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qwen3.5
Alibaba Qwen
Qwen3.5: Qwen's 2026 generation, 2B to 35B-A3B, with a 256K native context and strong coding for a general model.
- size
- 27B
- vram
- ~18 GB
- license
- Apache-2.0
75% of a 24 GB card
#chat #code #reasoning
qwen3-coder-next
Alibaba Qwen
Qwen3-Coder-Next: an 80B MoE with only 3B active, a 256K context, and training aimed at agent harnesses like Claude Code and Cline.
- size
- 80B-A3B
- vram
- ~52 GB
- license
- Apache-2.0
2.2× a 24 GB card
#code #agentic #moe
glm4.7-flash
Z.ai
GLM-4.7-Flash: a 30B-A3B MoE built for agentic coding. Only ~3B parameters active per token, so it runs at small-model speed.
- size
- 30B-A3B
- vram
- ~20 GB
- license
- MIT
83% of a 24 GB card
#code #agentic #moe
devstral2-small
Mistral AI
Devstral Small 2 (24B, 2512): Mistral's second-generation agentic coder, with a 256K context, under Apache-2.0.
- size
- 24B
- vram
- ~16 GB
- license
- Apache-2.0
67% of a 24 GB card
#code #agentic #tools
olmo-3-7b-instruct
Ai2
Open language model from Ai2, pre-trained on Dolma 3 and post-trained on the Dolci datasets; all code and checkpoints are released.
- size
- 7B
- vram
- ~6 GB
- license
- Apache-2.0
25% of a 24 GB card
#chat #instruct
glm4.6
Z.ai
GLM-4.6: Z.ai's flagship ~357B MoE for coding and agents, with a 200K context. Server- or big-workstation class.
- size
- 357B
- vram
- ~220 GB
- license
- MIT
9.2× a 24 GB card
#code #agentic #moe
qwen3-coder
Alibaba Qwen
Qwen3-Coder 30B-A3B: an agentic coding MoE with 256K context that runs at small-model speed on one GPU.
- size
- 30B-A3B
- vram
- ~19 GB
- license
- Apache-2.0
79% of a 24 GB card
#code #agentic #moe
glm4.5-air
Z.ai
GLM-4.5-Air: a 106B MoE with 12B active, hybrid reasoning and strong agent and coding scores, under MIT. Workstation-class.
- size
- 106B-A12B
- vram
- ~64 GB
- license
- MIT
2.7× a 24 GB card
#moe #agentic #reasoning
kimi-k2
Moonshot AI
Kimi K2: a 1-trillion-parameter MoE (32B active) tuned for agentic work. The top of the range: big servers, or very aggressive quants.
- size
- 1T-A32B
- vram
- ~250 GB
- license
- Modified MIT
10.4× a 24 GB card
#moe #agentic #code
nextcoder
Microsoft
NextCoder 32B: Microsoft's code-editing model, trained to apply changes to existing code rather than write it from scratch.
- size
- 32B
- vram
- ~22 GB
- license
- MIT
92% of a 24 GB card
#code #editing #refactoring
kimi-dev
Moonshot AI
Kimi-Dev 72B: Moonshot's open model for fixing real GitHub issues: it finds the bug, writes the patch, and checks it.
- size
- 72B
- vram
- ~42 GB
- license
- MIT
1.8× a 24 GB card
#code #swe #agentic
hunyuan3d-2.1
Tencent Hunyuan
Hunyuan3D-2.1: the highest-fidelity shapes of the family, from a 3.3B model, with fully open training code.
- size
- 3.3B
- vram
- ~10 GB
- license
- Tencent Hunyuan Community
42% of a 24 GB card
#3d #image-to-3d #high-fidelity
qwen3-embedding
Alibaba Qwen
Qwen3-Embedding: multilingual text and code embeddings at 0.6B, 4B and 8B, near the top of the MTEB leaderboard.
