Kimi K2.5 vs Qwen3.5-122B-A10B: Trillion-Parameter Giant Meets the Efficiency Miracle

This comparison pits two philosophies of open-weight AI against each other in the starkest possible terms. Kimi K2.5 from Moonshot AI is a 1-trillion-parameter MoE model that throws 384 experts at every problem and achieves some of the strongest benchmarks in the open-weight space. Qwen3.5-122B-A10B from Alibaba is an 8.2x smaller model that activates just 10 billion parameters per token - and still manages benchmark scores that have no business being that close to the giant.

K2.5 wins every published head-to-head comparison. MMLU-Pro 87.1 versus 86.7. GPQA Diamond 87.6 versus 86.6. SWE-bench 76.8% versus 72.0%. But those gaps - 0.4, 1.0, and 4.8 points respectively - look very different when you consider that K2.5 activates 3.2x more parameters per token and requires multi-GPU clusters while Qwen fits on a single consumer GPU.

The real question: is K2.5's marginal benchmark lead worth the dramatically higher cost of running it?

Quick Comparison

Kimi K2.5: Winning by Every Metric That Fits on a Benchmark Table

K2.5 is Moonshot AI's maximum-capability play. With 384 experts, 8 active per token, and 61 layers, the architecture is designed to have an answer for everything. And it does. AIME 2025 at 96.1 is competition-grade math. HMMT 95.4 confirms it. GPQA Diamond 87.6 is graduate-level scientific reasoning. MMLU-Pro 87.1 is broad knowledge. SWE-bench 76.8% is real-world software engineering. LiveCodeBench 85.0 is algorithmic coding. The model does not have a weak spot in its published benchmark suite.

The vision system sets K2.5 apart. MoonViT-3D is a 400-million-parameter encoder that handles native-resolution images and video without downsampling. MMMU-Pro 78.5 and OCRBench 92.3 demonstrate production-grade visual understanding. Qwen3.5-122B-A10B has no vision capabilities in its current variant. If your pipeline includes document analysis, chart interpretation, or any visual reasoning, this is not a close call - K2.5 is the only option. For how these vision scores compare across the field, see our multimodal benchmarks leaderboard.

Agent Swarm gives K2.5 a unique structural advantage. Using PARL-trained coordination, K2.5 decomposes complex tasks across up to 100 sub-agents. BrowseComp jumps from 60.6% to 78.4% with the swarm enabled. OSWorld 63.3 and WebArena 58.9 show real agentic performance. Qwen offers no comparable multi-agent system. For teams building autonomous agent workflows, this is a differentiator that no amount of parameter efficiency can replace. See our guide to AI agents for architectural context.

The math performance deserves special emphasis. AIME 2025 at 96.1 is not just "good" - it is in the 99th percentile of all models tested. Qwen does not publish an AIME score, and while its GPQA Diamond 86.6 is strong, K2.5's mathematical reasoning represents a genuinely different tier. For applications in scientific computing, financial modeling, or any domain where mathematical precision matters, K2.5's advantage is decisive. See our math olympiad leaderboard for the full picture.

Qwen3.5-122B-A10B: The Model That Should Not Be This Good

Here is what makes Qwen3.5-122B-A10B remarkable: it is 8.2x smaller than K2.5 in total parameters, activates 3.2x fewer parameters per token, fits on consumer hardware, costs nothing to license, and still comes within a few points of K2.5 on the hardest reasoning benchmarks in the industry.

GPQA Diamond 86.6 versus K2.5's 87.6. That is a 1.0-point gap for a model running on 10 billion active parameters versus 32 billion. MMLU-Pro 86.7 versus 87.1 - a 0.4-point gap. On these two benchmarks, Qwen is delivering roughly 95-99% of K2.5's performance at less than a third of the per-token compute.

The architecture matters here. Alibaba's Qwen team combined Gated Delta Networks with sparse MoE routing, creating a hybrid system that is fundamentally more selective about parameter utilization. The result is a model that extracts more reasoning capability per FLOP than anything else in the open-weight space. If you normalize GPQA Diamond by active parameters, Qwen delivers 8.66 points per billion active parameters versus K2.5's 2.74. That is a 3.2x efficiency advantage. For a deeper dive into Qwen's architecture, see our Qwen 3 review.

The self-hosting story is where this efficiency becomes transformative. At FP8 quantization, Qwen3.5-122B-A10B fits in roughly 60-70 GB of VRAM. That is a dual RTX 5090 setup, a single A100 80GB, or a well-configured Mac Studio. K2.5's trillion parameters require hundreds of gigabytes of VRAM across multiple nodes. The infrastructure cost difference is not 3x or 5x - it is an order of magnitude. For guidance on running models locally, see our guide to running open-source LLMs locally.

The context windows are nearly identical - 262K for Qwen versus 256K for K2.5, with Qwen supporting extension to 1M+ tokens. This is a non-factor in the comparison.

