The transition from late 2025 into early 2026 has marked a definitive inflection point in the trajectory of artificial intelligence, characterized by a fundamental decoupling of reasoning capability from pure model scale. If the preceding years were defined by a monolithic race for higher parameter counts, 2025 became the year of specialized reasoning, architectural efficiency, and the democratization of frontier intelligence through open-weight distillation. The landscape has shifted from simple chatbots to complex “System 2” reasoning agents, driven by an intensifying rivalry between proprietary giants and an explosive open-source ecosystem that has successfully cloned proprietary performance on consumer-grade hardware.

This transformation was catalyzed by the “Code Red” internal directives issued by major laboratories—most notably OpenAI—in December 2025.¹ This strategic alarm was precipitated by the simultaneous maturation of three distinct market forces: Google’s aggressive multimodal dominance with Gemini 3, Anthropic’s capture of the software engineering vertical with Claude Opus 4.5, and the “DeepSeek Moment,” where Chinese open-source innovation broke the correlation between training cost and reasoning performance.¹ The result is a fractured yet highly competitive ecosystem where the gap between a monthly subscription service and a locally hosted model on an NVIDIA RTX 4090 has narrowed to a margin of weeks.²

For the enterprise strategist, the developer, and the research scientist, the implications are profound. We are witnessing the release of models like Llama 4 (Scout and Maverick) and GPT-OSS, which bring Mixture-of-Experts (MoE) architectures—previously the domain of massive data centers—to local workstations. The focus has moved beyond “vibe checks” to rigorous benchmarks in agentic coding, mathematical proofs, and infinite-context retrieval.

This report provides an exhaustive analysis of this new ecosystem. It dissects the architectural breakthroughs of the leading proprietary models, evaluates the explosion of high-performance open models, and offers a definitive technical guide for deploying these systems on local hardware. We analyze the shift from “chatbots” to “reasoning agents,” the economics of inference, and the hardware realities of running 100B+ parameter models locally.

Big Players

The proprietary sector remains the proving ground for the absolute upper limits of AI capability. As of early 2026, the market has segmented into three distinct philosophies represented by OpenAI, Google, and Anthropic. Each player has carved out a specific niche—Reasoning, Engineering, and Context—forcing users to adopt a multi-model workflow rather than relying on a single “God model.”

OpenAI: GPT-5.2 and the “Thinking” Paradigm

OpenAI’s response to intensifying competition has been a bifurcation of its product line into high-speed generalist models and specialized reasoning engines. GPT-5.2, released in late 2025 following the “Code Red” initiative, represents the culmination of this strategy.¹

Architectural Philosophy: Structure and Safety

GPT-5.2 is not merely a larger GPT-4; it is a fundamental re-architecture designed to prioritize “professional knowledge work” and abstract reasoning.¹ Unlike its predecessors, which often prioritized fluency and conversational versatility, GPT-5.2 focuses on structured output and hallucination reduction. Internal benchmarks suggest the model has achieved a 30% reduction in factual errors compared to GPT-4o, a critical improvement for enterprise adoption where reliability is paramount.¹

The model’s standout feature is its domination in abstract reasoning benchmarks, specifically those that resist rote memorization. On the ARC-AGI-2 benchmark—a notorious test for novel problem-solving—GPT-5.2 scored 52.9%, a significant lead over competing frontier models like Claude Opus 4.5 (37.6%) and Gemini 3 Pro (31.1%).³ This performance differential suggests that OpenAI has successfully integrated the “chain-of-thought” (CoT) training methodologies—pioneered in their “o” series (o1, o3)—directly into the base pre-training and post-training of their flagship model. By internalizing these reasoning steps, the model can navigate complex, multi-step logical problems without requiring explicit user prompting for step-by-step analysis.

