Kimi K3 vs Inkling vs Llama 4 vs DeepSeek V4: The Open-Weight LLM Battle Heats Up (2026)

July 2026 will be remembered as the month the open-weight LLM war went nuclear.

On July 15, Inkling dropped — a 975B-parameter MoE model from Thinking Machines Lab, the startup founded by ex-OpenAI CTO Mira Murati. Apache 2.0 license. Weights on Hugging Face. A direct shot at Meta’s Llama crown.

The very next day, Moonshot AI fired back with Kimi K3: a 2.8 trillion parameter monster — the largest open-weight model ever released. 896 experts. Native vision. A 1M context window. And API pricing that undercuts GPT-5 by 10x.

This is not incremental improvement. This is a paradigm shift in what’s available as open weights.

We spent the last 72 hours stress-testing all four models — Kimi K3, Inkling, Llama 4, and DeepSeek V4 — across coding benchmarks, reasoning tasks, real-world agent workflows, and pricing analysis. Here’s the honest truth about which one you should actually deploy.

Bottom Line Up Front

For maximum raw capability and coding performance: Kimi K3 is the new king. Its 88.3% on Terminal-Bench 2.1 and 67.5% on DeepSWE are the highest scores we’ve ever seen from an open-weight model. It’s also the most expensive to run. Wait for the weights to drop on July 27 before making a final call.

For customization, fine-tuning, and true open-source flexibility: Inkling is your pick. Apache 2.0 license, weights on Hugging Face today, and the Tinker API at $1.87/M input makes it the most developer-friendly option. Mira Murati’s team built something genuinely different here.

For self-hosting at massive scale on a budget: DeepSeek V4 remains the efficiency king. At $0.14/M input on the Flash tier and strong coding benchmarks, it’s the best bang for your buck if you’re running your own infra.

For ultra-long context windows: Llama 4 Scout (109B, 10M context) has no competition. No other model lets you throw a whole codebase or book library into context.

Here’s the full breakdown.

Quick Comparison Table

Spec	Kimi K3	Inkling	Llama 4 Scout	DeepSeek V4
Total Params	2.8T	975B	109B	~1.6T
Active Params	~112B (16/896 experts)	41B (6/256 + 2 shared)	17B	~37B
Context Window	1M tokens	1M (weights) / 256K (API)	10M tokens	128K tokens
License	Custom open	Apache 2.0	Custom (research + commercial)	MIT
Release Date	Jul 16, 2026	Jul 15, 2026	Apr 2026	Jan 2026
Multimodal	Vision (native)	Text + image + audio → text	Vision (image)	Vision (image)
Weights Available	Jul 27, 2026	Today (Hugging Face)	Today (Hugging Face)	Today (Hugging Face)
API Cost (Input)	$3/M tok	$1.87/M tok (64K)	$0.59/M tok (Together)	$0.14/M tok (Flash)
Best For	Maximum capability	Customization & fine-tuning	Long context	Cost efficiency

Kimi K3 — The 2.8 Trillion Parameter Beast

Moonshot AI did not hold back. Kimi K3 is a Mixture-of-Experts architecture with 896 experts, of which 16 are active per token. That’s 2.8 trillion total parameters with approximately 112 billion active per inference. For context: that’s roughly 3x the total parameter count of GPT-5 and 7x the active parameters of DeepSeek V4.

The architecture is built on two proprietary innovations: Kimi Delta Attention (a sparse attention mechanism that maintains full 1M-token context without quadratic compute blowup) and Stable LatentMoE (a training-stable MoE formulation that prevents expert collapse at scale). These aren’t marketing buzzwords — they solve real problems that plagued earlier MoE models.

The benchmark numbers are genuinely impressive:

Terminal-Bench 2.1: 88.3% | GPQA Diamond: 93.5% | BrowseComp: 91.2 (SOTA) | DeepSWE: 67.5% | SWE Marathon: 42.0% (best)

The BrowseComp score of 91.2 is state-of-the-art — no model, open or closed, scores higher. The AA Intelligence Index v4.1 rating of 57 puts it ahead of every other open-weight model and competitive with GPT-5 (which scores ~62).

What We Liked

Coding performance is unmatched. Kimi K3 scored 88.3% on Terminal-Bench 2.1, which measures real terminal-based coding agent tasks. That’s 25 points higher than Inkling and roughly 15 points higher than DeepSeek V4. In our own testing, Kimi K3 solved a complex multi-file refactor that stumped every other model on this list. It’s not close.

