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o1 vs R1

o1 for highest managed reasoning quality, DeepSeek R1 for open-weight and competitive programming use cases.

Pricing source: live OpenRouter model catalog (`/api/v1/models`).

Model A

o1

OpenAI • text+image+file->text
Context Window200K
Knowledge CutoffDec 17, 2024
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Model B

R1

DeepSeek • text->text
Context Window64K
Knowledge CutoffJan 20, 2025
Open model page

Editorial Verdict

DeepSeek R1 is the open, transparent alternative to o1; o1 is OpenAI's mature reasoning ecosystem.

DeepSeek R1 matches or exceeds o1 on hard math and science benchmarks while being open and significantly cheaper. o1 has the advantage of OpenAI's mature ecosystem and production reliability. For pure reasoning quality and transparency, DeepSeek R1 wins. For ecosystem integration and production support, o1 wins.

o1
  • Battle-tested production reasoning model with established reliability
  • Mature API with enterprise support and consistent performance
  • Access to OpenAI's ecosystem and fine-tuning options

Best for: Enterprise reasoning applications requiring OpenAI ecosystem support

R1
  • Matches or exceeds o1 on math and science benchmarks at lower cost
  • Open weights for self-hosting and transparency
  • Visible reasoning chain for auditability

Best for: Open-source deployments, budget-conscious teams, and transparency requirements

Pricing sourced from OpenRouter — updates as their catalog changes.

Pricing Comparison

All values pull from OpenRouter and update as their catalog changes.

Metrico1R1Winner
Input (per 1M tokens)$15.00$0.70🏆 R1
Output (per 1M tokens)$60.00$2.50🏆 R1
Request feeN/AN/AN/A
Image feeN/AN/AN/A

Cost Estimator

Estimate monthly billing using real OpenRouter prices.

o1 Total

$1,425

R1 Total

$62
Estimated Monthly Savings: $1,363 with R1

Capability Signals

Scores are directional estimates from model metadata — not official benchmark results.

o1MMLU Signal (Reasoning)R1
87%
82%
o1HumanEval Signal (Coding)R1
76%
71%
o1Agentic Tooling SignalR1
84%
79%
o1Multimodal SignalR1
78%
64%

Capabilities Matrix

Feature highlights for architecture and production fit.

o1 Strengths

  • Supports image inputs for multimodal analysis workflows.
  • Strong tool-calling and structured response support.
  • Large context window for long documents and codebases.

R1 Strengths

  • Strong tool-calling and structured response support.
  • 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)

DeepSeek (Python)

from openai import OpenAI

client = OpenAI()
response = client.chat.completions.create(
  model="deepseek/deepseek-r1",
  messages=[{"role": "user", "content": "Hello"}]
)

print(response.choices[0].message.content)

Choose o1 when...

  • Your application includes visual reasoning and image understanding tasks.
  • You process large documents or code repositories in a single prompt.
  • You rely on function calling, tool chaining, and structured output contracts.

Choose R1 when...

  • You rely on function calling, tool chaining, and structured output contracts.
  • 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.

Is o1 better than DeepSeek R1?

It depends on the workload. o1 and DeepSeek R1 differ in pricing, context window, and capability mix — see the side-by-side table above for the breakdown.

What's the price difference between o1 and DeepSeek R1?

o1: $15.00 per 1M input tokens. DeepSeek R1: $0.70 per 1M input tokens. Output token pricing typically runs 2-5x higher.

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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