Qwen3-VL adalah model vision-language dalam seri Qwen3, mencapai performa state-of-the-art (SOTA) pada berbagai benchmark vision-language (VL). Model ini mendukung input Image dengan resolusi tinggi hingga level megapiksel dan memiliki kemampuan kuat dalam memahami visual secara umum, OCR multibahasa, pengkaitan visual yang detail, dan dialog visual. Sebagai bagian dari seri Qwen3, model ini mewarisi fondasi bahasa yang kuat, memungkinkannya memahami dan melaksanakan instruksi yang kompleks....

Qwen3-VL-Thinking adalah versi dari seri Qwen3-VL yang dioptimalkan khusus untuk tugas penalaran visual yang kompleks. Ini mengintegrasikan "Mode Berpikir", yang memungkinkannya untuk menghasilkan langkah-langkah penalaran perantara yang rinci (Rantai-Pemikiran) sebelum memberikan jawaban akhir. Desain ini secara signifikan meningkatkan kinerja Model pada jawaban pertanyaan visual (VQA) dan tugas-tugas bahasa-vision lain yang memerlukan logika multi-langkah, perencanaan, dan analisis mendalam....

Qwen3-VL-8B-Instruct adalah model vision-language dari seri Qwen3, yang menunjukkan kemampuan kuat dalam pemahaman visual umum, dialog yang berpusat pada visual, dan pengenalan Text multibahasa dalam Image....

Seri Qwen3-VL menawarkan pemahaman & generasi Text yang superior, persepsi visual & penalaran yang lebih dalam, panjang konteks yang diperpanjang, pemahaman dinamika Video dan spasial yang ditingkatkan, serta kemampuan interaksi agen yang lebih kuat. Tersedia dalam arsitektur Dense dan MoE yang dapat diskalakan dari edge ke cloud, dengan edisi Thinking yang ditingkatkan oleh instruksi dan penalaran....

Seri Qwen3-VL menawarkan pemahaman & generasi Text yang superior, persepsi visual & penalaran yang lebih dalam, panjang konteks yang diperpanjang, pemahaman dinamika Video dan spasial yang ditingkatkan, serta kemampuan interaksi agen yang lebih kuat. Tersedia dalam arsitektur Dense dan MoE yang dapat diskalakan dari edge ke cloud, dengan edisi Thinking yang ditingkatkan oleh instruksi dan penalaran....

Kimi K2.7 Code is a coding-focused agentic model built upon Kimi K2.6. With substantial improvements on real-world long-horizon coding tasks, it strengthens end-to-end task completion across complex software engineering workflows while improving token efficiency, reducing thinking-token usage by approximately 30% compared with Kimi K2.6....

MiniMax-M3 is MiniMax’s frontier multimodal coding and agentic model, built on the MiniMax Sparse Attention (MSA) architecture. It supports up to a 1M-token context window and accepts image and video inputs. The model is designed for code generation, agentic workflows, tool use, long-context understanding, and multi-step reasoning, showing strong performance on benchmarks such as SWE-Bench Pro, Terminal-Bench 2.1, and MCP Atlas....

Nex-N2 is a family of thinking models with Agentic Thinking. They adaptively decide when and how deeply to reason, unifying agent cognition across coding, search, and tool use into a single coherent paradigm. Key Claims - SOTA among open models on SWE-Verified, SWE-Pro, Terminal Bench 2.0, Tau3, WildClawBench, BFCL V4 - Top-tier in agentic coding (end-to-end dev loops), deep search (BrowserComp, Wild Search, FinSearch), and real-world productivity (GDP Val) - Adaptive Thinking: auto-adjusts reasoning depth per step, 30-50% fewer thinking tokens vs always-on, with equal or better performance - Plug-and-play with Claude Code, Cursor, OpenClaw, and agentic harnesses...

Kimi K2.6 is an open-source, native multimodal agentic model by Moonshot AI, achieving open-source state-of-the-art on benchmarks including HLE with tools, SWE-Bench Pro, and BrowseComp. Built on a MoE architecture with 1T total parameters and 32B activated, the model supports a 256K-token context window and multimodal inputs (image and video) via its MoonViT vision encoder. K2.6 is optimized for agentic workloads: it sustains 4,000+ tool calls over 12+ hours of continuous execution, scales to 300 parallel sub-agents × 4,000 steps per run to produce 100+ files from a single prompt, and supports both Thinking and Instant inference modes with function calling and multi-turn Preserve Thinking...

Qwen3.6-35B-A3B is a large language model from Alibaba's Qwen3.6 series, featuring a Mixture of Experts (MoE) architecture with 35 billion total parameters and approximately 3 billion active parameters per inference, delivering strong performance with efficient compute utilization. The model supports both thinking and non-thinking modes, offering flexible switching between rapid response and deep reasoning...

