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Model comparison

Kimi K2 Thinking vs DeepSeek-V3.2-Exp

Moonshot AI (Kimi)Kimi K2 Thinking
Input
$0.60 / 1M tokens
Output
$2.50 / 1M tokens
Context
256K
Released
2025-11-06
Full model page →DeepSeekDeepSeek-V3.2-Exp
Input
$0.28 / 1M tokens (cache miss)
Output
$0.42 / 1M tokens
Context
128K
Released
2025-09-29
Full model page →
Short answer

Kimi K2 Thinking scores higher on 4 of the 5 benchmarks both models report (the other leads on 1); DeepSeek-V3.2-Exp is cheaper on input tokens ($0.3 vs $0.6 per 1M, 2.1×); Kimi K2 Thinking has the larger context window (256K vs 128K).

Specs and pricing side by side

SpecKimi K2 ThinkingDeepSeek-V3.2-Exp
MakerMoonshot AI (Kimi)DeepSeek
Released2025-11-062025-09-29
Context window256K128K
Max output256K64K
Input price$0.60 / 1M tokens$0.28 / 1M tokens (cache miss)
Output price$2.50 / 1M tokens$0.42 / 1M tokens
Parameters1T total / 32B active (MoE)685B total (~37B active, MoE)
LicenseModified MIT LicenseMIT
WeightsOpen weightsOpen weightssame
ModalitiesTextTextsame

Not published by either maker: knowledge cutoff.

Benchmarks both models report

BenchmarkKimi K2 ThinkingDeepSeek-V3.2-Exp
Humanity's Last Exam44.9%19.8%
BrowseComp60.2%40.1%
AIME 202599.1%89.3%
HMMT 202595.1%83.6%
MMLU-Pro84.6%85%

Scores are as published by each maker or its cited source; harness, reasoning effort and date can differ between rows, so treat small gaps as ties. Sources are listed on each model page.

Which one should you pick?

Pick Kimi K2 Thinking if…

  • You care about Humanity's Last Exam, BrowseComp, AIME 2025 (it scores higher on 4 shared benchmarks).
  • You need the longer context (256K vs 128K).
  • Your use case is: autonomous research agents that browse, gather, and synthesize across many steps.

Pick DeepSeek-V3.2-Exp if…

  • You care about MMLU-Pro (it scores higher on 1 shared benchmark).
  • Cost matters: input tokens cost 2.1× less ($0.3 vs $0.6 per 1M).
  • Your use case is: long-context document, codebase, and transcript analysis where attention cost dominates.

FAQ

Which is better, Kimi K2 Thinking or DeepSeek-V3.2-Exp?

Kimi K2 Thinking scores higher on 4 of the 5 benchmarks both models report (the other leads on 1); DeepSeek-V3.2-Exp is cheaper on input tokens ($0.3 vs $0.6 per 1M, 2.1×); Kimi K2 Thinking has the larger context window (256K vs 128K). The better pick depends on whether you value benchmark scores, price per token, context length or openness for your workload.

How much cheaper is DeepSeek-V3.2-Exp?

Kimi K2 Thinking: $0.60 / 1M tokens input, $2.50 / 1M tokens output. DeepSeek-V3.2-Exp: $0.28 / 1M tokens (cache miss) input, $0.42 / 1M tokens output (list API prices as published by each maker).

What are the context windows of Kimi K2 Thinking and DeepSeek-V3.2-Exp?

Kimi K2 Thinking offers 256K with up to 256K output; DeepSeek-V3.2-Exp offers 128K with up to 64K output.

Is Kimi K2 Thinking newer than DeepSeek-V3.2-Exp?

Kimi K2 Thinking was released 2025-11-06; DeepSeek-V3.2-Exp was released 2025-09-29.

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