Compare the listed model data for DeepSeek-R1 by DeepSeek and Kimi-K2 Thinking by Moonshot AI. 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; Kimi-K2 Thinking: 128K tokens. The listed context-window values are equal. DeepSeek-R1 is listed for coding, reasoning, long-context tasks; Kimi-K2 Thinking is listed for reasoning. Shared catalog benchmark entries: GPQA Diamond: DeepSeek-R1 71.5%; Kimi-K2 Thinking 78.2%; SWE-bench Verified: DeepSeek-R1 49.2%; Kimi-K2 Thinking 67.3%; LiveCodeBench: DeepSeek-R1 65.9%; Kimi-K2 Thinking 77.8%. 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 1T total (32B active); the larger quantity is 1.49× the smaller. Total and active MoE parameters are not directly equivalent.
The catalog release dates are January 2025 and July 2025, about 6 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 Kimi-K2 Thinking if…
Choose Kimi-K2 Thinking when your task aligns with catalog-listed uses or strengths such as Complex mathematical proofs, Research-grade scientific reasoning, Auditable AI reasoning workflows, 84.1% AIME 2025 — near-human on elite competition mathematics.
Feature Comparison — DeepSeek-R1 vs Kimi-K2 Thinking
Catalog specifications for DeepSeek-R1 and Kimi-K2 Thinking
Specification
DeepSeek-R1
Kimi-K2 Thinking
Provider
DeepSeek
Moonshot AI
Parameters
671B total (37B active via MoE)
1T total (32B active)
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
Moonshot API: beta
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
Kimi K2 Model License
Coding support
Listed
Not listed
Reasoning support
Listed
Listed
Vision input
Not listed
Not listed
Multimodal
Not listed
Not listed
Open weights
Yes
Yes
Benchmark: GPQA Diamond
71.5%
78.2%
Benchmark: SWE-bench Verified
49.2%
67.3%
Benchmark: LiveCodeBench
65.9%
77.8%
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 Kimi-K2 Thinking?
DeepSeek-R1: 128K tokens; Kimi-K2 Thinking: 128K tokens. The listed context-window values are equal. DeepSeek-R1 is listed for coding, reasoning, long-context tasks; Kimi-K2 Thinking is listed for reasoning. Shared catalog benchmark entries: GPQA Diamond: DeepSeek-R1 71.5%; Kimi-K2 Thinking 78.2%; SWE-bench Verified: DeepSeek-R1 49.2%; Kimi-K2 Thinking 67.3%; LiveCodeBench: DeepSeek-R1 65.9%; Kimi-K2 Thinking 77.8%. 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 Kimi-K2 Thinking?
GPQA Diamond: DeepSeek-R1 71.5%; Kimi-K2 Thinking 78.2%; SWE-bench Verified: DeepSeek-R1 49.2%; Kimi-K2 Thinking 67.3%; LiveCodeBench: DeepSeek-R1 65.9%; Kimi-K2 Thinking 77.8%. 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; Kimi-K2 Thinking: 128K tokens. The listed context-window values are equal.
What are the listed costs per million tokens for DeepSeek-R1 and Kimi-K2 Thinking?
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). Kimi-K2 Thinking: Separate numeric input/output rates not listed (Moonshot API: beta). 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 Kimi-K2 Thinking?
The catalog lists DeepSeek-R1 as January 2025 and Kimi-K2 Thinking as July 2025, about 6 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 Kimi-K2 Thinking?
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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