The long-context race is on. Two open-weight models released in 2025 are pushing the limits of what a single inference call can hold: Kimi K3 from Moonshot AI and LLaMA 4 Scout from Meta. Both support million-token-plus context windows, both run on accessible hardware (or cheap APIs), and both are free to download. But they make very different trade-offs.
If you are choosing between them for a production project, you need more than benchmark headlines. This guide breaks down context length, cost, speed, multimodal capabilities, benchmark scores, and real-world fit — so you can make the right call for your use case.
Quick Specs: Side-by-Side Comparison
| Feature | Kimi K3 | LLaMA 4 Scout |
|---|---|---|
| Vendor | Moonshot AI | Meta |
| Release | July 2025 | April 2025 |
| Architecture | MoE (larger active params) | 17B active / ~272B total MoE |
| Context Window | 1,000,000 tokens (1M) | 10,000,000 tokens (10M) |
| Multimodal | Text + images | Text + images |
| Thinking Mode | Yes (extended reasoning) | No |
| Open Weights | Yes | Yes (Llama 4 Community License) |
| API Input Price | $0.30 / M tokens | $0.11 / M (Groq), $0.18 / M (Together AI) |
| API Output Price | $1.10 / M tokens | Varies by provider |
| MMLU | Not published at writing | ~79% |
| Chinese Language | Excellent | Limited |
| Self-Host Requirements | MoE, moderate GPU footprint | 2x A100 80GB (full); 1x A100 (quantized) |
The Pain Point: Context Window vs. Reasoning Quality
Long-context models solve a specific bottleneck: the ability to load entire codebases, legal documents, or research corpora into a single prompt without chunking. But raw context length and reasoning quality are not the same thing. This is where Kimi K3 and LLaMA 4 Scout diverge sharply.
LLaMA 4 Scout set a world record at release with a 10,000,000-token context window — ten times larger than Kimi K3's 1M. For pure retrieval tasks — "find the clause that mentions indemnity in this 5,000-page contract repository" or "locate the function that initializes the database connection across 200 source files" — Scout's context advantage is decisive. Nothing else comes close at this price point.
But long context does not equal deep reasoning. Scout's architecture prioritizes breadth: it runs only 17B active parameters from a 16-expert MoE pool. That keeps inference fast and cheap, but it means Scout performs at a "solid, not exceptional" level on complex multi-step tasks. Its MMLU sits at approximately 79%, which is competitive for its active parameter count but below frontier reasoning models.
Kimi K3 takes the opposite approach. It has a smaller but still substantial 1M context, and it compensates with a larger active parameter count and a dedicated thinking mode — an extended reasoning chain similar to what OpenAI o1 or DeepSeek-R1 introduced. For tasks that require logical inference across many steps (math proofs, code debugging, structured analysis), Kimi K3's thinking mode produces noticeably higher-quality outputs than models that simply generate the next token.
The pain point for most developers: you rarely need all 10M tokens. Typical enterprise documents run 50–200K tokens. For that range, the 1M window is more than sufficient, and you benefit more from Kimi K3's reasoning depth than from Scout's headroom.
Pricing and Speed
Cost is often the deciding factor in production, especially at scale.
LLaMA 4 Scout is aggressively priced. At $0.11/M tokens on Groq and $0.18/M on Together AI, it is one of the cheapest capable models available via managed APIs. Groq's inference chips make Scout exceptionally fast — latency is typically sub-second for short completions. For high-throughput pipelines where you send thousands of requests per day, the cost difference over Kimi K3 is substantial.
Kimi K3 costs $0.30/M input and $1.10/M output. That is roughly 3–6x more expensive than Scout depending on the provider you compare against. The output premium reflects Kimi K3's larger active parameters and the overhead of thinking-mode chain-of-thought generation. If you are comparing on a per-output-token basis, the gap widens further.
Verdict on cost: Scout wins decisively for cost-sensitive workloads. Kimi K3's higher pricing is justified when the task benefits from deep reasoning or requires strong Chinese language support — otherwise, Scout is the more economical choice.
Multimodal Capabilities
Both models accept image inputs alongside text, making them true multimodal systems:
- LLaMA 4 Scout was designed as a multimodal-native architecture from the ground up. It handles interleaved text and images with solid performance on vision benchmarks.
- Kimi K3 also supports vision inputs, with competitive image understanding capabilities. For Chinese-language documents with embedded images (scanned forms, charts in presentations), Kimi K3's combined Chinese text + vision capability is a meaningful advantage.
Neither model generates images — both are input-only for vision. If you need image generation in your pipeline, you will need a separate model.
Benchmark Scores in Context
Benchmarks are imperfect proxies, but they provide consistent reference points.
LLaMA 4 Scout's MMLU of ~79% is solid for a model with only 17B active parameters. It matches or exceeds models twice its active size from 2023–2024. Scout performs reasonably well on coding tasks, though it sits below Meta's larger LLaMA 4 Maverick variant.
Kimi K3's published benchmarks were not fully available at the time of writing. However, its MoE architecture with larger active parameters and thinking mode suggests it operates in a higher reasoning tier than Scout for multi-step tasks. Moonshot AI positions Kimi K3 as a direct competitor to frontier reasoning models, not as a lightweight retrieval tool.
