February 2026 marks a pivotal moment for local AI. Based on the Genspark global usage leaderboard, we extracted every open-source model that can be installed locally via LM Studio, Ollama, or llama.cpp. Out of the top 20 most-used AI models worldwide, 12 are open-source and locally installable — from Kimi K2.5's 182B monthly tokens to GLM 4.7 Flash's efficient deployment. Chinese models dominate alongside newcomers like DeepSeek V3.2 (MIT), GLM 4.7 (Apache 2.0), and Trinity Large (Arcee AI). Here are the 15 best open-source models you should know about.
Table of Contents
Genspark Global Usage Leaderboard (Feb 2026)
The Genspark leaderboard tracks real monthly token usage across all major AI models. Out of the top 20 models, here are the open-source ones you can run locally:
| Rank | Model | Provider | Monthly Usage | Local? |
|---|---|---|---|---|
| #1 | Kimi K2.5 | Moonshot AI | 182B tokens | ✓ GGUF |
| #2 | Trinity Large Preview | Arcee AI | 114B tokens | ✓ GGUF |
| #3 | Gemini 3 Flash Preview | 110B tokens | ✗ Proprietary | |
| #4-5 | Claude Sonnet 4.5 / Opus 4.5 | Anthropic | 39.6B / 36B | ✗ Proprietary |
| #6 | DeepSeek V3.2 | DeepSeek | 29B tokens | ✓ MIT |
| #7 | MiniMax M2.1 | MiniMax | 23.5B tokens | ✓ Apache 2.0 |
| #9 | Step 3.5 Flash | StepFun | 18.7B tokens | ✓ Open |
| #12 | GLM 4.5 Air | Zhipu AI | 16.3B tokens | ✓ Apache 2.0 |
| #17 | GLM 4.7 | Zhipu AI | 7.75B tokens | ✓ Apache 2.0 |
| #19 | GLM 4.7 Flash | Zhipu AI | 6.35B tokens | ✓ Apache 2.0 |
Key insight: 12 out of 20 top models are open-source and locally installable. Chinese AI companies (Moonshot, DeepSeek, Zhipu, Alibaba, StepFun, MiniMax) hold 8 spots, while Western players (Arcee AI, OpenAI open-weight) bring 2 more. The proprietary-only positions belong to Google (Gemini), Anthropic (Claude), and OpenAI (GPT-5.2). This confirms: open-source AI is winning.
Our Selection Methodology
To establish this ranking, we analyzed each model according to 5 essential criteria:
- Generation quality: Reasoning capabilities, coherence, and creativity
- Code performance: Passkey, HumanEval, and algorithmic problem solving
- Hardware efficiency: Required RAM/VRAM, inference speed
- Multimodality: Vision support, audio, or specializations
- License & accessibility: Freedom of use, GGUF availability
The Top 15 Open-Source LLMs 2026
Kimi K2.5 (32B/1T MoE)
Moonshot AI Best choice 2026The new 2026 champion. 256K context, unmatched reasoning.
MMLU
88.9%
HumanEval
91.2%
VRAM (Q4)
22 GB
License
Model License
Moonshot AI's K2.5 is a game-changer. Despite its massive 1 trillion parameter MoE architecture, only 32B are active at once—making it surprisingly efficient. The 256K context window is unprecedented at this VRAM requirement. Exceptional for long-document analysis, code review, and complex multi-step reasoning tasks. Note: This model is available via API and select partnerships; weights are not fully open-source.
Ideal for: Researchers, document analysis, complex coding projects, enterprises.
Qwen 3 (32B)
Alibaba Reasoning KingNear GPT-4 intelligence locally. Built-in thinking mode.
MMLU
84.7%
HumanEval
88.4%
VRAM (Q4)
20 GB
License
Apache 2.0
Qwen 3 represents the culmination of Alibaba's open-source research. The 32B version offers exceptional reasoning through its built-in chain-of-thought mode, competitive with models twice its size. Its efficiency makes high-end AI accessible to more hardware configurations.
Ideal for: Developers, researchers, complex reasoning tasks, 32GB RAM workstations.
Llama 3.3 70B
Meta VersatileThe industry standard, perfectly balanced.
MMLU
86.5%
HumanEval
88.4%
VRAM (Q4)
42 GB
License
Llama 3.3
Meta continues to refine its recipe with Llama 3.3. This model offers an exceptional balance between performance and accessibility. Its mature ecosystem (LoRA, fine-tuning, optimized quantization) makes it the default choice for many projects.
Ideal for: All uses, personalized fine-tuning, production deployment.
GLM-4 (32B)
Zhipu AI BilingualChinese-English excellence. Llama-70B class performance.
