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★ Featured Qwen3-Coder-Next: The 80B Coding Model That Activates Only 3B Parameters
Qwen3-Coder-Next is Qwen’s open-weight coding model built specifically for autonomous coding agents and local development. With 80B total parameters but only 3B activated per token, a 256K context window, strong tool-calling, and support for long-horizon coding tasks, it delivers unusually high coding-agent performance without activating the full model on every inference.
Trending AI & Software Tools
Quick verdicts from our testing lab.
DeepSeek
Powerful free AI for reasoning and coding
Pros
- ✓Excellent coding
- ✓Deep reasoning
- ✓Free chat
Cons
- ✕Peak-time limits
- ✕No dedicated IDE
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Kimi
Agentic AI for coding, research and long tasks
Pros
- ✓K2.6 free
- ✓Long context
- ✓Web search & agents
Cons
- ✕Advanced models paid
- ✕Some usage limits
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Qwen
Open AI models for coding, reasoning and research
Pros
- ✓Strong coding
- ✓Open-weight models
- ✓Long context
Cons
- ✕UI varies by model
- ✕Usage limits
Affiliate link — we may earn a commission at no extra cost to you.
OpenCode
Open-source terminal AI coding agent
Pros
- ✓Open source
- ✓Terminal based
- ✓Many model providers
Cons
- ✕Requires setup
- ✕Model access varies
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Latest Articles
Fresh reviews and guides, weekly.
KAT-Coder-V2.5-Dev: The 35B Mixture-of-Experts Model Built for Autonomous Coding Agents
Kwaipilot’s KAT-Coder-V2.5-Dev is an Apache-2.0 open-weight agentic coding model with 35 billion total parameters and only 3 billion activated parameters. Designed for software-engineering agents, it supports up to 262K tokens of context and delivers strong results across SWE-bench, Terminal-Bench, PinchBench, and KAT-Code-Bench.
Anima-2.9B: A 2.9B-Parameter Image Model Built for Anime and Illustration
Gazingstars123’s Anima-2.9B expands the original Anima architecture to roughly 2.9 billion parameters, targeting anime, illustration, and non-photorealistic image generation with newer training data, stronger prompt understanding, and support for ComfyUI and Forge-Neo.
Ling-3.0-tiny: A 7.9B AI Model With Just 1.3B Active Parameters for Fast Local Reasoning
InclusionAI’s Ling-3.0-tiny is a lightweight hybrid reasoning MoE model with 7.9B total parameters and only 1.3B activated per token. Built for coding, reasoning, agentic workflows, and local deployment, it can reach around 86–90 tokens per second on an M4 Pro MacBook while using roughly 8.34 GiB of peak memory at an 8K context length.
Meta Muse Glimmer 30B: The New King of Local AI Agents
Meta's new 30-billion parameter multimodal model brings agentic reasoning, native tool calling, and speculative decoding to consumer hardware. Here is everything you need to know.
NVIDIA Nemotron 3.5 Lightning 30B: A 3B-Active Model Built for Long-Running AI Agents
NVIDIA’s Nemotron 3.5 Lightning 30B-A3B-NVFP4 combines a 30-billion-parameter hybrid Mixture-of-Experts architecture with only 3 billion active parameters, a 1M-token context window, NVFP4 quantization, and speculative decoding designed for fast agentic workloads, coding, reasoning, RAG, and long-running AI systems.
Kimi K3: The Open-Weight AI Agent With 2.8 Trillion Parameters and a Million-Token Context
Moonshot AI’s Kimi K3 is an open-weight multimodal agentic model with 2.8 trillion total parameters, 104 billion activated parameters, native vision, and a 1-million-token context window. Its combination of long-horizon coding, tool use, multimodal reasoning, and open weights makes it one of the most ambitious AI releases of 2026.
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