The “Thinking” Models: o3-mini and o4

Complementing the mainline GPT series are the reasoning-specific models, o3-mini and o4. These models utilize inference-time compute to generate hidden chains of thought before outputting a response. This “System 2” approach allows them to excel in mathematics and complex logic where immediate token prediction fails.

Benchmarks indicate that GPT-5.2 (likely incorporating these techniques) achieves a perfect 100% on the AIME 2025 (American Invitational Mathematics Examination).³ This perfect score on a competition designed for the top high school mathematicians in the US signals that for purely logical, closed-system domains, we may have reached a saturation point in model capability. The implication for researchers is that future benchmarks must evolve beyond static math problems toward open-ended research and novel scientific discovery.

Market Positioning and Pricing

OpenAI has positioned GPT-5.2 as the engine of enterprise productivity. Data suggests the average enterprise user saves 40–60 minutes daily using these tools.¹ However, this capability comes at a price. The standard GPT-5.2 model is priced at $1.75 per million input tokens and $14 per million output tokens.⁴ While cheaper than Anthropic’s flagship, it remains a significant cost center for high-volume applications. The introduction of the “Pro” tier ($21 input/$168 output) for the most complex queries suggests a move toward tiered intelligence, where users pay a premium for “deep thought” versus standard inference.⁵

Anthropic: Claude Opus 4.5 and the Engineering Hegemony

If OpenAI owns “reasoning,” Anthropic has decisively captured “engineering.” Claude Opus 4.5, released in late 2025, is widely regarded as the premier model for software development and complex system architecture.¹

The “Vibe Coding” Champion

The term “vibe coding” emerged in 2025 to describe a workflow where developers rely on an LLM to handle the bulk of implementation details, acting more as architectural reviewers than line-by-line writers. Claude Opus 4.5 excels in this domain due to its high “pass@1” rates on complex refactoring tasks. On SWE-bench Verified, a benchmark derived from real-world GitHub issues, Opus 4.5 achieved a record 80.9%.¹

In head-to-head coding comparisons involving full-stack feature implementation—such as building a “Photoshop clone” or a “Minecraft” simulation—Claude Opus 4.5 consistently produced the most polished, production-ready code.⁵ Unlike competitors that might generate functional but messy code, Opus 4.5 demonstrates an understanding of project structure, maintainability, and UI polish. While it is the most expensive model on the market at $5/M input and $25/M output tokens ⁷, its ability to “one-shot” complex tasks often results in a lower Total Cost of Ownership (TCO) by drastically reducing the debugging loops required by cheaper models.

Extended Thinking Mode

A critical innovation in the Claude 3.7/4.5 lineage is Extended Thinking Mode.⁸ Unlike the opaque reasoning of OpenAI’s o-series (where the thought process is hidden from the user), Claude’s extended thinking is transparent in many implementations, walking through logic step-by-step. This transparency is vital for coding agents and enterprise compliance, allowing developers to debug the model’s logic, not just the code it produces. This feature effectively bridges the gap between the “black box” nature of neural networks and the interpretability required for critical software engineering.

Google: Gemini 3 and the Multimodal Context King

Google’s Gemini 3 (and its variants Pro, Flash, and Flash-Lite) represents the brute-force application of compute infrastructure and proprietary data to solve the context window problem.

Infinite Context and Native Multimodality

Gemini 3’s defining characteristic is its massive context window—ranging from 1 million to 10 million tokens depending on the specific variant and enterprise agreement.¹ This capability allows the model to ingest entire codebases, hour-long videos, or libraries of PDFs in a single prompt, fundamentally changing the architecture of Retrieval Augmented Generation (RAG) systems. With 10 million tokens, the need for complex vector databases and chunking strategies diminishes, replaced by direct “context stuffing.”

Crucially, Gemini is natively multimodal. Unlike GPT-4, which often relies on separate vision encoders stitched together with a language model, Gemini was trained from the start on mixed sequences of text, image, audio, and video. This results in superior performance on benchmarks like MMMU-Pro, where Gemini 3 Pro scores 81.0%, significantly outperforming text-first models that rely on “vision patches”.⁷ This makes Gemini the preferred model for video analysis, audio transcription with context, and analyzing complex visual documents like blueprints.

