Kimi K2.5 vs DeepSeek V3.2: The Battle of Open-Weight Chinese MoE Giants

Two open-weight Chinese MoE models. Two very different bets on what matters. Kimi K2.5 is Moonshot AI's trillion-parameter flagship - 32 billion active parameters, native multimodal, Agent Swarm, and benchmark scores that compete with the best closed-source models. DeepSeek V3.2 is the model that broke the pricing floor - 671 billion total parameters, 37 billion active, MIT licensed, and an API so cheap it reshaped the entire industry's expectations about what inference should cost.

K2.5 wins most benchmarks. DeepSeek V3.2 wins on price by a large margin. The question is whether the benchmark advantages justify paying 7x more on output tokens.

On AIME 2025: K2.5 scores 96.1 versus DeepSeek's 93.1. On GPQA Diamond: 87.6 versus 82.4. On SWE-bench Verified: 76.8% versus 73.1%. On BrowseComp with Agent Swarm: 78.4% versus DeepSeek's best of 67.6%. K2.5 leads across every published benchmark. But DeepSeek charges $0.28 per million input tokens and $0.42 per million output tokens. K2.5 charges $0.60/$3.00. On output - the token direction that dominates most bills - DeepSeek is 7.1x cheaper.

Quick Comparison

Kimi K2.5: The Capability Ceiling

K2.5 pushes further on every reasoning and agent benchmark than DeepSeek V3.2 does. The 3-point lead on AIME (96.1 vs 93.1) represents genuine separation on the hardest math competition problems. The 5.2-point GPQA Diamond gap (87.6 vs 82.4) is even more telling - graduate-level scientific reasoning is where the depth of a model's understanding shows, and K2.5 is decisively ahead.

The architecture runs 384 experts with 8 active per token across 61 layers, resulting in 32 billion active parameters from a 1-trillion-parameter pool. Compared to DeepSeek's 37 billion active from 671 billion total, K2.5 actually activates fewer parameters while achieving higher scores. The FLOP efficiency per correct answer favors Moonshot's design.

Where K2.5 truly separates itself is on capabilities DeepSeek simply does not have. MoonViT-3D is a 400-million-parameter vision encoder that handles native-resolution images and video without resizing or tiling hacks. OCRBench at 92.3 and MMMU-Pro at 78.5 mean K2.5 can read documents, interpret charts, and analyze visual content. DeepSeek V3.2 is text-only. If your workflow involves any visual input, this comparison ends immediately.

The Agent Swarm is the other differentiator. Trained via PARL reinforcement learning, K2.5 can orchestrate up to 100 sub-agents that collaboratively tackle complex research and reasoning tasks. BrowseComp shows the impact: 78.4% with the swarm versus 60.6% single-agent. DeepSeek's BrowseComp range of 51.4-67.6% falls below K2.5 in both configurations. For research-heavy workflows where a model needs to decompose a complex question, search multiple sources, and synthesize findings, the swarm architecture is a genuine structural advantage.

The 256K context window doubles DeepSeek's 128K. For long-document processing, extended conversations, or codebases that need to fit in a single prompt, K2.5 offers more room to work.

DeepSeek V3.2: The Price That Changes the Math

DeepSeek V3.2's value proposition is brutally simple: it is 7x cheaper on output tokens than K2.5, and the benchmarks are not 7x worse. They are 3-5 points lower on most tasks. That arithmetic is what makes this comparison genuinely difficult.

At $0.28 per million input tokens (cache miss) and $0.42 per million output tokens, DeepSeek V3.2 is the cheapest frontier-quality API in the world. Cache hits drop the input price to $0.028 - effectively 35 million input tokens per dollar. New users get 5 million free tokens with no credit card. This pricing is not a loss leader; DeepSeek has engineered their inference stack around Multi-Latent Attention (MLA) and DeepSeek Sparse Attention (DSA) to make these margins work at scale.

AIME 2025 at 93.1 is not 96.1, but it is still outstanding. This is a model that solves the vast majority of competition math problems. MMLU-Pro at 85.0 versus K2.5's 87.1 is a 2.1-point gap that most users will never notice in practice. LiveCodeBench at 83.3 versus K2.5's 85.0 is similarly tight. Codeforces at 2386 - a benchmark K2.5 has not published - shows DeepSeek's competitive programming strength.

