Kimi K2.5 vs Nemotron 3 Nano 30B-A3B: Benchmark King vs the Throughput Machine

This comparison pits the highest-scoring open-weight model against the most architecturally interesting one. Kimi K2.5 is Moonshot AI's trillion-parameter MoE with benchmark scores that compete with proprietary frontier models. Nemotron 3 Nano 30B-A3B is NVIDIA's experiment in what happens when you combine Mamba2 state-space layers with sparse MoE routing and optimize relentlessly for inference throughput. The result is a model with 1 million tokens of context, 3.3x the throughput of comparably-sized transformers, and API pricing at $0.06/$0.24 per million tokens - roughly 10x cheaper than K2.5.

K2.5 wins on raw intelligence by wide margins. That is not the interesting part of this comparison. The interesting part is that Nemotron represents a fundamentally different bet on what matters in AI deployment - one where tokens-per-second and cost-per-token matter more than ceiling benchmark scores. In a world where many production workloads need "good enough" quality at maximum throughput, NVIDIA's architecture choices may prove prescient.

Quick Comparison

Kimi K2.5: Maximum Intelligence per Token

K2.5 represents the high-water mark for open-weight model capability as of early 2026. Every token it generates comes from a routing decision across 384 specialized experts, selecting the 8 most relevant for each input token across 61 layers. The PARL-trained routing policy ensures that mathematical tokens activate math-specialized experts, code tokens activate code experts, and so on. This structured specialization is why K2.5 achieves benchmark scores that compete with models costing 5 to 20 times more per token.

The math performance is essentially at the ceiling. AIME 2025 at 96.1 means K2.5 solves 96% of American Invitational Mathematics Examination problems - questions designed to challenge the top 5% of high school mathematics students. HMMT at 95.4 shows equivalent strength on Harvard-MIT Mathematics Tournament problems. For a comprehensive ranking of math capability across all models, see our math olympiad leaderboard.

Coding capability is equally strong. SWE-bench Verified at 76.8% places K2.5 among the elite for resolving real GitHub issues. LiveCodeBench v6 at 85.0 confirms competitive programming strength. Terminal Bench 2.0 at 50.8 adds system-level operation capability. The Agent Swarm feature, enabling up to 100 coordinated sub-agents, makes K2.5 particularly suited for complex software engineering tasks that require reasoning across multiple files, understanding project architecture, and planning multi-step changes.

The vision system (MoonViT-3D, 400M parameters) processes images and video at native resolution, and the multimodal benchmarks confirm it works - MMMU-Pro at 78.5, OCRBench at 92.3. This is a model designed to handle whatever you throw at it, from a calculus proof to a screenshot of a bug report to a video walkthrough of a UI.

Nemotron 3 Nano 30B-A3B: Architecture as Strategy

Nemotron 3 Nano is not trying to beat K2.5 on benchmarks. It is trying to redefine what efficiency looks like in production AI deployment. The hybrid Mamba2 + MoE architecture is NVIDIA's answer to a question the industry has been circling: what if transformers are not the optimal architecture for inference-heavy workloads?

The Mamba2 state-space model layers replace some of the transformer's attention layers, fundamentally changing the computational profile. Traditional transformers scale quadratically with sequence length due to self-attention - processing a 1M-token input requires comparing every token against every other token. Mamba2 layers process sequences in linear time, which is why Nemotron can offer a 1 million token context window without the astronomical cost that would imply for a pure transformer. For a deeper understanding of how long-context models compare, see our long context benchmarks leaderboard.

The throughput numbers are the headline. NVIDIA reports 3.3x higher throughput compared to transformer models of equivalent active parameter count. At 3.2 billion active parameters, Nemotron is already lightweight - add the Mamba2 efficiency and you get a model that can serve requests at a rate that makes latency-sensitive production deployments practical even on modest hardware.

The API pricing reflects this efficiency. At $0.06 per million input tokens and $0.24 per million output tokens through DeepInfra, Nemotron is 10x cheaper than K2.5 on input and 12.5x cheaper on output. For high-volume applications - chatbots, document processing pipelines, code completion at scale - this pricing difference compounds into significant savings. Our cost efficiency leaderboard puts these economics in broader context.

NVIDIA hardware optimization is the other strategic advantage. Nemotron is specifically designed to extract maximum performance from NVIDIA GPUs, leveraging CUDA-optimized kernels for the Mamba2 layers and TensorRT integration for deployment. If your infrastructure is NVIDIA-based - and statistically, it probably is - Nemotron runs on your existing stack with minimal friction.

Benchmark Comparison

The benchmark table is almost entirely one-sided in K2.5's favor because Nemotron operates at a fundamentally different capability tier. With 10x fewer active parameters, Nemotron's reasoning ceiling is proportionally lower. NVIDIA has not published scores on the hardest academic benchmarks because the model was not designed to compete there.