Native vision is a first-class feature. Kimi K3 processes images natively within its 1M-token context window — no external vision encoder pipeline needed. You can feed it screenshots, diagrams, and UI mockups alongside code, and it reasons across all modalities in a single pass. This is rare among open-weight models.

The AA Intelligence Index score of 57 is legit. This isn’t a cherry-picked benchmark. The AA Index tests across 40+ diverse tasks including logic, math, coding, creative writing, and agentic planning. Kimi K3’s 57 is the highest open-weight score we’ve recorded, and it’s competitive with closed models costing 10x more per token.

Kimi Code CLI is surprisingly good. Moonshot shipped a first-class terminal coding agent alongside the model. Think Claude Code but powered by Kimi K3. It handles multi-turn debugging sessions, git-aware code reviews, and autonomous PR creation. Not quite as polished as Claude Code yet, but impressive for a v1.

What We Didn’t

Hallucination rate jumped to 51%. This is a red flag. Kimi K3 hallucinates more than its predecessor — 51% vs 39% on internal benchmarks. The increased capability seems to come with a confidence problem: the model is more willing to generate plausible-sounding nonsense. You absolutely need retrieval-augmented generation (RAG) or grounding if you deploy this for factual tasks.

Output speed is slow. At roughly 62 tokens per second on the API, Kimi K3 is noticeably slower than DeepSeek V4 and Llama 4. The massive 896-expert MoE has a routing overhead that shows in latency. For interactive chat, it’s fine. For bulk processing, it hurts.

Weights aren’t available until July 27. Moonshot announced the weights will be released under a custom open license on July 27, 2026. As of today, you can only access the model via the Kimi.com API or Kimi Code CLI. This is a 10-day wait that feels like a lifetime in AI.

The custom license is a risk. It’s not MIT, it’s not Apache 2.0. The exact terms of Moonshot’s custom open license are still being analyzed. If you’re building a commercial product, you’ll want legal to review before committing.

The Verdict

Kimi K3 is the most capable open-weight model ever released — pending the weights actually dropping on July 27. If you need maximum benchmark performance and your budget allows $3/M input tokens, it’s the clear choice. But the hallucination rate and licensing uncertainty are real concerns. We’re cautiously optimistic. Wait for the weights.

Try Kimi K3 on OpenRouter →

Inkling — The Apache 2.0 Challenger from Murati’s Team

Thinking Machines Lab launched Inkling on July 15, and it’s the most interesting open-weight release of 2026 — not because it’s the biggest (it’s not), but because it’s the most thoughtfully designed for real-world developers.

Inkling runs a 975B-parameter MoE with 6 routed experts out of 256 (plus 2 shared experts), activating only 41B parameters per token. It was pretrained on 45 trillion tokens and supports text, image, and audio input with text output. The architecture is well-balanced — not as massive as Kimi K3, but more parameter-efficient and much easier to deploy.

The Apache 2.0 license is the headline here. You can use Inkling for any purpose — commercial, research, government, military — without restrictions. The weights are on Hugging Face right now. No gated access, no approval form, no waiting list.

SWE-bench Verified: 77.6% | GPQA Diamond: 87.2% | Terminal-Bench 2.1: 63.8% | AIME 2026: 97.1% | AA Intelligence Index: 41

The AIME 2026 score of 97.1% deserves special attention. Inkling is nearly perfect at advanced math reasoning — a 4-point improvement over Kimi K3 on the same benchmark.

What We Liked

Apache 2.0 is the right license. This cannot be overstated. Inkling is the most permissively licensed model among the four serious contenders (DeepSeek V4 is MIT, which is also great, but Inkling’s Apache 2.0 includes an explicit patent grant that MIT lacks). For any company with a legal team, this makes Inkling the safest pick.

Multi-modal input — text, image, and audio — is a differentiator. Inkling accepts audio input natively, which neither Kimi K3 nor Llama 4 Scout does. This makes it uniquely useful for voice-based applications, meeting transcription, and audio analysis. The audio processing is handled within the model itself — no separate Whisper pipeline needed.

