Tag: coding
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Moonshot AI Releases Kimi K2.6 with 256K Context and 300-Agent Swarms
Moonshot AI released Kimi K2.6 on April 20, 2026, shipping an open-weight trillion-parameter Mixture-of-Experts model that leads headline agentic and coding benchmarks against GPT-5.4 and Claude Opus 4.6, while pushing its Agent Swarm system to 300 sub-agents running 4,000 coordinated steps. The weights are available on Hugging Face under a Modified MIT License, and the […]
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Anthropic Releases Claude Opus 4.7 With Sharper Coding and 3x Vision Resolution
Anthropic released Claude Opus 4.7 on April 16, 2026 — a direct upgrade to Opus 4.6 that focuses almost entirely on one thing: making the model better at long, hard software engineering work. The new release posts a 13% lift on Anthropic’s internal 93-task coding benchmark, resolves roughly 3x more production-grade tasks on Rakuten-SWE-Bench, and […]
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GLM-5.1: Z.ai’s Open-Weight Model Takes #1 on SWE-Bench Pro
On April 7, 2026, Z.ai (formerly Zhipu AI) released GLM-5.1 — a 754-billion-parameter open-weight Mixture-of-Experts model that claims the #1 position on SWE-Bench Pro with a score of 58.4%, edging out Claude Opus 4.6 (57.3%). Licensed under MIT and designed for agentic engineering, GLM-5.1 can autonomously sustain coding tasks for up to eight hours across […]
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MiniMax M2.7: The First AI Model That Helps Train Itself
MiniMax has released M2.7, the first frontier AI model designed to participate in its own training loop. Announced on March 18, 2026, M2.7 introduces what MiniMax calls “self-evolution” — the model autonomously analyzes its own failure trajectories, modifies its scaffold code, and runs evaluations across 100+ iterative rounds, achieving a 30% performance improvement on internal […]
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Leanstral: Mistral’s Open-Source Proof Agent for Lean 4
On March 16, 2026, Mistral AI released Leanstral — the first open-source AI agent purpose-built for Lean 4, the formal proof assistant used across mathematical research and verified software development. With 120 billion total parameters but only 6 billion active per token, Leanstral can formally prove that AI-generated code meets its specifications — addressing one […]
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Qwen3-Coder-Next: Alibaba’s Ultra-Sparse 80B Coding Agent
Key points Alibaba’s Qwen team released Qwen3-Coder-Next in February 2026 under the Apache 2.0 license, an open-weight coding model that activates only 3 billion of its 80 billion total parameters per forward pass. On SWE-Bench Verified with the SWE-Agent scaffold the model scores 70.6%, and it also scores 62.8% on SWE-Bench Multilingual and 44.3% on […]
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MiniMax M2.5: Frontier AI Performance at a Fraction of the Cost
Key points Announced on February 12, 2026, MiniMax’s M2.5 ships as open weights under a modified MIT license and, according to the company, matches leading proprietary systems at one-tenth to one-twentieth of their cost. M2.5 is a mixture-of-experts model with 229 billion total parameters, 10 billion active parameters and a 200,000-token context window, designed for […]
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Moonshot AI Releases Kimi K2.5 with Agent Swarm and Frontier Vision
Key points Moonshot AI released Kimi K2.5 on January 27, 2026, an open-weight model combining a trillion-parameter Mixture-of-Experts architecture with native multimodal reasoning and Agent Swarm. Kimi K2.5 is built on a Mixture-of-Experts architecture with 1 trillion total parameters, of which only 32 billion are activated per token. Agent Swarm decomposes complex tasks into subtasks […]
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GLM-5: Zhipu AI Ships a 744B Open-Weight Frontier Model
Key points On February 11, 2026, Zhipu AI released GLM-5 through its Z.ai platform with 744 billion total parameters and a Mixture-of-Experts architecture. GLM-5 has 744B total parameters with 40B active per token, spanning 80 layers and 256 experts of which 8 are active at inference, a sparsity rate of about 5.9%. GLM-5 scored 50 […]
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GLM-4.7-Flash: Z.ai’s Efficient 30B MoE Model for Coding and Agents
Key points Z.ai released GLM-4.7-Flash on January 20, 2026, a fully open-source model under the MIT license that is available for free via the Z.ai API platform. Despite its 30B label, the model uses a Mixture-of-Experts architecture with 31.2 billion total parameters but only approximately 3 billion active parameters during any given inference pass. It […]