my take on subq is that it’s not that big of a deal that someone benchmaxxed a linear attention model on mrcr v2 and swebench
people have already shown that you can take an oss model and linearize it without crazy perf loss pretty cheaply
just not that useful in practice
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OpenAI chose the worst time to post about SWE-bench Pro, just after Grok 4.5 smashed GPT 5.5 on that benchmark.
GLM-5.2 is looking surprisingly strong.
According to this evaluation across 8 tough benchmarks (SWE-bench Pro, Terminal-Bench, DeepSWE, ProgramBench, Tool-Decathlon, etc.), Zhipu’s latest model is beating or matching the top closed models in several key areas, especially coding and agentic tasks.
It leads in:
SWE-bench Pro
Terminal-Bench 2.1
DeepSWE
ProgramBench
MCP-Atlas
Tool-Decathlon
Claude Opus 4.8 still looks very competitive in some areas, but GLM-5.2 is clearly in the conversation now.
Interesting to see Chinese labs continuing to close the gap this aggressively on practical coding/agent benchmarks.
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New open-source SOTA on agentic coding! 🚀
Ornith-1.0-397B achieves 82.4 on SWE-bench Verified and 77.5 on Terminal-Bench 2.1, topping every open model in its class and beating Claude Opus 4.7 on both.
🤖
📦 Four sizes (9B to 397B-MoE), post-trained on Gemma 4 / Qwen 3.5, MIT licensed and globally accessible.
✨ Notably, Ornith uses RL to generate not just solution rollouts but also the scaffold that drives them. By jointly optimizing both, the model discovers better search trajectories and produces higher-quality solutions.
⚙️ Deployable on a single 8×80GB node, with vLLM and SGLang recipes in the model card.
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Introducing MiniMax M3: The First Open-Weights Model to Combine Three Frontier Capabilities
- Coding & Agentic Frontier: 59.0% SWE-Bench Pro, 66.0% Terminal Bench 2.1, 34.8% SWE-fficiency, 28.8% KernelBench Hard, 74.2% MCP Atlas
- MiniMax Sparse Attention scales context to 1M
- Natively Multimodal from Step Zero
API:
Token Plan:
🚀New! MiniMax Code:
Weights & Tech Report in ~10 Days
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In evals, Sonnet with an Opus advisor scored 2.7 percentage points higher on SWE-bench Multilingual than Sonnet alone, while costing 11.9% less per task.
Our Single-rollout Asynchronous Optimization (SAO), is able to train stably for one thousand steps and consistently outperform GRPO and its variants on agentic coding and reasoning benchmarks,
such as SWE-Bench Verified, BeyondAIME, and IMOAnswerBench.
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The models main strength is in coding. On the index splits, it scores 75.5% SWE-bench Verified, 86.9% on Vibe Code Bench, and 74.5% Terminal-Bench 2.1.