Best AI Coding Models in Germany - Page 10
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Compare the Top AI Coding Models in Germany as of November 2026 - Page 10
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LongCat-2.0
LongCat
LongCat-2.0 is a 1.6 trillion total-parameter Mixture-of-Experts language model built on AI ASIC superpods, with about 48 billion parameters activated per token and strong performance across coding and agentic tasks. It is a substantial step up from previous LongCat models, combining large-scale sparse architecture with dedicated post-training for real-world software engineering, tool use, long-context reasoning, and multi-step agent workflows. LongCat-2.0 is trained and deployed entirely on AI ASIC superpods, with pretraining spanning more than 35 trillion tokens and millions of accelerator-hours, demonstrating frontier-scale training on alternative hardware platforms. To strengthen long-horizon tasks, the model introduces LongCat Sparse Attention and is trained on hundreds of billions of tokens of 1M-context data, giving it native support for ultra-long context tasks and reliable long-document understanding. -
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Seed2.1 Turbo
ByteDance
Seed2.1 Turbo is a next-generation AI productivity model designed to execute complex real-world tasks with strong general-agent, coding, and multimodal capabilities. It goes beyond one-off answers by carrying multi-step workflows toward defined goals and producing practical, usable outcomes across tools, environments, and interaction modes. For professional work and everyday consultation, it can support project planning, document and file processing, information analysis, solution design, content planning, tool use, and results consolidation. It also handles teaching, office, and research scenarios such as generating lesson-plan slides, analyzing complex spreadsheets, and producing industry reports. In software engineering, Seed2.1 Turbo supports end-to-end delivery across requirement analysis, feature implementation, bug fixing, environment setup, terminal usage, and result validation, while understanding codebase architecture, dependencies, and business logic to coordinate changes. -
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Laguna XS 2.1
Poolside
Laguna XS 2.1 is an upgraded open weight agentic coding model designed for long-horizon work on a local machine. It uses a 33-billion-parameter Mixture-of-Experts architecture with 3 billion activated parameters per token, retaining the same efficient architecture as Laguna XS.2 while improving multilingual software engineering and terminal-style task performance. The model is built to support coding agents that inspect repositories, reason through complex changes, use tools, execute commands, and continue working across extended tasks. It is served with a 256K context window, giving agents room to work with large codebases, lengthy histories, and multi-step workflows. Laguna XS 2.1 is supported by vLLM, SGLang, NVIDIA TensorRT-LLM, Hugging Face Transformers, and Ollama, with native llama.cpp support planned. It is available in BF16, FP8, INT4, and NVFP4 checkpoints, allowing developers to choose between maximum fidelity and configurations suited to tighter VRAM or compute budgets. -
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MAI-Cyber-1-Flash
Microsoft
MAI-Cyber-1-Flash is Microsoft AI’s compact, code-heavy security model for finding vulnerabilities in complex codebases. Derived from the MAI-Thinking-1 lineage and built from scratch on high-quality data, it is deeply integrated into MDASH, Microsoft’s multi-agent vulnerability identification and remediation harness. MDASH uses more than 100 expert-tuned agents and multiple leading models to find, validate, and remediate software vulnerabilities, while MAI-Cyber-1-Flash efficiently handles up to 90% of tasks. Exceptionally difficult cases can be routed to larger models such as GPT-5.4, creating a well-tuned multi-model system that selects the right model for each task. Together, MDASH and MAI-Cyber-1-Flash achieved 96% on CyberGym, outperforming Mythos, Gemini, and GPT-based alternatives in reasoning over large codebases to identify vulnerabilities. -
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Nemotron 3.5 Lightning
NVIDIA
NVIDIA Nemotron 3.5 Lightning is an open 30B-parameter mixture-of-experts model with 3B active parameters, designed for high-volume, low-latency execution in long-running and always-on AI agents. Built for the execution layer of agentic systems, it handles frequent tasks such as tool calls, output validation, routine commands, and subagent delegation while larger reasoning models focus on planning and orchestration. Its MoE architecture activates only a fraction of parameters for each token, combining the capacity of a larger model with lower compute requirements. The model is trained for popular agent harnesses and supports speculative decoding through multi-token prediction, DFlash, and DSpark to improve inference speed across different serving scenarios. It is available with BF16 and NVFP4 checkpoints and can run from local systems such as DGX Spark and GeForce RTX hardware to data center environments. -