The Economics of Flash

Google has aggressively weaponized pricing with Gemini 3 Flash. Priced at roughly $0.50 per million input tokens ³, it is significantly cheaper than GPT-5.2 or Claude Opus. Surprisingly, benchmarks show Flash outperforming the “Pro” variant of the previous generation on coding tasks (78.0% on SWE-bench Verified).³ This “race to the bottom” in pricing for high-intelligence models challenges the viability of smaller open-source models for users who are not strictly privacy-constrained, as the API cost becomes negligible for many use cases.

Comparative Summary: The “Big Three”

Feature	GPT-5.2 (OpenAI)	Claude Opus 4.5 (Anthropic)	Gemini 3 Pro (Google)
Primary Strength	Abstract Reasoning, Math, Logic	Software Engineering, Long-form Writing	Multimodality, Context Window, Speed
Benchmark (SWE-bench)	80.0%	80.9%	76.2%
Benchmark (AIME Math)	100%	92.8%	95%
Context Window	128k – 200k	200k	1M – 10M
Pricing Strategy	Premium Enterprise	Ultra-Premium	Aggressive / High-Volume
Best For…	Logic puzzles, STEM research, Corporate data	Production coding, Creative writing	Video analysis, RAG over massive docs

Free LLM Models to Run on Local GPUs : Llama 4, DeepSeek, and GPT-OSS

While proprietary models push the ceiling of capability, open-weight models have fundamentally altered the floor. Late 2025 and early 2026 saw the release of models that allow individuals and companies to own their intelligence infrastructure without relying on API providers. This shift is driven by Mixture-of-Experts (MoE) architectures that decouple model size from inference cost.

Meta’s Llama 4: The MoE Standard

Released in April 2025, Llama 4 marks Meta’s definitive shift away from dense monolithic architectures toward Mixture-of-Experts (MoE).¹⁰ This architectural pivot is driven by the need to scale parameter counts into the trillions while keeping inference costs manageable for the open-source community.

Llama 4 Scout (109B Total / 17B Active)

Llama 4 Scout is the “lightweight genius” of the family, designed to be the ultimate local assistant.¹⁰

• Architecture: It utilizes 16 experts, activating only 17 billion parameters per token. This allows it to run on a single 80GB GPU (like an H100) or, with quantization, on dual consumer GPUs or high-end Mac Studios.¹¹
• Context: A staggering 10 million token context window, democratizing “infinite context” which was previously a Google exclusive.¹⁰
• Capabilities: Scout is natively multimodal (text+image in, text+code out) and multilingual across 12 languages. It achieves 57.2% on GPQA Diamond, making it competitive with proprietary models from early 2025.¹² The ability to process 10M tokens locally changes the paradigm for private document analysis.

Llama 4 Maverick (400B Total / 17B Active)

Llama 4 Maverick is the flagship open model, pushing the boundaries of what is possible outside a closed lab.

• Architecture: A massive 400B parameter model with 128 experts. Crucially, it also maintains only ~17B active parameters per token, identical to Scout.¹⁰ This high ratio of total-to-active parameters (sparsity) allows for immense knowledge storage without slowing down generation speed.
• Performance: Maverick outperforms GPT-4.5 on several benchmarks and rivals Gemini 2.0.¹¹ It requires enterprise-grade hardware (FP8 on H100/Blackwell clusters) to run at full precision, but it represents the new state-of-the-art for open weights. The 128-expert granular routing allows for extreme specialization, making it a formidable coding and multilingual engine.

DeepSeek-V3 and R1: The Efficiency Disrupters

Chinese lab DeepSeek shocked the industry with the release of DeepSeek-V3 and its reasoning variant R1 in early 2025.² Their approach focuses on extreme algorithmic efficiency.