- size
- 0.6B
- vram
- ~1 GB
- license
- Apache-2.0
4% of a 24 GB card
#embedding #retrieval #multilingual
devstral
Mistral AI & All Hands
Devstral Small 24B: built for agentic coding. It navigates a repo, edits files and runs tests, the way the ArcFlare agent and OpenCode work.
- size
- 24B
- vram
- ~16 GB
- license
- Apache-2.0
67% of a 24 GB card
#code #agentic #tools
mellum
JetBrains
Mellum 4B: JetBrains' completion model. A base model built for fill-in-the-middle autocomplete, not chat.
- size
- 4B
- vram
- ~5 GB
- license
- Apache-2.0
21% of a 24 GB card
#code #autocomplete #fim
seed-coder
ByteDance Seed
Seed-Coder 8B: a strong small coding model, with a separate reasoning variant. MIT licensed.
- size
- 8B
- vram
- ~7 GB
- license
- MIT
29% of a 24 GB card
#code #small #reasoning
olympiccoder
Hugging Face (open-r1)
OlympicCoder 32B: a reasoning model trained for competitive programming, from Hugging Face's open-r1 project.
- size
- 32B
- vram
- ~22 GB
- license
- Apache-2.0
92% of a 24 GB card
#code #reasoning #competitive
phi4
Microsoft
14B dense decoder-only Transformer trained on synthetic and filtered public data, built for high-quality reasoning in constrained environments.
- size
- 14B
- vram
- ~10 GB
- license
- MIT
42% of a 24 GB card
#chat #instruct
qwen2.5-coder-32b
Alibaba Qwen
Code-specific instruction-tuned 32B Qwen2.5-Coder model, improved at code generation, code reasoning and code fixing.
- size
- 32B
- vram
- ~21 GB
- license
- Apache-2.0
88% of a 24 GB card
#code #instruct #large
qwen2.5-coder-14b-instruct
Qwen
Instruction-tuned 14B code model with improved code generation, reasoning and fixing, and long-context support up to 128K tokens.
- size
- 14B
- vram
- ~10 GB
- license
- Apache-2.0
42% of a 24 GB card
#code #instruct
qwen2.5-coder
Alibaba Qwen
Code-specific instruction-tuned 7B Qwen2.5-Coder model, improved at code generation, code reasoning and code fixing.
- size
- 7B
- vram
- ~6 GB
- license
- Apache-2.0
25% of a 24 GB card
#code #instruct
yi-coder-9b-chat
01.AI
Open-source 9B code chat model with a 128K maximum context length, supporting 52 major programming languages.
- size
- 9B
- vram
- ~7 GB
- license
- Apache-2.0
29% of a 24 GB card
#code #instruct
mistral-nemo-12b
Mistral AI / NVIDIA
12B instruct model trained jointly by Mistral AI and NVIDIA with a 128k context window on multilingual and code data.
- size
- 12B
- vram
- ~9 GB
- license
- Apache-2.0
38% of a 24 GB card
#chat #instruct
gemma2
Google's instruction-tuned Gemma 2 9B, trained on 8T tokens of web documents, code and mathematics; no system prompt support.
- size
- 9B
- vram
- ~7 GB
- license
- Gemma
29% of a 24 GB card
#chat #instruct
deepseek-coder-v2-lite
DeepSeek
DeepSeek-Coder-V2-Lite: a 16B mixture-of-experts coder with a long context. Cheap to run for its size.
- size
- 16B
- vram
- ~12 GB
- license
- DeepSeek License
50% of a 24 GB card
#code #moe #long-context
codestral
Mistral AI
Codestral 22B: Mistral's dedicated code model, trained on 80+ languages. Non-production licence: research and personal use only.
- size
- 22B
- vram
- ~15 GB
- license
- Mistral Non-Production
63% of a 24 GB card
#code #fim #non-commercial
starcoder2
BigCode
Code LLM self-aligned from StarCoder2-15B with a fully permissive, transparent pipeline and no human annotations or distilled data.
- size
- 15B
- vram
- ~11 GB
- license
- BigCode OpenRAIL-M
46% of a 24 GB card
#code #instruct