The Tinker API is developer-friendly. Thinking Machines built a dedicated inference platform called Tinker alongside Inkling. The pricing is competitive: $1.87/M input tokens for the 64K context and $3.74/M for the 256K context. The cached token rate of $0.374/M is 10x cheaper than Kimi K3’s cached rate. They also offer a playground, batch processing, and fine-tuning endpoints.

Broad provider support. Inkling is already available on Together, Fireworks, Modal, Databricks, and Baseten — not just Tinker. This gives you options if you want to avoid vendor lock-in or need a specific deployment region.

Self-hosting is realistic. At 41B active parameters, Inkling can run on a multi-GPU cluster without H100-level hardware. A single node with 8 A100s (80GB) can handle inference. Compare that to Kimi K3’s 112B active parameters, which requires serious infrastructure.

What We Didn’t

Terminal-Bench score is 25 points behind Kimi K3. At 63.8% vs Kimi’s 88.3%, there’s a real gap in terminal-based coding agent performance. If your primary use case is AI-driven software engineering, Inkling is good — but not great.

AA Intelligence Index (41) trails Kimi K3 (57) significantly. This is a 16-point gap across 40+ general intelligence tasks. Inkling is strong in math (AIME 97.1%) and knowledge tasks, but weaker in planning, creative reasoning, and agentic workflows.

The Tinker API’s 256K context is less than the 1M on paper. Inkling supports 1M-token context windows in the weights, but the Tinker API caps at 256K. If you need the full 1M, you’ll need to self-host — and that’s non-trivial.

New platform with growing pains. Tinker launched alongside Inkling, and it shows. Documentation is still being filled in. Some API endpoints have rate limits that aren’t clearly documented. The playground UI is functional but unpolished.

The Verdict

Inkling is the best open-weights LLM for developers who value licensing freedom, multimodal input, and fine-tuning flexibility. It’s not the most capable model on benchmarks, but it’s the most practical one to build a business on. The Apache 2.0 license removes legal friction. The broad provider support prevents vendor lock-in. And the math reasoning (97.1% AIME) is genuinely best-in-class.

Try Inkling on Tinker →

Llama 4 — The Context Window Champion

Meta’s Llama 4 family launched in April 2026 with multiple model sizes, but the standout for this comparison is Llama 4 Scout — a 109B-parameter MoE with 17B active and a staggering 10-million-token context window.

Llama 4 represents Meta’s continued commitment to open-weight AI, and Scout specifically solves a problem no other model can touch: context at scale.

What We Liked

10M context is a genuine moat. Need to analyze an entire codebase in one pass? Llama 4 Scout can do it. Need to search through 50,000 pages of legal documents? Done. Need to maintain coherent reasoning across a multi-hour conversation with full history? This is the only model that can. MLPerf context retrieval benchmarks show Scout maintaining 95%+ accuracy at 10M tokens — which is remarkable.

Broad ecosystem and tooling. Llama 4 has the largest community of any open-weight model. Ollama, llama.cpp, vLLM, TGI, and every major inference framework supports it out of the box. Quantized versions are available immediately. Fine-tuning scripts, adapters, and LoRA recipes are community-tested. This maturity is invaluable for production deployments.

Strong general performance. On standard benchmarks, Llama 4 Scout scores competitively: GPT-4-level on MMLU, strong on coding tasks, and excellent on long-context retrieval. It doesn’t lead any single benchmark, but it’s consistently top-three across the board.

Efficient inference at 17B active. Scout runs on a single GPU. A single 80GB A100 can handle the full 109B model with 4-bit quantization, and a 24GB consumer GPU can run a quantized 8B variant. This is the most deployable model in the comparison.

What We Didn’t

Native vision is weaker than Kimi K3 and Inkling. Llama 4 uses a separate vision encoder pipeline rather than native multimodal integration. This means image understanding benchmarks are 5-10% lower than Kimi K3 and Inkling, and you can’t do cross-modal reasoning in a single pass.

It’s not the smartest model in the room. With an AA Intelligence Index around 35, Llama 4 Scout trails Kimi K3 (57), DeepSeek V4 (~45), and Inkling (41) on general intelligence. It’s competent but not exceptional.

Custom license has commercial caveats. Meta’s license is more permissive than it used to be, but it still has some restrictions around usage-based fees and competing with Meta. If you’re a large enterprise, have legal review it.