MMLU
83.2%
C-Eval
91.8%
VRAM (Q4)
20 GB
License
Model License*
Zhipu AI's GLM-4 is a hidden gem. Exceptional bilingual performance with strong capabilities in both Chinese and English. The 32B version rivals Llama 70B on many tasks while requiring half the VRAM. A top choice for multilingual applications and Asian markets. *Model License allows research and personal use; commercial use requires contacting Zhipu AI.
Ideal for: Bilingual projects, Asian market applications, 32GB RAM setups.
DeepSeek R1 Distill (32B)
DeepSeek ReasoningPhD-level reasoning locally. Shows its thought process step-by-step.
MMLU
82.1%
Math (AIME)
72.4%
VRAM (Q4)
20 GB
License
MIT
The distilled 32B version of DeepSeek R1 brings PhD-level reasoning to accessible hardware. Unlike other models, it transparently shows its chain-of-thought, making it exceptional for learning and debugging complex problems. The best choice for math, logic, and scientific tasks.
Ideal for: Mathematics, scientific research, logic puzzles, code architecture.
Gemma 3 (27B)
Google MultimodalVision + text understanding. Google's best open model.
MMLU
81.4%
Vision
Yes
VRAM (Q4)
17 GB
License
Gemma
Google's flagship open model understands both text and images natively. Gemma 3 27B offers exceptional visual reasoning—describe images, analyze charts, and discuss visual content. A top choice for multimodal applications on capable hardware.
Ideal for: Vision tasks, image analysis, multimodal chat, content creation.
DeepSeek V3.2 (37B/671B MoE)
DeepSeek Leaderboard #6Massive MoE flagship — 29B monthly tokens. MIT licensed.
Architecture
671B MoE
Active Params
37B
VRAM (Q4)
~40 GB
License
MIT
DeepSeek V3.2 is the evolution of the already impressive V3 line. With 671B total parameters but only 37B active at any time (Mixture of Experts), it delivers frontier-level performance with reasonable VRAM requirements. The MIT license makes it the most permissively licensed flagship model available. Exceptional at coding, reasoning, and long-form generation.
Ideal for: Enterprise deployments, code generation, research, 48GB+ RAM/VRAM setups.
Trinity Large Preview (70B MoE)
Arcee AI Leaderboard #2!The dark horse — 114B monthly tokens, free & open-source.
Monthly Usage
114B tokens
Architecture
MoE ~70B
VRAM (Q4)
~45 GB
License
Apache 2.0
The biggest surprise of 2026. Arcee AI's Trinity Large Preview skyrocketed to #2 on global usage with 114B monthly tokens — second only to Kimi K2.5. This free, open-source MoE model delivers exceptional versatility across coding, reasoning, and conversation. Its rapid adoption proves that great open models can compete with the biggest names.
Ideal for: All-purpose AI, heavy workloads, commercial deployment (Apache 2.0).
MiniMax M2.1 (45B MoE)
MiniMax Leaderboard #8200K context pioneer — 23.5B monthly tokens. Apache 2.0.
Context
200K tokens
Architecture
45B MoE
VRAM (Q4)
~18 GB
License
Apache 2.0
MiniMax M2.1 brings a 200K token context window to the open-source world — rivaling Kimi K2.5's 256K. This MoE architecture delivers strong general performance while remaining efficient on consumer hardware. At 23.5B monthly tokens, it's proven itself as a reliable choice for document analysis and long-context tasks.
Ideal for: Long document processing, RAG pipelines, 24GB+ RAM setups.
Positions 11-15: Essential picks for every budget
GLM 4.7
Zhipu AI - Leaderboard #17, 7.75B tokens. Flagship bilingual.
GLM 4.5 Air
Zhipu AI - Leaderboard #12, 16.3B tokens. Best efficiency.
Step 3.5 Flash
StepFun - Leaderboard #9, 18.7B tokens. Speed champion.
GLM 4.7 Flash
Zhipu AI - Leaderboard #19, 6.35B tokens. Ultra-efficient.
Qwen 3 (14B)
Alibaba - The reasoning sweet spot for 16GB systems. Apache 2.0, built-in thinking mode.
Note: The full DeepSeek R1 671B (MIT license, 404GB VRAM) and Qwen 3 235B MoE (Apache 2.0, 80-96GB VRAM) exist for cluster deployment but are excluded from this consumer-focused ranking. Many more open models (Command R+, WizardLM 2, Mistral Small 24B) remain excellent choices detailed in our configurator.
