Kimi K2.5 vs Llama 4 Scout: Benchmark King Meets Context King

This comparison is less about which model is "better" and more about which dimension of capability you actually need. Kimi K2.5 is a 1-trillion-parameter MoE model that posts some of the strongest benchmarks in the open-weight space - AIME 2025 at 96.1, GPQA Diamond at 87.6, SWE-bench Verified at 76.8%. Llama 4 Scout is a 109-billion-parameter MoE model with a 10-million-token context window - the longest of any production model available today.

K2.5 activates 32 billion parameters per token. Scout activates 17 billion. K2.5 costs $0.60 per million input tokens. Scout costs $0.08. K2.5 has 256K context. Scout has 10M. These models are optimized for completely different things, and the right choice depends entirely on whether your bottleneck is intelligence per token or tokens per request.

Quick Comparison

Kimi K2.5: Raw Intelligence at Scale

The case for K2.5 is simple: it is one of the most capable open-weight models ever released, full stop. With 384 experts and 8 active per token across 61 layers, the architecture gives each forward pass access to a carefully selected 32 billion parameters drawn from a 1 trillion-parameter reservoir. This is not brute force - it is selective specialization at massive scale.

The benchmark results confirm the approach works. AIME 2025 at 96.1 puts K2.5 in the same conversation as the best reasoning models from any provider. HMMT 95.4 reinforces that this math performance is consistent, not cherry-picked. GPQA Diamond 87.6 shows the reasoning transfers to graduate-level science. SWE-bench Verified 76.8% and LiveCodeBench v6 85.0 demonstrate that K2.5 can actually write and debug real software, not just solve textbook problems.

The multimodal capabilities add another dimension. MoonViT-3D is a 400-million-parameter vision encoder that processes native-resolution images and video without the resolution downsampling that limits many multimodal models. MMMU-Pro 78.5 and OCRBench 92.3 show this is production-grade visual understanding. For how K2.5's vision capabilities stack up against the field, see our multimodal benchmarks leaderboard.

Agent Swarm is K2.5's most distinctive feature. Using PARL-trained coordination, K2.5 can decompose complex tasks across up to 100 sub-agents. On BrowseComp, this pushes accuracy from 60.6% single-agent to 78.4% with the swarm - a 29% relative improvement just from better task decomposition. OSWorld 63.3 and WebArena 58.9 show strong agentic performance on real-world desktop and web tasks. If you are building autonomous agent pipelines, this is a uniquely powerful tool. For more on agent architectures, see our guide to building your first AI agent.

The limitation is context. At 256K tokens, K2.5 handles most documents and codebases comfortably, but it cannot touch Scout's 10M window. If your use case requires ingesting millions of tokens in a single pass, K2.5 is physically incapable of doing the job.

Llama 4 Scout: The Context Window That Changed the Rules

Scout's 10-million-token context window is not an incremental improvement. It is a category-defining feature. At 10M tokens, you can feed Scout an entire large codebase, a full-length novel series, months of conversation history, or hundreds of documents in a single prompt. No other production model comes close. K2.5's 256K is 39x smaller. For detailed rankings, see our long-context benchmarks leaderboard.

The architecture enables this through a relatively compact MoE design - 109B total parameters with just 16 experts and 17B active per token. This is small enough that the per-token compute cost stays low even when processing millions of tokens. At $0.08 per million input tokens on DeepInfra, Scout is 7.5x cheaper than K2.5 on input. Processing a million tokens costs eight cents. Processing K2.5's full 256K context would cost about $0.15 on Scout versus $0.15 on K2.5, but Scout can keep going for another 9.7 million tokens.

Scout is also multimodal, handling both text and images. It inherits the Llama 4 architecture's design for practical, everyday tasks rather than benchmark-topping performance. Meta positioned Scout as the model for applications where broad coverage and massive context matter more than peak reasoning accuracy.

The ecosystem advantage is significant. As a Llama model, Scout benefits from Meta's enormous open-source community. It is available on dozens of hosting platforms, has extensive quantization options, and integrates with virtually every LLM framework. Deploying Scout into production has minimal friction compared to a less common model like K2.5.

