Kimi K2.5 vs Llama 4 Maverick: The Open MoE Heavyweights Go Head to Head

Two open-weight Mixture-of-Experts models, two very different philosophies. Kimi K2.5 is Moonshot AI's 1-trillion-parameter colossus with 384 experts, 32 billion active per token, a dedicated vision encoder, and an Agent Swarm system that can orchestrate up to 100 sub-agents. Llama 4 Maverick is Meta's 400-billion-parameter model with 128 experts, 17 billion active per token, a 1-million-token context window, and an API price that undercuts almost everything on the market.

The benchmarks are not close. K2.5 scores 96.1 on AIME 2025, 87.6 on GPQA Diamond, 76.8% on SWE-bench Verified. Maverick does not publish comparable numbers on most of these, but it beats GPT-4o on Chatbot Arena and costs a third of what K2.5 charges per token. These are not the same class of model. But they are both open, both MoE, and both competing for the same developer mindshare.

The real question is whether you need K2.5's raw benchmark dominance - or whether Maverick's combination of low cost, enormous context, and Meta-backed ecosystem is the smarter bet for your actual workload.

Quick Comparison

Kimi K2.5: The Benchmark Machine

Kimi K2.5 is the largest open-weight model to ship benchmarks this strong. The numbers speak plainly: 96.1 on AIME 2025 puts it in the same tier as the best reasoning models from OpenAI and Google. HMMT 95.4 confirms this is not a one-benchmark fluke. GPQA Diamond 87.6 means it handles graduate-level scientific reasoning better than most proprietary models. SWE-bench Verified 76.8% and LiveCodeBench v6 85.0 demonstrate serious software engineering chops.

The architecture is massive - 384 experts with 8 active per token across 61 layers. This gives the model enormous capacity to specialize, with each forward pass selecting a small fraction of the total parameter space. The result is a model that can be simultaneously excellent at math, code, vision, and language without the compromises you typically see when a single dense model tries to do everything.

The vision system is particularly noteworthy. MoonViT-3D is a 400-million-parameter encoder that handles native-resolution images and video. This is not a bolted-on CLIP adapter - it is a purpose-built multimodal frontend. On MMMU-Pro, K2.5 scores 78.5, and OCRBench hits 92.3. If your pipeline involves document understanding, chart reading, or visual reasoning, this matters.

Then there is Agent Swarm. Trained with PARL (a reinforcement learning method for multi-agent coordination), K2.5 can spawn up to 100 sub-agents for complex tasks. On BrowseComp, this pushes accuracy from 60.6% in single-agent mode to 78.4% with the swarm. OSWorld 63.3 and WebArena 58.9 confirm strong agentic performance across desktop and web environments. For background on how agent systems work, see our guide to AI agents.

The downside is cost and accessibility. At $0.60/$3.00 per million tokens on Moonshot's API, K2.5 is 3x more expensive than Maverick on input and 5x more on output. Self-hosting a trillion-parameter model requires serious infrastructure - you are looking at multi-node setups with hundreds of gigabytes of VRAM.

Llama 4 Maverick: The Ecosystem Play

Maverick takes the opposite approach. Instead of maximizing raw capability, Meta optimized for the sweet spot of cost, context, and practical utility. At 400B total parameters with 17B active, Maverick is less than half the size of K2.5 and activates roughly half the parameters per token. The result is dramatically cheaper inference.

At $0.20 per million input tokens and $0.60 per million output tokens on DeepInfra, Maverick is one of the most affordable frontier-class models available. You can process 5 million input tokens for a dollar. For high-volume applications - chatbots, content pipelines, document processing - the economics are compelling. For a broader look at pricing across providers, check our free AI inference providers guide.

The 1-million-token context window is Maverick's standout feature against K2.5. That is 4x longer than K2.5's 256K. For applications that need to process entire codebases, long legal documents, or extended conversation histories, this is a material advantage. See our long-context benchmarks leaderboard for how this compares across the field.

Maverick is also multimodal, handling both text and images. And its conversational quality is strong enough to beat GPT-4o on Chatbot Arena, which measures real human preference in blind comparisons. This is not a model that feels like a budget option in everyday use.

The Meta ecosystem is a genuine differentiator. Llama models have the largest open-source community, the broadest tooling support, and the most third-party hosting options. Quantized Maverick variants are available across dozens of providers. If you need to deploy quickly with minimal friction, the ecosystem advantage is real.