Apache 2.0 versus Modified MIT licensing is a real consideration. Apache 2.0 imposes essentially zero restrictions on commercial use, modification, or distribution. K2.5's Modified MIT adds conditions that may matter depending on your use case. For teams that need maximum licensing flexibility, Qwen wins cleanly. For a broader comparison of open versus proprietary licensing, see our open-source vs proprietary AI guide.

Where Qwen falls short is everywhere that K2.5 publishes and Qwen does not: AIME (K2.5 at 96.1), vision (MoonViT-3D versus nothing), and agentic capability (Agent Swarm versus nothing). SWE-bench is also a meaningful gap - 76.8% versus 72.0% is 4.8 points, the largest head-to-head difference. On software engineering tasks, K2.5's advantage is more than marginal.

Benchmark Comparison

K2.5 wins every benchmark where both models publish scores. But the margins on MMLU-Pro and GPQA Diamond are slim enough that individual prompt variations could close the gap. SWE-bench and LiveCodeBench show more meaningful separation - K2.5 is genuinely stronger on software engineering and algorithmic coding. The vision and agentic categories are not close because Qwen does not compete there.

Pricing Analysis

The pricing comparison is heavily asymmetric. K2.5 requires either paying Moonshot's API rates or investing in enterprise-grade multi-node infrastructure. Qwen can run on hardware that a well-funded individual developer could own. Once you have provisioned a dual-GPU workstation for Qwen, your marginal cost per token is electricity. K2.5 cannot offer anything comparable. For current cost comparisons, see our cost efficiency leaderboard and our home GPU LLM leaderboard.

Kimi K2.5: Pros and Cons

Pros:

• AIME 96.1 - among the highest math scores from any open model
• Agent Swarm coordinates up to 100 sub-agents for complex workflows
• MoonViT-3D provides production-grade vision for images and video
• Wins every head-to-head benchmark against Qwen3.5-122B-A10B
• SWE-bench 76.8% and LiveCodeBench 85.0 show strong coding
• 384-expert architecture enables deep task-specific specialization

Cons:

• Benchmark margins over Qwen are slim on MMLU-Pro (+0.4) and GPQA (+1.0)
• Requires multi-node GPU clusters for self-hosting
• Modified MIT license is less permissive than Apache 2.0
• $0.60/$3.00 API pricing with limited provider options
• 3.2x more active parameters for relatively modest benchmark gains
• No self-hosting path for most teams and individuals

Qwen3.5-122B-A10B: Pros and Cons

Pros:

• GPQA 86.6 and MMLU-Pro 86.7 within 1 point of K2.5 at 3.2x fewer params
• Self-hostable on consumer hardware (60-70 GB VRAM at FP8)
• Apache 2.0 license - completely unrestricted commercial use
• 262K context with extension to 1M+ tokens
• Zero marginal inference cost once hardware is provisioned
• Best parameter efficiency ratio in the open-weight space

Cons:

• SWE-bench 72.0% trails K2.5's 76.8% by a meaningful margin
• LiveCodeBench ~78 versus K2.5's 85.0 shows a real coding gap
• No vision capabilities in the current variant
• No agentic features or multi-agent coordination
• No published AIME score to compare against K2.5's 96.1
• Limited third-party API availability

Verdict

Choose Kimi K2.5 if you need the absolute best performance available in the open-weight space and you have the budget and infrastructure to support it. The vision and Agent Swarm capabilities alone justify K2.5 for multimodal pipelines and agentic workflows - Qwen simply cannot compete there. For heavy mathematical reasoning (AIME 96.1) and software engineering (SWE-bench 76.8%), K2.5's lead is real, even if it is smaller than the 8.2x parameter difference would suggest. Full details at the Kimi K2.5 model card.

Choose Qwen3.5-122B-A10B if you want frontier-adjacent reasoning that you can run on your own hardware without mortgaging the office. Scoring within 1 point of a trillion-parameter model on GPQA Diamond while running on a single GPU is an engineering achievement that changes the accessibility equation. For startups, research labs, and individual developers who need strong reasoning without API dependency, Qwen is the practical choice. Apache 2.0 licensing removes every commercial friction point. See the Qwen3.5-122B-A10B model card and our Qwen3.5-122B-A10B vs DeepSeek V3.2 comparison for additional context.

The bottom line: K2.5 is objectively better on every benchmark. Qwen is objectively better on every practical metric - cost, hardware requirements, licensing, and self-hosting feasibility. The right choice depends on whether your constraint is capability or infrastructure. For the full landscape, see our open-source LLM leaderboard.

Sources

• Kimi K2.5 Technical Report - Moonshot AI
• Qwen3.5-122B-A10B - Hugging Face
• Qwen3.5 Official Blog
• Moonshot AI Platform - API Pricing
• LLM Benchmarks 2026 - llm-stats.com
• SWE-bench Verified Leaderboard