Where Scout clearly falls behind is raw reasoning capability. It does not publish scores on AIME, GPQA Diamond, SWE-bench, or LiveCodeBench that would compete with K2.5. For tasks that require precise mathematical reasoning, complex multi-step coding, or expert-level scientific analysis, Scout is not in the same tier. It is designed for breadth and volume, not peak intelligence per token.

Benchmark Comparison

The benchmark comparison is heavily one-sided because these models target different objectives. K2.5 is optimized for maximum accuracy on the hardest reasoning tasks. Scout is optimized for maximum context at minimum cost. Comparing their AIME scores would be like comparing a sports car's lap time to a cargo truck's payload capacity - technically possible but missing the point.

Pricing Analysis

The pricing gap is dramatic. Scout is 7.5x cheaper on input tokens and 10x cheaper on output tokens. For workloads that process large volumes of text - document analysis, log parsing, codebase comprehension - Scout's economics are hard to argue with. Even at its full 10M context, filling the window costs only $0.80 on input. K2.5 cannot even accept that much text, and filling its 256K window costs $0.15 - comparable in absolute terms but for 39x less context. For the latest pricing data across all major models, check our cost efficiency leaderboard.

Kimi K2.5: Pros and Cons

Pros:

• AIME 96.1, GPQA Diamond 87.6, SWE-bench 76.8% - top-tier reasoning
• Agent Swarm coordinates up to 100 sub-agents for complex tasks
• MoonViT-3D provides native-resolution image and video understanding
• LiveCodeBench 85.0 and Terminal Bench 50.8 show real coding strength
• 384-expert architecture enables deep specialization
• BrowseComp 78.4% with Agent Swarm demonstrates web-scale task solving

Cons:

• 256K context is 39x shorter than Scout's 10M
• 7.5x more expensive on input, 10x on output compared to Scout
• 1T total parameters makes self-hosting impractical
• Modified MIT license has more restrictions than Llama's open terms
• Overkill for simple tasks where Scout would suffice at a fraction of the cost
• Limited third-party hosting options compared to the Llama ecosystem

Llama 4 Scout: Pros and Cons

Pros:

• 10M token context window - the longest available in any production model
• $0.08/$0.30 per million tokens - among the cheapest API options anywhere
• 109B/17B MoE is compact enough for practical self-hosting
• Multimodal with text and image support
• Meta's ecosystem provides broad tooling and hosting options
• Ideal for document-heavy, retrieval, and long-session workloads

Cons:

• Reasoning capability is far below K2.5 on math, science, and coding
• No agentic capabilities or multi-agent coordination
• 16 experts limits architectural specialization compared to K2.5's 384
• Vision capabilities are less sophisticated than MoonViT-3D
• Not suitable for tasks requiring elite accuracy on hard problems
• Performance may degrade on very long contexts in practice

Verdict

Choose Kimi K2.5 if your tasks require the highest possible reasoning accuracy - mathematical proofs, scientific analysis, complex software engineering, or agentic workflows. K2.5 is for workloads where getting the right answer matters more than processing speed or cost. If you are building systems that need to solve hard problems, not just process large volumes of text, K2.5 is in a class that Scout does not reach. Full specs at the Kimi K2.5 model card.

Choose Llama 4 Scout if your primary constraint is context length or cost. No other model lets you ingest 10 million tokens in a single request at eight cents per million. For codebase analysis, legal document review, long-running conversation agents, or any pipeline where you need to see everything at once, Scout is uniquely capable. The reasoning ceiling is lower, but for many real-world applications, you do not need AIME-96 math - you need to read the whole document. See the Llama 4 Scout model card for full details.

The bottom line: This is not a competition between better and worse. It is a choice between maximum intelligence per token and maximum tokens per dollar. For a broader view of where both models land in the current landscape, see our overall LLM rankings and our understanding AI benchmarks guide.

Sources

• Kimi K2.5 Technical Report - Moonshot AI
• Llama 4 Scout - Meta AI
• Llama 4 Scout - DeepInfra
• Moonshot AI Platform - API Pricing
• LLM Benchmarks 2026 - llm-stats.com
• Long-Context LLM Evaluation - Papers With Code