Open-Weight LLM Catalog

Complete catalog of open-weight models for local inference β€” coding, chat, reasoning, vision, and MoE.

πŸ‡·πŸ‡Ί Russian version: catalog.ru.md

← Local models Β· How to choose β†’

Contents

  1. Coding models
  2. General chat/instruction models
  3. Reasoning / math models
  4. Multimodal / Vision models
  5. Small fast models (up to 4B)
  6. MoE (Mixture-of-Experts) models
  7. Long context models
  8. Hidden gems
  9. Quick selection by RAM
  10. License reference
  11. Whats next

1. Coding models

Small (up to 14B) β€” for 8-16 GB RAM

Model Params Context FIM HumanEval Where to run Ollama
Stable Code 3B 2.7B 16K ~55% 4GB VRAM, CPU ollama run stable-code:3b
CodeGemma 2B 2B 8K ~50% 2GB VRAM ollama run codegemma:2b
Qwen 2.5 Coder 1.5B 1.5B 32K ~45% 2GB, CPU ollama run qwen2.5-coder:1.5b
Yi-Coder 1.5B 1.5B 128K ~48% 2GB, CPU ollama run yi-coder:1.5b
Granite Code 3B 3B 4K ~52% 4GB Community
Phi-4-mini (3.8B) 3.8B 128K 74.4% 3GB ollama run phi4-mini
Qwen 2.5 Coder 7B 7B 128K 82% 6GB ollama run qwen2.5-coder:7b
CodeGemma 7B 7B 8K ~65% 8GB ollama run codegemma:7b
StarCoder2 7B 7B 16K ~60% 7GB Community
DeepSeek R1 Distill Qwen 7B 7B 32K ~58% 6GB ollama run deepseek-r1:7b
Mistral Small 3 (7B) 7B 128K ~60% 6GB ollama run mistral-small:7b
Yi-Coder 9B 9B 128K ~70% 8GB ollama run yi-coder:9b

Bottom line: For coding on 16 GB Mac β€” Qwen 2.5 Coder 7B (FIM, 82% HumanEval). If it fits β€” Yi-Coder 9B (128K context). For autocomplete (FIM) β€” CodeGemma 2B or Qwen 2.5 Coder 1.5B (lightning fast).

Medium (14B-30B) β€” for 24-32 GB RAM

Model Params Context SWE-bench LiveCodeBench When to choose
Qwen3-Coder-30B-A3B (MoE) 30B / 3.3B active 256K ~70% β€” Best MoE coder, 256K ctx
Devstral Small 2 (24B) 24B dense 256K 68.0% β€” Agent coding, multi-file edits
Codestral 25.01 (22B) 22B dense 256K β€” β€” Best FIM (95.3%), 80+ languages
gpt-oss-20b 20B MoE 128K β€” β€” ~o3-mini, Apache 2.0
Granite Code 20B 20B 4K β€” β€” Enterprise, Apache 2.0
Phi-4 (14B) 14B 16K β€” β€” MMLU 84.8%, coding + math
Phi-4-reasoning (14B) 14B 16K β€” β€” Reasoning + code
DeepSeek R1 Distill Qwen 14B 14B 32K β€” β€” Reasoning for complex tasks
Qwen 2.5 Coder 14B 14B 128K β€” β€” FIM, 128K ctx
StarCoder2 15B 15B 16K β€” β€” 600+ languages

Large (30B+) β€” for 48-128 GB RAM or cloud

Model Params SWE-bench LiveCodeBench License
Qwen3.6-27B dense 27B 77.2% 83.9% Apache 2.0 best consumer coding model 2026
DeepSeek-V4-Flash 284B / 13B active ~78% ~80% MIT
Devstral 2 (123B) 123B dense 72.2% β€” Modified MIT
Qwen3-Coder-480B 480B 69.6% β€” Apache 2.0
Kimi K2.6 1T / 38B active 80.2% 72.4% Modified MIT
Kimi K2.7 Code 1T+ / ~38B active β€” β€” Modified MIT
Kimi K3 2.8T / ~50B active β€” β€” Modified MIT
GLM-5.1 744B / 40B active β€” 73.9% MIT
DeepSeek-V4-Pro 1.6T / 49B active 80.6% 93.5% MIT best open-weight coder

