Compare the listed model data for DeepSeek-R1 by DeepSeek and DeepSeek-R1 (Thinking Model) by DeepSeek. Benchmark and pricing records are not a controlled test and may change.
Data last updated: 2026-10-16. Verify current model details with each provider.
What the listed data says
DeepSeek-R1: 128K tokens; DeepSeek-R1 (Thinking Model): 128K tokens. The listed context-window values are equal. DeepSeek-R1 is listed for coding, reasoning, long-context tasks; DeepSeek-R1 (Thinking Model) is listed for coding, reasoning, long-context tasks. Shared catalog benchmark entries: MATH-500: DeepSeek-R1 97.3%; DeepSeek-R1 (Thinking Model) 97.3%; AIME 2024: DeepSeek-R1 79.8%; DeepSeek-R1 (Thinking Model) 79.8%; SWE-bench Verified: DeepSeek-R1 49.2%; DeepSeek-R1 (Thinking Model) 49.2%. These records are reference data, not a controlled head-to-head test; verify current pricing and specifications with each provider.
Catalog source links
Use these model and provider links to verify current specifications, pricing, and availability.
The first listed parameter quantities are 671B total (37B active via MoE) and 671B total (37B active via MoE); the larger quantity is 1× the smaller. Total and active MoE parameters are not directly equivalent.
The catalog release dates are January 2025 and January 2025, about 0 months apart at the precision supplied.
Cost example: 1M input and 1M output tokens
A cost example for 1M input plus 1M output tokens cannot be calculated reliably because separate numeric input and output rates are not listed for both models.
Choose DeepSeek-R1 if…
Choose DeepSeek-R1 when your task aligns with catalog-listed uses or strengths such as Competition mathematics and proof verification, Advanced software engineering: debugging complex systems, production-grade algorithms, Scientific research in physics, chemistry, biology, Enterprise AI agents needing transparent, auditable reasoning.
Choose DeepSeek-R1 (Thinking Model) if…
Choose DeepSeek-R1 (Thinking Model) when your task aligns with catalog-listed uses or strengths such as Competition mathematics, Advanced software engineering, Scientific research, MIT license — free for commercial use.
Feature Comparison — DeepSeek-R1 vs DeepSeek-R1 (Thinking Model)
Catalog specifications for DeepSeek-R1 and DeepSeek-R1 (Thinking Model)
Specification
DeepSeek-R1
DeepSeek-R1 (Thinking Model)
Provider
DeepSeek
DeepSeek
Parameters
671B total (37B active via MoE)
671B total (37B active via MoE)
Context window
128K tokens
128K tokens
Maximum output
Not listed
Not listed
Catalog pricing
Open source MIT license — free to self-host. DeepSeek API: $0.14/MTok input (cache hit), $0.55/MTok input, $2.19/MTok output
MIT — free. DeepSeek API: $0.55/MTok input
Listed input/output cost per 1M tokens
input $0.55, output $2.19 per 1M tokens
Separate numeric input/output rates not listed
License
MIT
MIT
Coding support
Listed
Listed
Reasoning support
Listed
Listed
Vision input
Not listed
Not listed
Multimodal
Not listed
Not listed
Open weights
Yes
Yes
Benchmark: MATH-500
97.3%
97.3%
Benchmark: AIME 2024
79.8%
79.8%
Benchmark: SWE-bench Verified
49.2%
49.2%
Figures are transcribed from LITHOVEX model records. Benchmark versions and evaluation setups may differ; pricing and specifications can change. Check each provider's current documentation before making a decision.
Frequently Asked Questions
What is different between DeepSeek-R1 and DeepSeek-R1 (Thinking Model)?
DeepSeek-R1: 128K tokens; DeepSeek-R1 (Thinking Model): 128K tokens. The listed context-window values are equal. DeepSeek-R1 is listed for coding, reasoning, long-context tasks; DeepSeek-R1 (Thinking Model) is listed for coding, reasoning, long-context tasks. Shared catalog benchmark entries: MATH-500: DeepSeek-R1 97.3%; DeepSeek-R1 (Thinking Model) 97.3%; AIME 2024: DeepSeek-R1 79.8%; DeepSeek-R1 (Thinking Model) 79.8%; SWE-bench Verified: DeepSeek-R1 49.2%; DeepSeek-R1 (Thinking Model) 49.2%. These records are reference data, not a controlled head-to-head test; verify current pricing and specifications with each provider.
What benchmark values are listed for DeepSeek-R1 and DeepSeek-R1 (Thinking Model)?
MATH-500: DeepSeek-R1 97.3%; DeepSeek-R1 (Thinking Model) 97.3%; AIME 2024: DeepSeek-R1 79.8%; DeepSeek-R1 (Thinking Model) 79.8%; SWE-bench Verified: DeepSeek-R1 49.2%; DeepSeek-R1 (Thinking Model) 49.2%. The catalog values may use different versions and evaluation setups, so they are reference points rather than a controlled head-to-head result.
Which model has the larger listed context window?
DeepSeek-R1: 128K tokens; DeepSeek-R1 (Thinking Model): 128K tokens. The listed context-window values are equal.
What are the listed costs per million tokens for DeepSeek-R1 and DeepSeek-R1 (Thinking Model)?
DeepSeek-R1: input $0.55, output $2.19 per 1M tokens (Open source MIT license — free to self-host. DeepSeek API: $0.14/MTok input (cache hit), $0.55/MTok input, $2.19/MTok output). DeepSeek-R1 (Thinking Model): Separate numeric input/output rates not listed (MIT — free. DeepSeek API: $0.55/MTok input). Where separate numeric input and output rates are available, the worked example uses 1M input plus 1M output tokens. Confirm current terms with each provider.
How far apart are the listed release dates for DeepSeek-R1 and DeepSeek-R1 (Thinking Model)?
The catalog lists DeepSeek-R1 as January 2025 and DeepSeek-R1 (Thinking Model) as January 2025, about 0 months apart at the precision supplied.
When was the comparison data last updated?
The model catalog records 2026-10-16 as its data update date. Provider specifications and prices may have changed since then.
How should I choose between DeepSeek-R1 and DeepSeek-R1 (Thinking Model)?
Compare the listed specifications and capabilities, review the benchmark notes, then test both with the same representative prompt for your task. This page does not claim a universal winner.
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