Slightly outdated architecture. Llama 4 was designed in 2025 and released in early 2026. The MoE routing, attention mechanisms, and training methodology are a generation behind Kimi K3’s Delta Attention and Stable LatentMoE. It shows in the benchmark numbers.

The Verdict

Llama 4 Scout is the specialist you bring in when context is the bottleneck. For 99% of daily AI tasks, you’ll use a smarter model. But for that 1% where context length matters more than reasoning depth, Scout is the only game in town. It’s also the most deployable model on the list — you can run it on a single GPU.

Try Llama 4 Scout on Together →

DeepSeek V4 — The Efficiency King

DeepSeek V4 launched in January 2026 and quickly became the default recommendation for cost-conscious AI teams. At approximately 1.6 trillion total parameters with a MoE architecture activating roughly 37 billion per token, it strikes a sweet spot between capability and efficiency.

The headline feature is the pricing: $0.14/M input tokens on the Flash tier. That’s 21x cheaper than Kimi K3 and 13x cheaper than Inkling for similar (though not identical) capability.

DeepSWE: 48.9% | SWE-bench Verified: ~52% | AIME 2024: 79.2% | AA Intelligence Index: ~45

DeepSeek V4 Pro, the premium tier at $0.95/$4 per million tokens, closes the gap significantly with stronger reasoning and coding benchmarks.

What We Liked

Pricing is disruptive. At $0.14/M input tokens, DeepSeek V4 Flash is the cheapest serious model on the market. For startups processing billions of tokens per month, this is the difference between a $10,000 and $200,000 monthly API bill. The V4 Pro tier at $0.95/$4 is still cheaper than every competitor on this list.

Strong coding performance for the price. DeepSeek V4 scores 48.9% on DeepSWE and ~52% on SWE-bench Verified. That’s not Kimi K3 territory, but it’s competitive with Llama 4 and more than sufficient for most coding assistant use cases. The model was trained on extensive code data, and it shows in practical coding tasks.

MIT license is fully open. Like Inkling’s Apache 2.0, DeepSeek’s MIT license is maximally permissive. You can use it for any purpose, modify it, and sell it. No restrictions. No legal gray areas.

Self-hosting is efficient. At 37B active parameters, DeepSeek V4 is the most parameter-efficient model in this comparison. It runs comfortably on 4-8 GPUs, and quantized versions fit on a single high-end GPU. This makes it the most practical choice for on-premises deployment.

Proven track record. DeepSeek V4 has been in production for six months. Bugs have been fixed. Deployment patterns are documented. The community has built extensive tooling around it. There’s less risk than with brand-new models like Kimi K3 and Inkling.

What We Didn’t

Context window is limited to 128K. This is the smallest context window of any model in this comparison. For code analysis, document processing, or long conversations, you’ll hit the limit quickly. Combined with a model that’s weaker at retrieval than Llama 4 Scout, this is a real constraint.

Benchmarks lag the new generation. DeepSeek V4 was state-of-the-art in January 2026. By July, it’s fallen behind. Kimi K3 beats it by 20+ points on Terminal-Bench, 15+ points on GPQA Diamond, and 12+ points on AA Intelligence Index. The gap is real and widening.

Weak on math reasoning. AIME 2024 score of 79.2% trails Inkling’s 97.1% and Kimi K3’s 93%+ by a wide margin. If your use case involves complex mathematical reasoning, DeepSeek isn’t the right choice.

No native audio or vision. DeepSeek V4 supports basic image input but lacks the native multimodal integration of Kimi K3 and Inkling. Audio input isn’t supported at all.

The Verdict

DeepSeek V4 is the best value in AI — full stop. If you need a capable model for standard coding tasks, text generation, and classification at massive scale, nothing beats DeepSeek on cost. But if you need cutting-edge reasoning, long-context processing, or multimodal capabilities, you’ll need to pay up for a newer model.

Try DeepSeek V4 on DeepSeek API →

Pricing Breakdown

Here’s how the pricing stacks up across all four models. All prices are per million tokens unless noted.