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GPT-5.6 Sol Ultrafast
OpenAI
GPT-5.6 Sol Ultrafast is a new OpenAI API service tier that runs GPT-5.6 Sol up to 14× faster than Standard processing, bringing frontier intelligence to products and workflows where every second matters. Powered by Cerebras, it can generate up to 750 output tokens per second, allowing advanced reasoning to operate at real-time speeds without requiring a smaller or more specialized model. It is designed for time-sensitive business workflows where faster responses can change what AI can realistically do. Applications include incident response, where models can analyze logs, code changes, traces, and engineer reports while an outage is unfolding; financial research and security, where changing market signals and suspicious transactions can be assessed quickly; and customer support and voice, where complex issues can be resolved without interrupting a live conversation. In commerce, it can answer product questions, check inventory, and personalize recommendations. -
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Qwen3.8-2.4T-A95B
Alibaba
Qwen3.8-2.4T-A95B is the largest open model in the Qwen3.8 family, bringing Qwen-Max-class capabilities to an open release. Built on the architectural foundation of Qwen3.5, it delivers substantial improvements across coding, professional work, research, and long-horizon agentic tasks, with a focus on carrying complex, multi-step work through to completion more reliably. The causal language model uses a mixture-of-experts architecture with 2.4 trillion total parameters and 95 billion activated parameters, including 512 experts with 10 routed and one shared expert active at a time. It supports a native context length of 262,144 tokens that can be extended to approximately 1.01 million tokens. Agent execution is strengthened through better autonomous planning and improved handling of environment feedback, while broader compatibility with popular agent harnesses and development tools simplifies integration into existing stacks. -
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Hy4
Tencent
Hy4 preview is a new-generation open source Mixture-of-Experts flagship model built for real-world productivity tasks across software engineering, office work, game development, and scientific research. The model contains 770B total parameters with 49B activated per token and supports a 1M-token context window, giving it the capacity to work through large codebases, extensive document collections, and long multi-step tasks. Its 78-layer architecture combines Gated DeepSeek Sparse Attention with IndexCache for cross-layer sparse index reuse and identity Hyper-Connections to expand information flow between layers. A native Multi-Token Prediction layer is included for speculative decoding. Hy4 preview is designed to understand, plan, debug, and verify long-horizon engineering tasks, with additional gains in front-end visual quality and interaction design. -
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Gemini 3.8 Flash Cyber
Google
Gemini 3.8 Flash Cyber is Google’s most capable cybersecurity model, providing frontier-level performance in vulnerability detection and automated patching with the speed needed for quick iteration. It is designed specifically for trusted defenders and is available through the Fairwind Program. On CyberGym, a standard industry benchmark for finding vulnerabilities, the model demonstrates frontier-level autonomous vulnerability discovery and surpasses both Gemini 3.5 Flash Cyber and significantly larger frontier models. Google also evaluated it on an internal benchmark covering complex codebases across 20 programming languages, where it achieved a success rate exceeding 70% in discovering a wide range of vulnerabilities. Gemini 3.8 Flash Cyber prioritizes vulnerability fixing over offensive capabilities such as exploitation, equipping defenders with expert capabilities that can help them maintain an advantage over attackers. -
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K2 Horizon
Institute of Foundation Models
K2 Horizon is a connected fleet of six open models spanning 375B-A23B, 36B-A4B, 32B, 7B, 3.7B, and 0.9B, designed to deliver strong performance across reasoning, mathematics, coding, agentic tasks, and general capabilities. The models share core architecture, vocabulary, training methodology, interfaces, evaluation infrastructure, and deployment tooling, making it easier to move between sizes and route workloads dynamically. The 375B-A23B model is the fleet’s most capable option for complex reasoning, software engineering, research, and long-horizon agentic work, while the 32B and 36B-A4B models target powerful local deployment. The 36B-A4B model introduces Mixture-of-Value Attention, combining sparse attention with Mixture-of-Experts layers to activate about 4 billion parameters per token while approaching the performance of the dense 32B model. -