The “DeepSeek Moment”

DeepSeek-V3 utilizes a Multi-head Latent Attention (MLA) architecture and a highly optimized MoE structure (671B total, 37B active).¹³ Its training cost was reported at a remarkably low $5.5 million (2.788M H800 GPU hours), a fraction of the estimated $100M+ for GPT-4 level models.¹³

• MLA Innovation: By compressing the Key-Value (KV) cache into a latent space, DeepSeek-V3 significantly reduces the VRAM required for long-context inference. This architecture allows the massive 671B model to be served on fewer GPUs than comparable dense models.¹⁴
• FP8 Training: DeepSeek pioneered native FP8 (8-bit floating point) training, further accelerating the process and reducing memory bandwidth requirements.¹³

DeepSeek-R1: Open Reasoning

DeepSeek-R1 is the open-source answer to OpenAI’s o1/o3. It uses Group Relative Policy Optimization (GRPO) reinforcement learning to develop “thinking” capabilities without relying on massive supervised fine-tuning datasets.¹³

• Performance: It matches or beats proprietary models on math and coding benchmarks.
• Distillation: DeepSeek released distilled versions of R1 based on Llama and Qwen architectures (ranging from 1.5B to 70B parameters). These distillations allow users to run SOTA reasoning models on laptops, effectively transferring the “reasoning patterns” of the giant model into efficient local packages.¹⁵

Qwen 3: The Dual-Mode Innovator

Alibaba’s Qwen 3 series, released in mid-2025, introduced the concept of “Thinking Mode” directly into the model architecture, creating a versatile tool for both chat and logic.¹⁶

• Hybrid Reasoning: Qwen 3 models can toggle between a fast, heuristic “chat” mode and a slow, deliberative “thinking” mode.² This flexibility makes it ideal for applications that need to switch between casual conversation and complex problem solving without swapping models.
• Qwen3-235B-A22B: A massive MoE model (235B total, 22B active) designed for high-end servers. It excels in multilingual tasks and coding, often outperforming DeepSeek-V3 in pure instruction following.¹⁷
• Qwen3-30B-A3B: A “mid-size” MoE that has become highly popular for local inference. With only ~3B active parameters, it runs blazingly fast while retaining the knowledge base of a 30B model.¹⁸ This specific model is cited by many local LLM enthusiasts as the “sweet spot” for 24GB VRAM cards.

GPT-OSS: OpenAI’s Strategic Pivot

Perhaps the most surprising development of 2025 was OpenAI’s release of GPT-OSS (Open Source Software) models in August 2025.¹⁶

• Motivation: Likely a strategic move to undercut the momentum of Llama and DeepSeek, OpenAI released gpt-oss-120b (MoE) and gpt-oss-20b (dense/MoE hybrid).
• Capabilities: These models are optimized for agentic workflows and tool use. They introduce the Harmony response format, a structured output protocol designed to prevent agents from getting stuck in loops.²⁰
• Performance: The 120B model rivals the proprietary o4-mini in reasoning, while the 20B model is designed to run on high-end consumer hardware (RTX 3090/4090).²¹ The release of an official open-weight model from OpenAI signals a commoditization of the “base layer” of intelligence, shifting value to the application layer.

Technical Deep Dive: Architectures Enabling Local Inference

The ability to run models like Llama 4 Scout (109B) or Qwen 3 (235B) on local systems is not magic; it is the result of specific architectural innovations that decouple model size from inference cost. Understanding these is crucial for selecting the right hardware.

Mixture-of-Experts (MoE)

The dominant trend in 2025-2026 is MoE. In a dense model (like Llama 3 70B), every single parameter is used for every token generated. In an MoE model, the network is divided into “experts” (e.g., a coding expert, a history expert). A “router” determines which experts are needed for the current token.

• Active vs. Total Parameters: This is the critical metric for local inference.