Where Maverick falls short is at the top end of reasoning and coding benchmarks. It does not publish AIME, GPQA, or SWE-bench numbers that compete with K2.5. For tasks that demand elite mathematical reasoning or complex multi-step coding, K2.5 operates in a different tier. For more on Maverick's strengths in practice, see our Llama 4 Maverick review.

Benchmark Comparison

The benchmark picture is lopsided because K2.5 publishes extensively and Maverick does not compete on the same tests. But the absence of published scores is itself informative - Meta is positioning Maverick as a practical, well-rounded model rather than a benchmark chaser. The Chatbot Arena ranking against GPT-4o suggests Maverick's real-world conversational quality is strong, even if it cannot match K2.5 on structured reasoning tasks.

Pricing Analysis

Maverick is 3x cheaper on input and 5x cheaper on output. For a typical workload with a 1:1 input-output ratio at 1 million tokens each, Maverick costs roughly $0.80 versus K2.5's $3.60. That is a 4.5x cost difference. At scale, this compounds fast. For current pricing comparisons across all major models, see our cost efficiency leaderboard.

Kimi K2.5: Pros and Cons

Pros:

• AIME 96.1, GPQA Diamond 87.6 - frontier-class reasoning benchmarks
• Agent Swarm with up to 100 sub-agents for complex agentic tasks
• MoonViT-3D vision handles native-resolution images and video
• SWE-bench 76.8% and LiveCodeBench 85.0 - elite coding performance
• 384-expert MoE architecture with enormous specialization capacity
• Terminal Bench 2.0 score of 50.8 shows strong real-world tool use

Cons:

• 3-5x more expensive per token than Maverick
• 1T parameters makes self-hosting impractical for most teams
• 256K context is 4x shorter than Maverick's 1M
• Modified MIT license has more restrictions than standard open source
• Smaller ecosystem and fewer third-party hosting options
• Moonshot API is the primary access point with limited alternatives

Llama 4 Maverick: Pros and Cons

Pros:

• $0.20/$0.60 per million tokens - among the cheapest frontier models
• 1M context window handles massive documents and long conversations
• Beats GPT-4o on Chatbot Arena for conversational quality
• Largest open-source ecosystem with broad tooling and hosting support
• 400B/17B MoE is more accessible for self-hosting than K2.5
• Multimodal with image understanding built in

Cons:

• Does not publish competitive scores on AIME, GPQA, or SWE-bench
• No agentic capabilities comparable to K2.5's Agent Swarm
• Reasoning ceiling is lower than K2.5 for complex math and coding
• Llama license has specific restrictions compared to MIT or Apache 2.0
• Vision capabilities are less specialized than MoonViT-3D
• 17B active parameters limits maximum per-token intelligence

Verdict

Choose Kimi K2.5 if your workload demands the highest possible accuracy on mathematical reasoning, graduate-level science, or complex software engineering. If you are building agentic systems that need to coordinate multiple sub-tasks, K2.5's Agent Swarm is unique in the open-weight space. The cost premium is justified when errors are expensive - legal analysis, scientific research, financial modeling - and when multimodal vision quality matters. See the full Kimi K2.5 model card for detailed specifications.

Choose Llama 4 Maverick if you need a cost-effective, well-rounded model for high-volume production workloads. The 1M context window opens use cases that K2.5 simply cannot handle at 256K. If your application is conversational AI, content generation, document summarization, or any task where good-enough reasoning at low cost beats perfect reasoning at high cost, Maverick is the practical choice. The Meta ecosystem means you will never struggle to find hosting, tooling, or community support.

The bottom line: These models are not really competing for the same slot. K2.5 is a capability maximizer - you use it when you need the best answer regardless of cost. Maverick is an efficiency maximizer - you use it when you need a good answer at the lowest possible cost with the longest possible context. For a wider view of how open MoE models compare, see our open-source LLM leaderboard.

Sources

• Kimi K2.5 Technical Report - Moonshot AI
• Llama 4 Maverick - Meta AI
• Llama 4 Maverick - DeepInfra
• Moonshot AI Platform - API Pricing
• LLM Benchmarks 2026 - llm-stats.com
• Chatbot Arena Leaderboard - LMSYS