2. General chat/instruction models

2.1 Qwen 3.5 (Feb 2026) β€” all Apache 2.0

Model Params Context Feature
Qwen3.5-0.8B 0.8B 256K Edge, CPU inference
Qwen3.5-2B 2B 256K Mobile
Qwen3.5-4B 4B 256K Laptops
Qwen3.5-9B 9B 256K Sweet spot for 16GB
Qwen3.5-27B 27B 256K 24GB GPU
Qwen3.5-35B-A3B 35B / 3B active (MoE) 262K Efficient MoE
Qwen3.5-122B-A10B 122B / 10B active (MoE) 262K Mid-range frontier
Qwen3.5-397B-A17B 397B / 17B active 262K Flagship (Hybrid DeltaNet)

2.2 Qwen 3.6 (Apr 2026) β€” Apache 2.0

Model Params Context Key
Qwen3.6-27B 27B dense 262K SWE-bench 77.2%, native multimodal
Qwen3.6-35B-A3B 35B / 3B active (MoE) 262K MoE version

Important: Qwen3.6-27B is the best local model of 2026 for coding. In Q4_K_M ~17GB, fits in 24GB.

2.3 Llama 4 (Apr 2026) β€” Llama Community License

Model Params Context MMLU-Pro Feature
Llama 4 Scout 109B / 17B active (MoE) 10M β€” Longest context, 1Γ— H100
Llama 4 Maverick 400B / 17B active (MoE) 1M 80.5% Multimodal

2.4 Llama 3.x β€” Llama Community License

Model Params Context When to choose
Llama 3.2 1B 1B 128K Edge / CPU
Llama 3.2 3B 3B 128K 8GB RAM, fast
Llama 3.1 8B 8B 128K Proven baseline
Llama 3.3 8B 8B 128K Improved 3.1 8B
Llama 3.3 70B 70B 128K 32GB+, powerful general

2.5 Google Gemma 4 (Apr 2026) β€” all Apache 2.0

Model Params Context MMLU-Pro AIME Feature
Gemma 4 E2B ~2.3B eff (5.1B total) 128K β€” β€” Phones, IoT, audio
Gemma 4 E4B ~4.5B eff (8B total) 128K 69.4% 42.5% Mobile + vision
Gemma 4 12B Unified ~12B 256K β€” β€” Text + image + audio
Gemma 4 26B A4B MoE 25.2B / 3.8B active 256K 82.6% 88.3% 97% quality of 31B at 4B cost
Gemma 4 31B Dense 30.7B 256K 85.2% 89.2% Best single-GPU model

2.6 Others

Model Params Context Feature
gpt-oss-20b (OpenAI) 20B MoE 128K Open-weight from OpenAI, Apache 2.0
gpt-oss-120b (OpenAI) 117B / 5.1B active 128K Codeforces Elo 2622, Apache 2.0
GLM-5.2 753B / ~40B active 1M SWE-Bench Pro 62.1%, MIT
Mistral Large 2 ~123B MoE 128K Frontier general-purpose
Command R+ 104B 128K RAG-specialized
Yi-1.5 6B / 9B / 34B 200K Long context

3. Reasoning / math models

Model Params Context MATH-500 AIME 2024 Where to run
Phi-4-reasoning 14B 16K β€” β€” 16GB
Qwen QwQ-32B 32B 32K β€” β€” 24GB
DeepSeek-R1-Distill-Qwen-1.5B 1.5B 32K β€” β€” 4GB
DeepSeek-R1-Distill-Qwen-7B 7B 32K β€” β€” 8GB
DeepSeek-R1-Distill-Llama-8B 8B 32K β€” β€” 8GB
DeepSeek-R1-Distill-Qwen-14B 14B 32K β€” β€” 16GB
DeepSeek-R1-Distill-Qwen-32B 32B 32K β€” β€” 24GB
DeepSeek-R1-Distill-Llama-70B 70B 32K β€” β€” 48GB
DeepSeek-R1 (full) 671B / 37B active 128K 97.3% 79.8% Cloud / cluster

How to choose:

4. Multimodal / Vision models

Model Params VRAM (Q4) Context Strengths
Moondream 2 1.9B ~2 GB β€” Ultra-light, photos
PaliGemma 2 3B ~3 GB β€” Google, photos + docs
SmolVLM 2 2.2B ~2 GB β€” HuggingFace, compact
LLaVA 1.6 7B 7B ~6 GB β€” Classic, photos/docs
MiniCPM-V 4.5 (8B) 8B ~6 GB β€” Best OCR for documents
Qwen3-VL 8B 7B ~6 GB 256K Multilingual OCR, dynamic resolution
InternVL 2.5 8B 8B ~8 GB β€” Best for UI/charts/code screenshots
Llama 3.2 Vision 11B 11B ~8 GB 128K Best all-rounder for 8-16GB
Gemma 4 12B Unified ~12B ~8 GB 256K Text + image + audio
Gemma 4 26B A4B MoE 25.2B / 3.8B active ~14 GB 256K Vision + 140+ languages
Gemma 4 31B Dense 30.7B ~20 GB 256K Best single-GPU vision
Qwen3-VL 72B 72B ~48 GB 256K Best open-source VLM
Llama 3.2 Vision 90B 90B ~64 GB 128K Best local VLM

5. Small fast models (up to 4B)

For 8GB RAM, CPU inference, edge devices.