• Llama 4 Scout: 109B Total / 17B Active.
• Implication: You need VRAM to store the 109B parameters (requiring massive memory capacity), but you only need compute power (FLOPS) for 17B parameters (requiring only moderate GPU core speed).¹¹
• The VRAM Bottleneck: This architecture shifts the bottleneck from compute to memory bandwidth and capacity. This explains why Apple’s Unified Memory architecture has become so dominant for local inference; it offers high capacity (up to 192GB) even if the core speed is lower than an NVIDIA H100.

Multi-head Latent Attention (MLA)

Pioneered by DeepSeek and adopted by others, MLA compresses the Key-Value (KV) cache.¹³

• The Problem: Long context windows (128k+) explode VRAM usage because the model must “remember” previous tokens. For a standard model, the KV cache can grow larger than the model weights themselves at long context.
• The Solution: MLA projects the KV heads into a lower-dimensional latent space, reducing the memory footprint of the cache by 50-70%. This is what allows models like DeepSeek-V3 to support 128k context on fewer GPUs, making long-context local RAG feasible.

Native Multimodality (Early Fusion)

Llama 4 and Gemini 3 use Early Fusion. Instead of using a separate vision encoder (like CLIP) that translates images into text tokens, the model processes pixel patches directly as tokens alongside text tokens.²² This results in “reasoning” about images rather than just “describing” them. For local users, this means Llama 4 Scout can analyze charts and diagrams with the same fidelity as text, without the overhead of loading a separate vision model.

Local Inference Guide: Hardware and Software Configuration

Running these models locally requires navigating a complex matrix of hardware capabilities, quantization formats, and software engines. This section provides a definitive guide for 2026 hardware.

The Hardware Tiers

Tier 1: The “Everyman” Enthusiast (24GB VRAM)

• Hardware: Single NVIDIA RTX 3090 or RTX 4090.
• Capabilities: This is the “sweet spot” for entry-level serious inference.²³ It allows for running high-quality quantizations of mid-sized models or low-bit quantizations of large MoEs.

• Recommended Models:

• Qwen 3 30B-A3B: Runs comfortably at 4-bit or 8-bit quantization with high speeds (60+ tokens/s).²⁴ This is arguably the best “bang for buck” model for this tier.
• GPT-OSS-20B: Fits entirely in VRAM with room for context.²⁵ Excellent for agentic tasks.
• Mistral Small 3.2: A dense model that fits easily and offers strong general performance.
• Llama 4 Scout (Heavily Quantized): Can run at very low bitrates (e.g., IQ2_S or Q3_K_M) but will struggle with context length due to VRAM limits.²⁶

Tier 2: The Prosumer / Workstation (48GB – 96GB VRAM)

• Hardware: Dual RTX 3090/4090s (NVLink helpful but not strictly necessary for inference) or single/dual RTX 6000 Ada.
• Capabilities: Can run flagship open models at decent precision. This tier opens up the world of large MoEs.

Recommended Models:

• Llama 4 Scout: Runs at Q4_K_M (approx. 67GB) split across two cards.²⁶ This unlocks the 109B parameter intelligence.
• DeepSeek-V3 (FP8/Int4 hybrid): Can run using techniques that offload experts to system RAM (KTransformers), though performance drops compared to pure VRAM.²⁷
• GPT-OSS-120B: Requires significant quantization (MXFP4) to fit.²⁸

Tier 3: The Unified Memory King (Mac Studio/Ultra)

• Hardware: Apple Silicon M2/M3/M4 Ultra with 128GB or 192GB Unified Memory.
• Capabilities: Apple has cornered the market on local large model inference because system RAM is VRAM.²⁹ While slower in tokens-per-second than NVIDIA, the capacity allows for running models that simply cannot fit on consumer PC hardware.
• Recommended Models:

