... Claude by Anthropic announced Opus 4.8 late May, and @MiniMax launched M3 four days later . They are two models at radically different price points (Opus 4.8 is 8-10 times higher). And from a performance perspective? The @Kilo Code team ran both models with a code audit job. They tracked tokens, cost, time, and how many known issues each model identified. Here's what they found. Key takeaways @MiniMax

... peak hours is just painful for any production app. To solve this for my own development workflows, I ve been building a high-speed API relay station. It aggregates models like DeepSeek-V4, Qwen 3.7, GLM 5.1, Kimi 2.6, and Minimax-M2.7, with a primary focus on concurrency and stability. Here is a quick look at the infrastructure under the hood: Routing & Latency: Deployed on optimized acceleration routes (Singapore/US West). The TTFT (Time-To-First-Token) is nearly ...

... local coding agent you like, but route it to more model providers without constantly editing config files. What changed: - Local API Bridge: VibeAround can translate between OpenAI Responses, OpenAI Chat Completions, and Anthropic Messages. - More providers: DeepSeek, Qwen/DashScope, Kimi, MiniMax, Z.AI/GLM , Gemini, OpenRouter, Azure OpenAI, and custom endpoints are easier to use from compatible local coding agents. - Reworked Launch/Profile UI: profiles, API routes, model selection, terminal/workspace selection, and per-agent launch state are now cleaner. - Better Claude Code / Codex ... ... Bridge compatibility, including fixes for Qwen streaming and MiniMax

... tests, all green. 6 confirmed issues. Opus 4.7 scored 91/100 at $3.56 Kimi K2.6 reached 75% of this score (68/100) at 19% of the cost ($0.67) Full write-up here As pointed out in another thread comparing @MiniMax M2.7 with Opus 4.6, [1] the gap between open-weight and frontier models has narrowed significantly over the past year. For prototyping or exploring a design, the $0.67 run is a good deal. For work requiring correctness and accuracy, Opus 4.7 remains ... ... ahead. Any experiences coding with open-weight models? [1]: MiniMax

... title generation is now handled by an independent system assistant, automatically selecting the most suitable model in native and preset modes, with fully customizable configuration. Editing a user message during a chat will now create a new branch. Added MiniMax ( minimaxi.com

... Launched last week, open-source frontier model @MiniMax M2.7 scores 56.2% on SWE Bench Pro, converging towards the best proprietary models like @Claude by Anthropic Opus 4.6. How do they compare in practice? The @Kilo Code team just ran both models through three coding tasks to see if the benchmark numbers hold up. They created three TypeScript codebases, each model received the same prompt with no hints, and they scored each model independently after all tests were complete. Key takeaways ... ... Both models found all bugs and security vulnerabilities. Claude Opus 4.6 produced more thorough fixes and 2x more tests. MiniMax

... model definitely depends on your workflows and priorities. High success rate, fast completions, or cost efficient? For coding tasks, there's this thread [1] suggesting @Claude by Anthropic , @Gemini , and @OpenAI 's GPT models, while open-weight models like @MiniMax are bridging the gap with every release. [2] Curious what the community recommends for @OpenClaw ? Note: The poll is inspired by the current leaderboard on pinchbench.com by @KiloClaw [1]: What's the best AI model for coding? [2]: MiniMax

... Anthropic said it uncovered industrial-scale efforts by Chinese Labs ( @DeepSeek , Moonshot and @MiniMax-M2.5 ) to extract capabilities from its Claude models via distillation. According to Anthropic, the campaigns involved: 16M+ exchanges ~24,000 fraudulent accounts Proxy networks to bypass regional restrictions Targeted extraction of reasoning, coding and tool-use abilities Distillation is a common technique for training smaller models from larger ones. The controversy here is using a competitor