Quick Comparison Table — Top 15
| # | Model | Params | Q4 VRAM | Leaderboard | Specialty |
|---|---|---|---|---|---|
| 1 | Kimi K2.5 | 32B (1T MoE) | 22 GB | #1 — 182B | Champion |
| 2 | Qwen 3 32B | 32B | 20 GB | — | Reasoning |
| 3 | Llama 3.3 70B | 70B | 42 GB | — | Balanced |
| 4 | GLM-4 32B | 32B | 20 GB | — | Bilingual |
| 5 | DeepSeek R1 32B | 32B | 20 GB | — | Math/Logic |
| 6 | Gemma 3 27B | 27B | 17 GB | — | Vision |
| 7 | DeepSeek V3.2 | 37B (671B MoE) | 40 GB | #6 — 29B | MIT Flagship |
| 8 | Trinity Large | 70B MoE | 45 GB | #2 — 114B | Dark horse |
| 9 | MiniMax M2.1 | 45B MoE | 18 GB | #8 — 23.5B | 200K ctx |
| 10 | GLM 4.7 | 26B | 16 GB | #17 — 7.75B | Bilingual+ |
| 12 | GLM 4.5 Air | 14B | 9 GB | #12 — 16.3B | Efficient |
| 13 | Step 3.5 Flash | 14B | 9.5 GB | #9 — 18.7B | Fast |
| 14 | GLM 4.7 Flash | 9B | 5.5 GB | #19 — 6.35B | Ultra-light |
| 15 | Qwen 3 14B | 14B | 9.5 GB | — | Reasoning |
NEW Rows highlighted in cyan are new additions from the Genspark leaderboard (Feb 2026). Leaderboard column shows global rank and monthly token usage.
How to Choose Your Local LLM?
The choice mainly depends on three factors: your available hardware, your primary use case, and your budget constraints.
Based on your hardware configuration
- MacBook Pro M3 Max (36-48GB): Kimi K2.5, Qwen 3 32B, DeepSeek V3.2 Q4, Trinity Large Q4
- PC Gamer RTX 4090 (24GB): Kimi K2.5 Q4, Qwen 3 32B Q4, MiniMax M2.1, GLM 4.7
- Multi-GPU Workstation (48-96GB): Trinity Large, DeepSeek V3.2, Qwen 3 32B, Llama 3.3 70B
- Standard setup (16GB): Qwen 3 14B Q4, GLM 4.5 Air, Step 3.5 Flash, Phi-4 14B
- Modest setup (8GB): GLM 4.7 Flash, Qwen 3 8B, Gemma 3 4B, Llama 3.3 8B
- Cluster/server (100GB+): Qwen 3 235B MoE, WizardLM 2, Command R+
Based on your usage
- Development & Code: DeepSeek V3.2 (MIT), Kimi K2.5, Qwen 3 32B, Qwen 2.5 Coder
- Writing & Content: Trinity Large, Llama 3.3, GLM 4.7, Qwen 3
- Document analysis (long context): Kimi K2.5 (256K), MiniMax M2.1 (200K), Command R+ (128K)
- Vision & Multimodal: Gemma 3, LLaVA variants, Qwen-VL
- Conversational chatbot: Trinity Large, Qwen 3, Llama 3.3, GLM 4.7
- Mathematics & Sciences: DeepSeek R1 (essential), DeepSeek V3.2, Kimi K2.5, Qwen 3
- Bilingual CN/EN: GLM 4.7, GLM 4.5 Air, Kimi K2.5, Qwen 3, Step 3.5 Flash
- Speed-first (8GB RAM): GLM 4.7 Flash, Qwen 3 8B, Step 3.5 Flash
Based on your cloud budget (API inference)
If you don't run locally but via API, the quality/price ratio changes:
- Best quality/price ratio: DeepSeek V3.2 (MIT), Qwen 3 (Alibaba Cloud), Step 3.5 Flash (free)
- Premium quality: DeepSeek V3.2 API, Kimi K2.5 API, GPT-5.2, Claude Sonnet 4.5
- European alternative: Mistral Large 2 (via Mistral AI)
- Long context specialist: MiniMax M2.1 (200K), Kimi K2.5 (256K), Command R+ (128K)
Conclusion: Open-Source AI Is Winning
February 2026 is a landmark moment: 12 out of the top 20 most-used AI models globally are open-source. The Genspark leaderboard confirms what enthusiasts always suspected — open models are not just catching up, they're leading. Chinese AI companies (Moonshot, DeepSeek, Zhipu, Alibaba, StepFun, MiniMax) now hold 8 of those 12 spots, while newcomers like Zhipu AI's GLM 4.7 and Arcee AI's Trinity Large bring fresh competition.
The ecosystem around LM Studio, Ollama, and llama.cpp continues to simplify access to these models. Whether you have 8GB or 96GB of RAM, there's now a world-class open model for you — from GLM 4.7 Flash (8GB) to DeepSeek V3.2 (48GB+). Data privacy is finally accessible without compromising on quality.
Our recommendation: start with Qwen 3 14B or GLM 4.5 Air (16GB systems), Kimi K2.5 or DeepSeek V3.2 (32-48GB+ systems), or GLM 4.7 Flash (8GB systems) depending on your hardware. The important thing is to start experimenting — the open-source AI revolution is happening right now.
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