• Llama 4 Maverick (400B): Can run on a 192GB Mac Studio significantly quantized (e.g., IQ2 or Q2_K), albeit slowly (2-5 tokens/s). This is the only way to run a 400B model on a “desktop.”
• Llama 4 Scout: Runs comfortably at high precision (Q8 or FP16) with massive context windows usable.
• DeepSeek-V3: Runs via MLX or optimized llama.cpp builds.²⁹

The Software Stack

Quantization Formats

• GGUF: The standard for CPU/Apple Silicon inference (via llama.cpp). It allows specific layers to be quantized differently (e.g., keeping “attention” heads at higher precision) to preserve quality. It is essential for Mac users.³⁰
• MXFP4 (Micro-scaling formats): A new format promoted by NVIDIA and supported by GPT-OSS. It allows 4-bit floating point inference with near-FP16 accuracy on Blackwell/Hopper GPUs.¹⁹ This is the future of high-performance local inference on NVIDIA hardware.
• AWQ / EXL2: Best for NVIDIA GPU-only inference. EXL2 offers the fastest speeds but requires the model to fit entirely in VRAM.

Inference Engines

• Ollama: Has updated its engine to support Llama 4’s specific MoE and multimodal architecture.³¹ It abstracts the complexity of GGUF and Modelfiles, making it the easiest entry point.
• llama.cpp: The backend for most local tools. It now supports Harmony format parsing for GPT-OSS ³² and “thinking” tokens for DeepSeek-R1. It is the most versatile engine, supporting everything from Android phones to clusters.
• vLLM: The enterprise choice. Supports high-throughput serving of MoE models like DeepSeek-V3 using FP8.³³ Best for users setting up a local API server for multiple users.
• KTransformers: A specialized engine for DeepSeek models that optimizes the CPU/GPU split for MoEs, allowing users with limited VRAM to run massive models by keeping only active experts on GPU.²⁷

Benchmarking and “Vibe Check”: Use Case Analysis

While benchmarks are useful, “vibes”—the qualitative feel of a model during daily use—often dictate user preference. Here, we analyze which models win in specific real-world scenarios.

Coding Agents

• The Winner: Claude Opus 4.5. Despite the cost, its integration into tools like Claude Code (CLI) and Cursor makes it the default for professionals. It handles large-scale refactors with a “surgical” precision that other models lack.⁶
• The Contender: GPT-5.2 Codex. Excellent for pure algorithmic logic and speed, but sometimes struggles with the “big picture” of a messy codebase compared to Opus.⁴
• The Budget King: Gemini 3 Flash. For bulk tasks like “document this entire repo” or “write unit tests for these 50 files,” Flash is unbeatable due to its speed, context window, and negligible cost.³
• Local Pick: Qwen 3 Coder. The 30B version is a favorite for local development, integrated into IDEs via Ollama.

Reasoning and Logic

• The Winner: DeepSeek-R1 / GPT-5.2. For pure math puzzles, riddles, or complex logic chains, the reinforcement-learning-tuned models dominate. DeepSeek-R1’s ability to “self-correct” during its thinking process (visible in its output traces) makes it highly reliable for STEM tasks.³⁴
• Local Pick: DeepSeek-R1-Distill-Llama-70B. A distilled version that brings “thinking” capabilities to local hardware, often outperforming the original base models in logic tests.

Creative Writing and Roleplay

• The Winner: Claude Sonnet/Opus. Anthropic models retain a distinct “literary” capability, understanding nuance, subtext, and pacing better than the more “robotic” GPT series.
• The Local Winner: Llama 4 Scout. When uncensored or fine-tuned (e.g., “Abliterated” versions), Llama 4’s massive knowledge base and MoE creativity make it a favorite in the r/LocalLLaMA community for roleplay.³⁵ Its large context window allows for long-term narrative consistency.