Model Params RAM (Q4) MMLU HumanEval tok/s (M1 8GB)
SmolLM2-135M 135M ~0.2 GB β€” β€” 100+
SmolLM2-1.7B 1.7B ~1.2 GB β€” β€” 50+
Qwen3.5-0.8B 0.8B ~0.5 GB β€” β€” 60+
Llama 3.2 1B 1B ~0.8 GB β€” β€” 40-50
TinyLlama 1.1B 1.1B ~0.7 GB β€” β€” 40+
Llama 3.2 3B 3B ~2.0 GB 63.4% β€” 30-40
Qwen3.5-2B 2B ~1.5 GB β€” ~55% 35+
Gemma 4 E2B ~2.3B eff ~1.5 GB ~60% β€” 30+
Qwen3-4B 4B ~2.8 GB ~70% ~74% 22-26
Gemma 3 4B 4B ~2.5 GB 59.6% 71.3% ~24
Phi-4-mini 3.8B ~2.5 GB 67.3% 74.4% ~28
Gemma 4 E4B ~4.5B eff ~5.0 GB 69.4% 52% ~20
SmolLM3-3B 3B ~2.0 GB β€” β€” β€”
H2O-Danube 3B 3B ~2.0 GB β€” β€” β€”
Stable LM 2 3B 3B ~2.0 GB β€” β€” β€”

Bottom line for 8GB Mac:

6. MoE (Mixture-of-Experts) models

MoE activates only a fraction of parameters per token β€” higher quality at the same compute.

Model Total / Active Experts Context Quality Where to run
Qwen3.5-35B-A3B 35B / 3B β€” 262K ~9B level 16-24GB
Qwen3-Coder-30B-A3B 30B / 3.3B β€” 256K coding leader 24GB
Gemma 4 26B A4B 25.2B / 3.8B 128 (8+1) 256K 97% of 31B 24GB
gpt-oss-20b 20B / ~5B β€” 128K ~o3-mini 16GB
Mixtral 8Γ—7B 47B / 13B 8 32K solid 32GB
Mixtral 8Γ—22B 141B / 39B 8 65K powerful 64GB
Qwen3.5-122B-A10B 122B / 10B β€” 262K frontier 48GB+
gpt-oss-120b 117B / 5.1B 128 128K Codeforces 2622 48GB+
Nemotron 3 Super 120B / 12B 512 (top-22) 1M hybrid Mamba 64GB+
Llama 4 Scout 109B / 17B β€” 10M solid 48GB+
Llama 4 Maverick 400B / 17B 128 1M multimodal 48GB+
DeepSeek-V4-Flash 284B / 13B β€” 1M budget frontier 64GB+
DeepSeek-V4-Pro 1.6T / 49B 384+1 1M best coder 8Γ—H200
Qwen3.5-397B-A17B 397B / 17B 512 262K hybrid DeltaNet 64GB+
Kimi K3 2.8T / 50B 896 (16 active) 1M largest open-weight Cluster
GLM-5.2 753B / 40B 256 (8 active) 1M MIT, SWE-Bench Pro 62.1% Cluster

Bottom line: For 24GB Mac β€” Gemma 4 26B A4B or Qwen3-Coder-30B-A3B. For 16GB β€” Qwen3.5-35B-A3B (3B active, ~20GB in Q4 β€” tight but works).

7. Long context models

Model Context Params Real long-context work
Llama 4 Scout 10M 109B / 17B RULER strong
Kimi K2.6 2M ~1.2T / 38B Agent swarm
Kimi K3 1M 2.8T / 50B Frontier
DeepSeek-V4-Pro 1M 1.6T / 49B MIT
DeepSeek-V4-Flash 1M 284B / 13B MIT, budget
GLM-5.2 1M 753B / 40B MIT
Nemotron 3 Ultra 1M 550B / 55B Hybrid Mamba
Qwen3.5-397B-A17B 262K (ext. ~1M YaRN) 397B / 17B Apache 2.0
Qwen3.6-27B 262K 27B dense Apache 2.0, 24GB
Gemma 4 31B 256K 31B RULER 66.4% @128K
Llama 4 Maverick 1M 400B / 17B Multimodal

Important: Long context β‰  quality on long context. Gemma 4 scored RULER 66.4% at 128K β€” 5Γ— better than Gemma 3. Qwen 3.5/3.6 support YaRN for context extension.