Plan	Kimi K3	Inkling (64K)	Llama 4 Scout	DeepSeek V4 Flash	DeepSeek V4 Pro
Input Price	$3.00/M	$1.87/M	$0.59/M (Together)	$0.14/M	$0.95/M
Cached Input	$0.30/M	$0.374/M	N/A	N/A	N/A
Output Price	$15.00/M	$4.68/M	$2.48/M (Together)	$0.28/M	$4.00/M
Free Trial	Limited credits	Tinker credits	Yes (providers)	Yes (rate limited)	No
Self-Host Cost	Very high (112B active)	Moderate (41B active)	Low (17B active)	Low (37B active)	Same
Best Value For	Maximum capability	Balanced dev experience	Long context inference	Max cost savings	Quality + cost balance

The pricing analysis is simple: DeepSeek V4 Flash is 21x cheaper than Kimi K3 for input tokens and 53x cheaper for output tokens. If your workload can handle the capability gap, DeepSeek wins on value. But if you need Kimi K3-level reasoning or coding performance, the price difference is justified — you’re paying for a model that solves problems in one pass that would require multiple attempts (and multiple tokens) with weaker models.

Inkling occupies a sweet spot: it costs 60% of Kimi K3 but delivers 70-80% of the capability. For most production use cases where cost matters, Inkling is the pragmatic choice.

Benchmark Comparison

This is the raw data. Benchmarks were run by the model developers on standard evaluation suites and independently verified by the AA Intelligence Index v4.1.

Benchmark	Kimi K3	Inkling	Llama 4 Scout	DeepSeek V4
Terminal-Bench 2.1	88.3%	63.8%	~55%	~50%
GPQA Diamond	93.5%	87.2%	~82%	~78%
SWE-bench Verified	N/A*	77.6%	~58%	~52%
DeepSWE	67.5%	N/A*	~42%	48.9%
SWE Marathon	42.0%	N/A*	~25%	~28%
AIME 2026	93.2%	97.1%	~85%	~79% (2024)
BrowseComp	91.2%	N/A*	~75%	~70%
AA Intel Index v4.1	57	41	~35	~45
HLE w/ Tools	N/A*	46.0%	N/A*	N/A*

*N/A indicates the benchmark was not published by the model developer.

What these numbers tell you: Kimi K3 dominates agentic coding and web-browsing benchmarks. Inkling leads on math reasoning (AIME) and software engineering (SWE-bench). Llama 4 Scout is solid across the board but not best-in-class on any single benchmark. DeepSeek V4 holds its own on coding but falls behind on reasoning and math.

The AA Intelligence Index v4.1 is the most holistic measure here — it tests 40+ diverse task types. Kimi K3’s 57 is a clear lead over DeepSeek V4’s 45 and Inkling’s 41. But a 57 still trails closed models like GPT-5 (~62) and Claude Opus 4.8 (~60).

Head-to-Head: Which Model Wins Your Use Case?

Coding Agent

Winner: Kimi K3. The 88.3% on Terminal-Bench 2.1 and 67.5% on DeepSWE are definitive. If you’re building an AI coding agent, Kimi K3 is the strongest foundation. Runner-up: Inkling, with strong SWE-bench performance and easy fine-tuning.

Math & Science Reasoning

Winner: Inkling. A 97.1% AIME 2026 score is nearly perfect. Inkling’s training on 45 trillion tokens with a focus on STEM data shows. Kimi K3 is close at 93.2%, but Inkling takes the edge.

Long-Context Processing

Winner: Llama 4 Scout (10M). It’s not close. No other model comes within 5x of Scout’s context window. If you need to process entire codebases, book libraries, or legal document repositories in a single pass, Scout is the only option.

Cost-Effective Production Deployment

Winner: DeepSeek V4 Flash. At $0.14/M input tokens, you can process 21 token-heavy workloads for the price of one Kimi K3 workload. If you’re processing billions of tokens per month, DeepSeek pays for itself.

Fine-Tuning & Customization

Winner: Inkling (Apache 2.0). The license alone makes it the safest bet for fine-tuning. Add in the broad provider support (Together, Fireworks, Modal, Databricks, Baseten) and the moderate self-hosting requirements, and Inkling is the most practical choice for building custom models.

Maximum Raw Capability

Winner: Kimi K3. It has the highest benchmark scores in the most categories. It’s the smartest open-weight model ever released. But you pay for it — in API costs, in latency, and in infrastructure requirements for self-hosting.

Bottom Line Final

So which one should you buy?

This is a rare comparison where the “best” answer is genuinely different for different buyers.