For developers evaluating the two models, the most useful comparison is task-specific testing on your actual data, not aggregate benchmarks. A model that scores 5 points higher on MMLU may perform worse on your specific domain if its training distribution does not match your use case.
The Competitive Differentiator: Where Each Model Truly Shines
LLaMA 4 Scout's Differentiator: Extreme Context at Minimal Cost
Scout's 10M context window is genuinely unprecedented. At release, it was the longest context window of any openly available model by a factor of ten. For applications that must load entire archives, databases, or multi-book corpora in a single prompt, Scout is in a category of one.
Practical use cases where Scout's context advantage matters:
- Legal discovery across thousands of documents
- Codebase-wide refactoring analysis (large monorepos)
- Scientific literature synthesis across entire journals
- Log analysis for long-running production systems
Combined with Scout's low price and fast inference on Groq, it is an easy default for retrieval-heavy pipelines that don't need deep reasoning per query.
Kimi K3's Differentiator: Reasoning Depth and Chinese Language
Kimi K3's competitive advantage is two-fold:
1. Thinking mode. Extended chain-of-thought reasoning means Kimi K3 can tackle problems that require multiple reasoning steps without needing external orchestration or prompt chaining. For complex coding tasks, mathematical derivations, or structured document analysis, this is a significant quality lift.
2. Chinese language. Moonshot AI is a Beijing-based company, and Kimi K3 is significantly stronger than LLaMA 4 Scout on Chinese-language tasks. For any product targeting Chinese users, processing Chinese documents, or requiring bilingual (Chinese/English) output, Kimi K3 is the clear choice. Scout's Chinese performance is functional but noticeably weaker on nuanced text.
Self-Hosting Considerations
Both models are open-weight and can be run on-premises, which matters for data-sensitive applications.
LLaMA 4 Scout requires 2x A100 80GB GPUs for full-precision inference, or a single A100 80GB for quantized versions. The 17B active parameter count means the model is relatively memory-efficient at inference time despite its large total parameter pool.
Kimi K3 has a larger active parameter footprint, which means higher VRAM requirements for self-hosting. Exact hardware requirements were not fully documented at the time of writing — check Moonshot AI's HuggingFace repository for up-to-date quantization options.
How GLM 5.2 Fits Into This Picture
If you need a model that combines strong performance across all these dimensions — context, reasoning, multimodal, and pricing — it is worth comparing both Kimi K3 and Scout against GLM 5.2 (glm-4-plus) from Zhipu AI.
GLM 5.2 is a 753B total / 40B active MoE model with a 1M context window, 158 tokens/second throughput, vision support, and strong benchmark scores: 89% on GPQA Diamond and 62.1% on SWE-bench Pro. It is available under an MIT open license, costs $1.40/M input and $4.40/M output via the Zhipu API, and uses an OpenAI-compatible endpoint at https://open.bigmodel.cn/api/paas/v4/.
For teams that want enterprise-grade reasoning and coding capability — without the cost of GPT-4o or Claude Sonnet — GLM 5.2 occupies a distinct tier above both Kimi K3 and Scout. You can explore the full GLM 5.2 API capabilities and integration guide at glm5.app/blog/glm-5-2-api.
Decision Framework: Which Model Should You Choose?
Use this quick guide to match your project requirements to the right model:
Choose LLaMA 4 Scout if:
- You need context beyond 1M tokens (only Scout can do it)
- Cost is the primary constraint and quality requirements are moderate
- You need fast, high-throughput inference via Groq
- Your task is retrieval-dominant (find, extract, summarize) rather than reasoning-dominant
Choose Kimi K3 if:
- Your tasks require multi-step reasoning or mathematical derivation
- You are processing Chinese-language content or serving Chinese-speaking users
- You want a thinking mode without paying frontier model prices
- 1M context is sufficient (it is, for most real-world documents)
Consider GLM 5.2 if:
- You need the highest-quality reasoning in the open-weight tier
- Coding accuracy (SWE-bench 62.1%) or scientific reasoning (GPQA 89%) is critical
- You want an MIT-licensed model with a mature, OpenAI-compatible API
Developers integrating any of these models into production stacks should run evaluation suites on their specific task types before committing. Benchmark scores generalize; your data does not.
Try It Now
If you want to experiment with GLM 5.2 alongside the models compared in this article, glm5.app provides a clean interface for testing prompts, comparing outputs, and exploring the API — no setup required.
Sources
- Moonshot AI / Kimi K3 official announcement: https://kimi.ai
- Meta LLaMA 4 Scout model card: https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E
- Meta LLaMA 4 release blog: https://ai.meta.com/blog/llama-4-multimodal-intelligence/
- Zhipu AI GLM-4-Plus model page: https://open.bigmodel.cn/dev/api/normal-model/glm-4
- GLM 5.2 on HuggingFace: https://huggingface.co/THUDM/glm-4
- Artificial Analysis benchmark (GLM-4-Plus): https://artificialanalysis.ai/models/glm-4-plus
- Groq LLaMA 4 Scout pricing: https://groq.com/pricing/
- Together AI model catalog: https://www.together.ai/models