GLM-5.3-Flash
5.0 out of 5 stars
(1 Rating)
GLM-5.3-Flash is Z.ai’s natively multimodal model in the GLM-5 series (previously previewed as Ox Alpha), designed to deliver strong coding, agentic, visual, and knowledge-work performance at relatively low inference cost. It uses 320 billion total parameters with 18 billion active parameters, along with a hybrid architecture that combines sparse and linear attention to reduce the cost of long-context processing. The model supports context lengths of up to one million tokens and was trained on a 30-trillion-token multimodal corpus. GLM-5.3-Flash can reason across text, images, documents, interfaces, dashboards, and other visual information while using that feedback to refine its own outputs. Z.ai reports substantial gains over GLM-5.2 on coding and agentic benchmarks, including DeepSWE and AutomationBench, while approaching higher-cost frontier models on several evaluations.
Learn more about GLM-5.3-Flash
DeepSWE
DeepSWE is a fully open source, state-of-the-art coding agent built on top of the Qwen3-32B foundation model and trained exclusively via reinforcement learning (RL), without supervised finetuning or distillation from proprietary models. It is developed using rLLM, Agentica’s open source RL framework for language agents. DeepSWE operates as an agent; it interacts with a simulated development environment (via the R2E-Gym environment) using a suite of tools (file editor, search, shell-execution, submit/finish), enabling it to navigate codebases, edit multiple files, compile/run tests, and iteratively produce patches or complete engineering tasks. DeepSWE exhibits emergent behaviors beyond simple code generation; when presented with bugs or feature requests, the agent reasons about edge cases, seeks existing tests in the repository, proposes patches, writes extra tests for regressions, and dynamically adjusts its “thinking” effort.
Learn more about DeepSWE
Claude Sonnet 5.5
5.0 out of 5 stars
(1 Rating)
Claude Sonnet 5.5 is Anthropic’s mid-tier Claude 5.5 model designed for fast, well-scoped everyday work, coding, and professional document creation. Anthropic says it runs more than 30% faster than Claude Sonnet 5 and typically costs up to 30% less per task while keeping the same base token pricing. The model shows major gains in agentic coding, with a 70.6% score on Terminal-Bench 4.0 and stronger performance on FrontierCode and CursorBench than its predecessor. Sonnet 5.5 also performs strongly on knowledge work, computer use, chart understanding, and long-horizon tasks, in some cases approaching Claude Opus 5.5 at higher effort settings. Anthropic positions it as especially useful for debugging, routine coding, collaboration, and producing polished documents, slides, spreadsheets, and user interfaces. Claude Sonnet 5.5 is available across Anthropic’s platforms as well as AWS, Google Cloud, and Microsoft Azure.
Learn more about Claude Sonnet 5.5
Devstral Small 2
Devstral Small 2 is the compact, 24 billion-parameter variant of the new coding-focused model family from Mistral AI, released under the permissive Apache 2.0 license to enable both local deployment and API use. Alongside its larger sibling (Devstral 2), this model brings “agentic coding” capabilities to environments with modest compute: it supports a large 256K-token context window, enabling it to understand and make changes across entire codebases. On the standard code-generation benchmark (SWE-Bench Verified), Devstral Small 2 scores around 68.0%, placing it among open-weight models many times its size. Because of its reduced size and efficient design, Devstral Small 2 can run on a single GPU or even CPU-only setups, making it practical for developers, small teams, or hobbyists without access to data-center hardware. Despite its compact footprint, Devstral Small 2 retains key capabilities of larger models; it can reason across multiple files and track dependencies.
Learn more about Devstral Small 2