But benchmarks measure capability ceiling, not production value. A model that scores 50% on GPQA Diamond but serves answers 3.3x faster at 10x lower cost may deliver more total value in a high-volume deployment than a model scoring 87.6% that costs 10x more per token. The right metric depends entirely on your application. If every wrong answer costs money or reputation, K2.5's quality ceiling matters. If you are serving millions of requests where "good enough" quality at low latency drives engagement, Nemotron's throughput advantage matters more. For a nuanced take on this trade-off, see our article on whether AI benchmarks still matter.

Pricing Analysis

The economics here are dramatic. For a workload processing 100 million tokens per day (a reasonable volume for a production chatbot or document processing pipeline), the daily costs break down as follows:

• K2.5: $60 input + $300 output = approximately $360/day or $10,800/month
• Nemotron: $6 input + $24 output = approximately $30/day or $900/month

That is a 12x cost difference at the same volume. Over a year, the gap is roughly $118,800 versus $10,800 - more than $100,000 in savings. And because Nemotron processes tokens 3.3x faster, you need fewer GPU instances to serve the same request volume, further reducing infrastructure costs for self-hosted deployments.

The self-hosting economics are equally asymmetric. Nemotron fits on a single consumer GPU and serves requests at high throughput. K2.5 requires a multi-node enterprise cluster. The capital expenditure difference alone can be six figures. For comprehensive pricing data across all models, check our cost efficiency leaderboard.

Kimi K2.5: Pros and Cons

Pros:

• AIME 2025 96.1 and GPQA Diamond 87.6 - best reasoning in open weights
• SWE-bench Verified 76.8% - elite real-world coding capability
• MoonViT-3D vision with native resolution images and video
• Agent Swarm orchestrates up to 100 sub-agents
• BrowseComp 78.4% (swarm) shows strong web navigation
• OSWorld 63.3 and WebArena 58.9 confirm agentic capability
• Modified MIT license for commercial deployment

Cons:

• 10x more expensive per token than Nemotron
• 1T parameters requires enterprise cluster infrastructure
• 256K context is 3.9x shorter than Nemotron's 1M
• Lower throughput due to larger active parameter count
• Not practical for high-volume, latency-sensitive deployments
• API limited to Moonshot's infrastructure

Nemotron 3 Nano 30B-A3B: Pros and Cons

Pros:

• 3.3x throughput advantage over comparable transformers
• 1M token context window - 3.9x longer than K2.5
• $0.06/$0.24 per million tokens - 10x cheaper than K2.5
• Hybrid Mamba2+MoE enables linear-time sequence processing
• Runs on a single consumer GPU for self-hosting
• NVIDIA hardware optimization with CUDA and TensorRT integration
• Open source license for full commercial use

Cons:

• Significantly lower reasoning capability than K2.5
• No published scores on GPQA, AIME, SWE-bench, or other hard benchmarks
• No vision or multimodal capabilities
• No multi-agent orchestration features
• 3.2B active parameters limits knowledge depth and breadth
• Mamba2 architecture has less community tooling than transformers

Verdict

Choose Kimi K2.5 if your application's value depends on answer quality. Mathematical reasoning, scientific analysis, complex software engineering, multimodal understanding, and multi-agent workflows all require the depth that K2.5's trillion-parameter architecture provides. When a wrong answer costs more than the inference - medical analysis, legal reasoning, critical code generation - K2.5's benchmark ceiling justifies its cost. For context on where it sits in the overall rankings, see the overall LLM rankings.

Choose Nemotron 3 Nano 30B-A3B if your application's value depends on throughput, cost, and context length. High-volume chatbots, document processing pipelines, long-context summarization, RAG systems processing large knowledge bases, and latency-sensitive consumer applications all benefit from Nemotron's 3.3x throughput advantage and 10x lower cost. The 1M context window enables use cases - full codebase analysis, book-length document processing - that K2.5's 256K cannot accommodate. For more on RAG architecture and how long context fits in, see our guide to RAG.

The bottom line: This is a comparison between a model optimized for intelligence and a model optimized for deployment. K2.5 gives you the best answers. Nemotron gives you the most answers per second at the lowest cost. The right choice depends on whether your bottleneck is quality or throughput - and in production, knowing which one actually limits your system is the most important architectural decision you will make.

Sources

• Kimi K2.5 Technical Report - Moonshot AI
• Nemotron 3 Nano Technical Blog - NVIDIA
• Kimi K2.5 Model Card - Hugging Face
• Nemotron 3 Nano Model Card - Hugging Face
• Mamba2 Architecture Paper
• LLM Benchmarks 2026 - llm-stats.com
• DeepInfra Pricing