Strategic Implications and Future Outlook

The release of GPT-OSS and Llama 4 signals a strategic shift in the AI industry. OpenAI and Meta are attempting to commoditize the model layer of the AI stack. By making high-intelligence models free (or open), they force value accrual to shift upwards to the application layer (agents, vertical SaaS) or downwards to the infrastructure layer (chips, cloud).

The Death of the “Wrapper”

With models like Gemini 3 offering 10M context windows, many RAG (Retrieval Augmented Generation) startups are facing obsolescence. You no longer need a complex vector database to search your documents; you simply feed the entire documentation into the prompt. The value of “chunking” and “retrieval” algorithms diminishes as context windows expand.

The Rise of the Local Agent

2026 will be the year of the Local Agent. With Llama 4 Scout running on a local Mac Studio, a developer can have an always-on, privacy-preserving coding companion that knows their entire filesystem, reads their screen (multimodal), and executes terminal commands—all without sending a single byte of data to the cloud.³⁶ This enables highly sensitive workflows (healthcare, defense, finance) to adopt agentic AI.

What’s Next?

• Llama 5: Rumors suggest a move towards “System 2” reasoning baked into the core pre-training, similar to DeepSeek-R1, potentially merging the “Scout” and “Maverick” lines into a unified reasoning engine.
• GPT-6: Likely to focus on “World Models”—understanding physics, cause-and-effect, and video generation as a simulation engine rather than just a text predictor.
• Hardware: The upcoming NVIDIA RTX 5090 (Blackwell consumer card) is expected to feature architectural optimizations (like native FP4 support) that will supercharge local MoE inference, potentially making 200B+ models viable on high-end gaming PCs.²¹

Conclusion

The LLM landscape of early 2026 is defined by choice and specialization. The era of the single “God model” is over. Users must now select the right tool for the specific task at hand.

• For the enterprise demanding software perfection: Claude Opus 4.5.
• For the data hoarder needing infinite context: Gemini 3.
• For the privacy advocate and hacker: Llama 4 Scout or DeepSeek-V3 on local hardware.
• For the efficiency maximizer on consumer GPUs: Qwen 3 or GPT-OSS.

The barrier to entry for “superintelligence” has collapsed. It no longer requires a million-dollar contract; it requires a decent GPU, a GGUF file, and the curiosity to run ollama run llama4:scout.

Detailed Comparison Table of Key Models (Early 2026)

Model Name	Developer	License	Architecture	Context	Best Use Case	Local Hardware Req
GPT-5.2	OpenAI	Proprietary	Dense/MoE Hybrid	200k	Enterprise Logic, STEM	N/A (Cloud)
Claude Opus 4.5	Anthropic	Proprietary	Dense	200k	Coding, Engineering	N/A (Cloud)
Gemini 3 Pro	Google	Proprietary	Multimodal MoE	2M – 10M	RAG, Video Analysis	N/A (Cloud)
Llama 4 Scout	Meta	Community	MoE (109B/17B)	10M	General Local Assistant	2x RTX 3090 / Mac Ultra
Llama 4 Maverick	Meta	Community	MoE (400B/17B)	1M	Research, Distillation	H100 Cluster / High-RAM Mac
DeepSeek-V3	DeepSeek	MIT	MoE (671B/37B)	128k	Coding, Low-Cost Intelligence	Mac Ultra / 4x GPU
Qwen 3 30B	Alibaba	Apache 2.0	MoE (30B/3B)	128k	Local Laptop Inference	RTX 3090 / 4070 Ti
GPT-OSS-20B	OpenAI	Apache 2.0	MoE/Dense	128k	Agentic Tool Use	RTX 3090 / 4090
Grok-3	xAI	Proprietary	Dense	1M+	Real-time Info, “Truth”	N/A (Cloud)

This ecosystem is vibrant, chaotic, and moving at breakneck speed. The winning strategy for any organization or individual is not to bet on a single model, but to build workflows that are model-agnostic, allowing them to swap in the “state of the art” as it changes—which, in 2026, is happening almost every week.