R1
DeepSeek • text->text
Context Window64K
Knowledge CutoffJan 20, 2025
R1 vs Gemini 2.5 Pro
DeepSeek R1 for math and coding reasoning, Gemini 2.5 Pro for multimodal long-context understanding.
Open model page
Gemini 2.5 Pro
Google • text+image+file+audio+video->text
Context Window1.0M
Knowledge CutoffJun 17, 2025
Editorial Verdict
DeepSeek R1 is the reasoning specialist; Gemini 2.5 Pro is the versatile multimodal flagship.
DeepSeek R1 and Gemini 2.5 Pro represent two different approaches — R1 is a reasoning specialist for hard problems; Gemini 2.5 Pro is a versatile multimodal flagship. For hard math and science, DeepSeek R1 leads. For general-purpose production with multimodal and long context, Gemini 2.5 Pro wins.
R1
- State-of-the-art on hard math, competitive programming, and science benchmarks
- Transparent reasoning chain for auditability
- Open weights for self-hosting with lower cost than Gemini 2.5 Pro
Best for: Hard math, science, and competitive programming reasoning tasks
Gemini 2.5 Pro
- 1M token context for processing massive documents and codebases
- Multimodal reasoning with native video and audio understanding
- Versatile general-purpose model suitable for diverse production applications
Best for: Long-context multimodal production applications
Pricing sourced from OpenRouter — updates as their catalog changes.
Pricing Comparison
All values pull from OpenRouter and update as their catalog changes.
| Metric | R1 | Gemini 2.5 Pro | Winner |
|---|---|---|---|
| Input (per 1M tokens) | $0.70 | $1.25 | 🏆 R1 |
| Output (per 1M tokens) | $2.50 | $10.00 | 🏆 R1 |
| Request fee | N/A | N/A | N/A |
| Image fee | N/A | $0.000001 | 🏆 Gemini 2.5 Pro |
Cost Estimator
Estimate monthly billing using real OpenRouter prices.
R1 Total
$62Gemini 2.5 Pro Total
$193.75Estimated Monthly Savings: $131.75 with R1
Capability Signals
Scores are directional estimates from model metadata — not official benchmark results.
R1MMLU Signal (Reasoning)Gemini 2.5 Pro
82%
92%
R1HumanEval Signal (Coding)Gemini 2.5 Pro
71%
81%
R1Agentic Tooling SignalGemini 2.5 Pro
79%
89%
R1Multimodal SignalGemini 2.5 Pro
64%
89%
Capabilities Matrix
Feature highlights for architecture and production fit.
R1 Strengths
- Strong tool-calling and structured response support.
- Balanced general-purpose profile for chat, extraction, and automation tasks.
Gemini 2.5 Pro Strengths
- Supports image inputs for multimodal analysis workflows.
- Supports audio and speech scenarios with native modality support.
- Strong tool-calling and structured response support.
API Implementation
Quick start snippets for each provider style.
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)Google (Python)
from google import genai client = genai.Client(api_key="YOUR_API_KEY") response = client.models.generate_content( model="google/gemini-2.5-pro", contents="Hello" ) print(response.text)
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.
Choose Gemini 2.5 Pro when...
- You need low-latency voice and speech workflows with Gemini 2.5 Pro.
- Your application includes visual reasoning and image understanding tasks.
- You process large documents or code repositories in a single prompt.
Frequently Asked Questions
Practical checks before selecting a production model.
Is DeepSeek R1 better than Gemini 2.5 Pro?
It depends on the workload. DeepSeek R1 and Gemini 2.5 Pro differ in pricing, context window, and capability mix — see the side-by-side table above for the breakdown.
What's the price difference between DeepSeek R1 and Gemini 2.5 Pro?
DeepSeek R1: $0.70 per 1M input tokens. Gemini 2.5 Pro: $1.25 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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