The MIT license is maximally permissive, arguably more so than K2.5's Modified MIT. There are no commercial restrictions, no attribution requirements beyond what MIT specifies. The weights are freely available, the community is massive, and the third-party ecosystem is the largest of any open-weight model. For a deeper look at DeepSeek's strengths and weaknesses, see our DeepSeek V3.2 review.

SWE-bench at 73.1% versus K2.5's 76.8% is a real gap - 3.7 points on real-world code repair. But at 7x the output cost, every K2.5 API call that fails still costs more than multiple DeepSeek attempts. For many teams, the cost-effective strategy is to call DeepSeek more times rather than pay for K2.5's higher single-pass accuracy.

Benchmark Comparison

K2.5 leads every shared benchmark. The gap ranges from 1.7 points (LiveCodeBench) to over 10 points (BrowseComp, GPQA Diamond). But none of these gaps are so large that DeepSeek feels like a lesser model. It is more like comparing a high-end sedan to a slightly faster one that costs 7x more to fuel.

Pricing Analysis

The output cost ratio is the decisive number. A team generating 10 million output tokens per day pays $30/day with K2.5 or $4.20/day with DeepSeek. Annualized: $10,950 versus $1,533. Over a year, the savings from DeepSeek could fund significant GPU infrastructure for self-hosting. For teams exploring self-hosted deployment of either model, see our best local LLM tools guide.

Both models are large enough that self-hosting requires serious GPU infrastructure. K2.5 at 1T total parameters is larger than DeepSeek's 671B, though K2.5 activates fewer parameters per token (32B vs 37B). Neither model is running on consumer hardware.

Kimi K2.5: Pros and Cons

Pros:

• Leads DeepSeek V3.2 on every shared benchmark
• GPQA Diamond 87.6 versus 82.4 - substantially stronger scientific reasoning
• Native multimodal via MoonViT-3D (DeepSeek is text-only)
• Agent Swarm with up to 100 sub-agents for complex research
• 256K context window (2x DeepSeek's 128K)
• AIME 96.1 and HMMT 95.4 for elite math reasoning

Cons:

• 7.1x more expensive on output tokens ($3.00 vs $0.42)
• 2.1x more expensive on input tokens ($0.60 vs $0.28)
• Modified MIT is slightly less permissive than DeepSeek's pure MIT
• 1T total parameters requires more infrastructure to self-host
• Smaller community and ecosystem than DeepSeek
• No published Codeforces rating for competitive programming comparison

DeepSeek V3.2: Pros and Cons

Pros:

• 7.1x cheaper output tokens - the lowest price for frontier-quality inference
• Cache hit pricing of $0.028/M input tokens is effectively free
• MIT license - maximally permissive
• Codeforces 2386 shows strong competitive programming
• Massive community, extensive third-party ecosystem
• 5 million free tokens for new users

Cons:

• Text-only - no native vision or video processing
• 128K context window is half of K2.5's 256K
• GPQA Diamond 82.4 trails by 5.2 points on hard reasoning
• No Agent Swarm or multi-agent orchestration
• BrowseComp 51.4-67.6% significantly trails K2.5's 78.4%
• SWE-bench 73.1% trails K2.5's 76.8% on code repair

Verdict

Choose Kimi K2.5 if you are building a system where peak accuracy matters more than per-token cost, especially if your workload involves vision, video, or multi-agent research. The benchmark leads are consistent and meaningful - not just on one task but across the board. The Agent Swarm makes K2.5 uniquely suited for complex research workflows that require decomposition and parallel investigation. If you can self-host, the per-token cost argument weakens, and K2.5's capabilities become unambiguously the better choice.

Choose DeepSeek V3.2 if you are optimizing for cost efficiency at scale and your workload is text-only. The 7x output price advantage is not subtle - it changes whether entire product categories are economically viable. For high-volume text processing, chatbot deployments, batch analysis, or any application where you can tolerate 3-5 point lower benchmarks in exchange for dramatically lower costs, DeepSeek is the rational pick. The enormous community and MIT license also mean better tooling support and zero licensing friction. For a broader view of how these models rank against the full field, see our open-source LLM leaderboard.

Sources

• Kimi K2.5 Technical Report - Moonshot AI
• DeepSeek V3.2 Technical Report
• DeepSeek API Pricing
• OpenRouter Model Pricing