].message.contentReal-world engineering trade-offs and hidden bill shock traps to watch out for.
Explore complementary benchmarks and financial modeling tools across the index.
Compare flagship reasoning models head-to-head.
Compare dynamic hybrid thinking against pure RL reasoning.
Explore o1, o3-mini, GPT-4o, and Realtime audio pricing.
Rankings of all budget AI token providers on the market.
DeepSeek-R1 is roughly 50% cheaper: $0.55/M input and $2.19/M output, compared to OpenAI o3-mini's $1.10/M input and $4.40/M output.
OpenAI allows developers to choose between 'low', 'medium', and 'high' reasoning effort on o3-mini. 'Low' reduces latency and token cost for easier tasks, while 'high' maximizes reasoning depth for difficult math and coding.
On 'high' reasoning effort, OpenAI o3-mini scores 87.3% on AIME 2024, outperforming DeepSeek-R1 (79.8%) and OpenAI's original o1 model (83.3%).
No. OpenAI redacts the internal chain-of-thought tokens on o3-mini and o1. DeepSeek-R1 provides 100% visibility into its reasoning trace via the reasoning_content API field.
Both models are virtually tied on SWE-bench Verified coding: OpenAI o3-mini scores 49.3% and DeepSeek-R1 scores 49.2%.
Yes! o3-mini natively supports function calling, structured JSON Schema outputs, and developer system messages.