Qwen3.6-27B is the first open-weight small-to-mid-sized dense model in the Qwen3.6 series, with targeted improvements for code generation, agent workflows, and real-world development tasks. Compared with Qwen3.5-27B, it delivers clear gains in frontend development, repository-level reasoning, tool use, and complex problem solving, while adding support for preserving reasoning context across turns to reduce redundant reasoning in iterative workflows. It also supports vision understanding with a native context length of 262,144 tokens...

GLM-5V-Turbo is Zhipu’s latest flagship multimodal foundation model, optimized for multimodal coding and agent capabilities. It supports up to 200K tokens of image, video, and text context, and, when integrated with frameworks such as Claude Code and OpenClaw, can handle complex long-horizon programming and assistant tasks....

Qwen3.5-397B-A17B is the latest vision-language model in the Qwen series, featuring a Mixture-of-Experts (MoE) architecture with 397B total parameters and 17B activated parameters. It natively supports 256K context length, extensible to approximately 1M tokens, with support for 201 languages, unified vision-language understanding, tool calling, and reasoning (thinking) mode...

Qwen3.5-122B-A10B is a native multimodal large language model from the Qwen team, with 122B total parameters and only 10B activated. It features an efficient hybrid architecture combining Gated Delta Networks with sparse Mixture-of-Experts (MoE), natively supporting a 256K context length extensible up to ~1M tokens. Through early fusion training, it achieves unified vision-language capabilities supporting text, image, and video understanding, with strong performance across knowledge, reasoning, coding, agents, visual understanding, and multilingual benchmarks, surpassing GPT-5-mini and Qwen3-235B-A22B on multiple metrics. It defaults to thinking mode, supports tool calling, and covers 201 languages and dialects...

Qwen3.5-35B-A3B is a native multimodal large language model from the Qwen team, with 35B total parameters and only 3B activated. It features an efficient hybrid architecture combining Gated Delta Networks with sparse Mixture-of-Experts (MoE), natively supporting a 262K context length extensible up to ~1M tokens. The model achieves unified vision-language capabilities through early fusion training, supporting text, image, and video understanding with strong performance across reasoning, coding, agents, and visual understanding benchmarks. It defaults to thinking mode, supports tool calling, and covers 201 languages and dialects...

Qwen3.5-27B is a native multimodal large language model from the Qwen team with 27B parameters. It features an efficient hybrid architecture combining Gated Delta Networks with Gated Attention, natively supporting a 256K context length extensible up to ~1M tokens. The model achieves unified vision-language capabilities through early fusion training, supporting text, image, and video understanding with strong performance across reasoning, coding, agents, and visual understanding benchmarks, surpassing Qwen3-235B-A22B and GPT-5-mini on multiple metrics. It defaults to thinking mode, supports tool calling, and covers 201 languages and dialects...

Qwen3.5-9B is a native multimodal large language model from the Qwen team with 9B parameters. As a lightweight dense model in the Qwen3.5 series, it features an efficient hybrid architecture combining Gated Delta Networks with Gated Attention, natively supporting a 262K context length extensible up to ~1M tokens. The model achieves unified vision-language capabilities through early fusion training, supporting text, image, and video understanding. It defaults to thinking mode, supports tool calling, and covers 201 languages and dialects...

Gemma 4 26B is Google DeepMind's latest open-source MoE model, built on a 26B-parameter Mixture of Experts architecture that activates only 3.8B parameters during inference for exceptionally fast token throughput. Purpose-built for advanced reasoning and agentic workflows, it ranks #6 among all open models on the Arena AI leaderboard — outperforming models up to 20x its size — with native function-calling, 256K context, and full Apache 2.0 licensing....

Kimi K2.5 adalah model agen multimodal asli open-source yang dibangun melalui pelatihan berkelanjutan pada sekitar 15 triliun kombinasi token visual dan Text di atas Kimi-K2-Base. Dengan arsitektur MoE berparameter 1T (32B aktif) dan panjang konteks 256K, model ini memadukan pemahaman Vision dan bahasa secara mulus dengan kemampuan agenik canggih, mendukung kedua mode instan dan pemikiran, serta paradigma percakapan dan agenik....

Gemma 4 26B is Google DeepMind's latest open-source MoE model, built on a 26B-parameter Mixture of Experts architecture that activates only 3.8B parameters during inference for exceptionally fast token throughput. Purpose-built for advanced reasoning and agentic workflows, it ranks #6 among all open models on the Arena AI leaderboard — outperforming models up to 20x its size — with native function-calling, 256K context, and full Apache 2.0 licensing....

Gemma 4 31B is Google DeepMind's latest open-source model, built on a 31B dense architecture from the same research foundation as Gemini 3. Purpose-built for advanced reasoning and agentic workflows, it ranks #3 among all open models on the Arena AI leaderboard — outperforming models up to 20x its size — with native function-calling, 256K context, and full Apache 2.0 licensing....