8. Hidden gems

Models that deserve more attention than they get.

For coding

Model Size Why try
Stable Code 3B 2.7B CodeLlama 7B quality at 3B size. Best sub-3B coder
Granite Code 3B/8B 3-8B Apache 2.0, built-in PII filtering, 116 languages
Yi-Coder 9B 9B 128K context, beats CodeLlama 34B at 9B
WaveCoder 7B Specialized for code repair/refactoring

For Russian language

Model Size Why
Vikhr-7B 7B Best open-source Russian model. Beats Saiga and ruadapt on NLU
Saiga-YandexGPT-8B 8B Fine-tuned on YandexGPT 5 Lite, 4.98/5 fluency
ruGPT-13B 13B Trained from scratch on Russian corpus

Specialized domains

Model Domain Size Why
BioMistral 7B Medicine 7B Best open-source medical LLM under 10B
ChatLaw 7B Law 7B MoE + multi-agent, reduces hallucinations
FinGPT 7B Finance 7B LoRA fine-tune for <$300

Experimental architectures

Model Architecture Size Feature
Falcon Mamba 7B Pure SSM 7B Constant memory at 128K, 85 tok/s
Codestral Mamba 7B SSM 7B Infinite context via recurrence
Zamba 2 7B Mamba + Attention 7B 16K, hybrid
RWKV 7 G1 Recurrent 1.5-14B Infinite context, CPU-friendly
Jamba 1.5 Mini Hybrid 52B / 12B active 256K, production hybrid

Community Favorites (r/LocalLLaMA)

Model Community verdict
Phi-3 Mini (3.8B) β€œRuns on anything, surprisingly capable”
Gemma 3 9B β€œApple Silicon sweet spot”
Llama 4 Scout β€œUnderrated β€” 100% accuracy in batch tests”
DeepSeek R1 Distills β€œReasoning quality competitive with models 10Γ— larger”
Stable Code 3B Best hidden gem β€” 7B quality at 3B

9. Quick selection by RAM

RAM Mac What to run tok/s Quality
8GB Phi-4-mini (3.8B) or Qwen3.5-4B 25-35 Basic-good
16GB Qwen 2.5 Coder 7B or Gemma 3 9B 15-25 Good
24GB Qwen3.6-27B (Q4) or Gemma 4 26B MoE 18-28 Excellent
32GB Qwen3.6-35B-A3B MoE or Llama 3.1 70B (Q3) 20-40 Excellent
48GB Llama 3.3 70B (Q4) or Qwen3.5-397B MoE (Q4) 10-20 Superior
64GB Llama 3.3 70B (Q6) or Gemma 4 31B (Q8) 8-15 Near FP16
128GB DeepSeek-V4-Flash or Qwen3.5-397B (Q8) 15-30 Maximum
Fast OK Slow but tolerable

10. License reference

License Models Commercial use
Apache 2.0 Qwen 3/3.5/3.6, Gemma 4, gpt-oss, Granite Code Unlimited
MIT DeepSeek V3/V4/R1, GLM-5, Phi-4, Stable Code Unlimited
Llama Community Llama 3.x/4 >700M MAU β€” pay
Modified MIT Kimi K2/K3, Devstral 2 (123B) Check model card
Mistral Non-Prod Codestral Non-commercial only
Gemma Terms Gemma 3 Not OSI-compatible

11. Whats next

If you want Go to
Choose a model for your task and hardware models.md
How much memory does a model need memory-and-context.md
Benchmarks on Mac benchmarks/apple-silicon.md
Run a model if you havent yet running-models.md
Back to navigation README.md

Last updated: July 23, 2026 Sources: HuggingFace model cards, Ollama library, r/LocalLLaMA, CraftRigs, MLJourney, arXiv, official announcements

In section: getting-started Β· running-models Β· models Β· catalog Β· quantization Β· memory-and-context Β· tools Β· advanced-setup Β· troubleshooting Β· apple-silicon
Related sections: Zero Level Β· AI Agents Β· Use Cases
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