According to @_avichawla, DeepSeek V4 Pro leads SWE-bench Verified, but real coding performance depends on the harness, not just leaderboard scores.

According to TheRundownAI, GLM-5.2 debuts with 1M tokens, MIT license, and leads coding, SWE-bench Pro, AIME 2026, plus No.1 on Designarena.

According to TheRundownAI, ZAI’s GLM-5.2 posts 1M context and tops coding, SWE-bench Pro, and AIME 2026 scores, with open weights under MIT.

According to AINewsOfficial_, Claude Fable 5 posts 80.3% on SWE-bench Pro and adds 1M context with 128k output for multi-day autonomy, surpassing rivals.

According to @godofprompt, Opus 4.7 ignores project instructions and skips MCP configs; Anthropic acknowledged regressions versus 4.6 despite higher benchmarks.

According to @_avichawla, Kimi K2.6 matches Claude’s chat, code, cowork at 1/6 price, ranks #1 on OpenRouter, and posts 58.6 on SWE-Bench Pro.

According to @godofprompt, Claude Opus 4.7 follows instructions literally, lifts SWE-bench to 87.6% from 80.8%, and breaks 4.6-tuned prompts.

According to @claudeai on Twitter, Sonnet paired with an Opus advisor achieved a 2.7 percentage point higher score on SWE-bench Multilingual than Sonnet alone while reducing per-task cost by 11.9%. As reported by the Claude account post, this advisor-enhanced workflow indicates measurable quality gains and cost efficiency in multilingual software engineering benchmarks. For AI product teams, the data suggests a practical orchestration strategy: route primary reasoning to Sonnet and use Opus selectively for guidance to improve pass rates and lower run-time spending. According to the tweet, these results come from evals on SWE-bench Multilingual, highlighting a repeatable method for cost-aware performance optimization in LLM-based coding assistants.

According to God of Prompt on X (Twitter), MiniMax M2.5 surpassed Opus 4.6 on the SWE-Bench Verified benchmark with an 80.2% score, delivers roughly 3x faster execution, and is offered at a flat $1 per hour, while using only 10B activated parameters, positioning it as the smallest Tier-1 model for coding tasks. As reported by the same source, these metrics imply lower latency and significantly reduced inference cost, enabling 24/7 autonomous coding agents and continuous integration bots at practical budgets. According to the post, the combination of high benchmark accuracy and small active parameter count suggests strong efficiency-per-dollar, which can improve ROI for software teams deploying code assistants, test repair bots, and maintenance agents in production pipelines.