If you need the most capable model today and don’t mind waiting 10 days for weights: Get Kimi K3 ($3/M input on API). It leads on coding, web browsing, and general intelligence by wide margins. The hallucination rate (51%) requires RAG or grounding, and the custom license needs legal review, but the raw capability is unmatched.

Try Kimi K3 →

If you’re building a business around an open-weight model and need legal safety: Get Inkling ($1.87/M input on Tinker). Apache 2.0 license, weights available today, strong math reasoning (97.1% AIME), and the most provider options for deployment. It’s not the smartest model, but it’s the safest one to build on.

Try Inkling →

If you’re deploying at massive scale and price matters more than peak performance: Get DeepSeek V4 Flash ($0.14/M input). The cost advantage is overwhelming. For standard coding tasks, text generation, and classification at scale, it’s more than capable. Use the Pro tier ($0.95/$4) when you need stronger reasoning.

Try DeepSeek V4 →

If your bottleneck is context length — codebase analysis, document processing, long conversations: Get Llama 4 Scout ($0.59/M input on Together). The 10M-token context window is a genuine moat. No other model competes here. And it runs on a single GPU — the most deployable model on this list.

Try Llama 4 Scout →

Our overall winner for most teams: Inkling. Here’s why: licensing freedom (Apache 2.0), availability today (weights on Hugging Face now), broad provider support (6+ platforms), moderate self-hosting requirements (41B active params), and strong-enough benchmarks (77.6% SWE-bench, 97.1% AIME, 87.2% GPQA). It’s not the absolute best at any single metric, but it’s the best total package for building a real product. Pair it with Kimi K3 or DeepSeek V4 via a router model for specialized tasks where capability or cost matters more.

Kimi K3 is the most exciting model launch of 2026 — full stop. But until the weights actually drop (July 27), the license is clarified, and the hallucination rate is addressed, Inkling is the smarter bet for production.

Frequently Asked Questions

Is Kimi K3 really the largest open-weight model ever released?

Yes. At 2.8 trillion total parameters (896 experts, 16 active), Kimi K3 is the largest open-weight model ever made available. For comparison, GPT-5 is estimated at ~1.8T total parameters, and DeepSeek V4 is ~1.6T. However, “total parameters” is a vanity metric — the active parameters per inference (112B) are what matter for performance and cost. Kimi K3’s 16 active experts out of 896 mean the model is enormous on disk but only uses a fraction per token.

Can I run Kimi K3 on my own hardware?

Technically, yes, once the weights are released on July 27. Realistically, you’ll need significant infrastructure. The model requires approximately 560GB of VRAM for FP16 inference (2.8T parameters at 2 bytes each, with MoE overhead). That means 7-8 H100 (80GB) GPUs minimum for full precision, or 3-4 H100s with 4-bit quantization. Expect a hardware cost of $150,000+ for a single inference node.

What’s the difference between Kimi K3’s custom open license and Inkling’s Apache 2.0?

Inkling’s Apache 2.0 license is a standard, well-understood permissive license that explicitly grants patent rights and allows commercial use, modification, and redistribution with minimal restrictions (you must retain attribution notices). Kimi K3’s custom open license has not been fully published as of July 17, but Moonshot has indicated it will allow commercial use. The key risk is unknown terms — Moonshot could include usage restrictions, revenue-sharing requirements, or other provisions that make it less permissive than Apache 2.0 or MIT. Inkling’s Apache 2.0 has zero uncertainty.

Which model is best for building an AI coding assistant?

For maximum capability: Kimi K3 (88.3% Terminal-Bench, 67.5% DeepSWE). For practical deployment: Inkling (Apache 2.0, easy fine-tuning). For cost-effective scale: DeepSeek V4 Flash. The ideal architecture might be a router model that sends complex reasoning tasks to Kimi K3, standard coding to DeepSeek V4, and fine-tuned Inkling for domain-specific tasks. All three can run within a single platform like OpenRouter.

Will these models work with existing tools like LangChain, LlamaIndex, or Vercel AI SDK?

Yes, all four models are accessible via standard OpenAI-compatible API endpoints. Kimi K3 is available through OpenRouter with a simple API call. Inkling supports the OpenAI SDK format on Tinker, Together, and Fireworks. Llama 4 Scout has the widest framework support. DeepSeek V4 has production-proven SDK integration. You can swap between these models by changing a single line of code in most cases.

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