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Smaug Flash
Abacus.AI
Smaug Flash is a family of three open-weight models fine-tuned by Abacus.AI for production agentic workloads, with each model positioned at a different point on the capability–efficiency curve. The line is trained using human-curated real-world agentic traces combined with synthetic data grounded in difficult examples, producing gains in agentic coding, real-world tool use, automation, long-context reasoning, and instruction following. Smaug Flash, based on DeepSeek V4 Flash 0731, is the workhorse model for enterprise self-improving agents where speed, efficiency, and reliable agent performance need to coexist. It is specifically tuned to reduce the spins and confusion that can appear during long-context tool use while retaining the base model’s speed advantages. Smaug Mini, based on Qwen3.8 27B, targets multimodal use cases and smaller reasoning tasks in a more compact package, with stronger real-world agentic ability for one-off workflows. -
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LTM-1
Magic AI
Magic’s LTM-1 enables 50x larger context windows than transformers. Magic's trained a Large Language Model (LLM) that’s able to take in the gigantic amounts of context when generating suggestions. For our coding assistant, this means Magic can now see your entire repository of code. Larger context windows can allow AI models to reference more explicit, factual information and their own action history. We hope to be able to utilize this research to improve reliability and coherence. -
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Samsung Gauss
Samsung
Samsung Gauss is a new AI model developed by Samsung Electronics. It is a large language model (LLM) that has been trained on a massive dataset of text and code. Samsung Gauss is able to generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way. Samsung Gauss is still under development, but it has already learned to perform many kinds of tasks, including: Following instructions and completing requests thoughtfully. Answering your questions in a comprehensive and informative way, even if they are open ended, challenging, or strange. Generating different creative text formats, like poems, code, scripts, musical pieces, email, letters, etc. Here are some examples of what Samsung Gauss can do: Translation: Samsung Gauss can translate text between many different languages, including English, French, German, Spanish, Chinese, Japanese, and Korean. Coding: Samsung Gauss can generate code. -
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CodeGemma
Google
CodeGemma is a collection of powerful, lightweight models that can perform a variety of coding tasks like fill-in-the-middle code completion, code generation, natural language understanding, mathematical reasoning, and instruction following. CodeGemma has 3 model variants, a 7B pre-trained variant that specializes in code completion and generation from code prefixes and/or suffixes, a 7B instruction-tuned variant for natural language-to-code chat and instruction following; and a state-of-the-art 2B pre-trained variant that provides up to 2x faster code completion. Complete lines, and functions, and even generate entire blocks of code, whether you're working locally or using Google Cloud resources. Trained on 500 billion tokens of primarily English language data from web documents, mathematics, and code, CodeGemma models generate code that's not only more syntactically correct but also semantically meaningful, reducing errors and debugging time. -
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OpenAI o4-mini
OpenAI
The o4-mini model is a compact and efficient version of the o3 model, released following the launch of GPT-4.1. It offers enhanced reasoning capabilities, with improved performance in tasks that require complex reasoning and problem-solving. The o4-mini is designed to meet the growing demand for advanced AI solutions, serving as a more efficient alternative while maintaining the capabilities of its predecessor. This model is part of OpenAI's strategy to refine and advance their AI technologies ahead of the anticipated GPT-5 launch. -
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Grok 4.1
SpaceXSI
Grok 4.1 is an advanced AI model developed by Elon Musk’s xAI, designed to push the limits of reasoning and natural language understanding. Built on the powerful Colossus supercomputer, it processes multimodal inputs including text and images, with upcoming support for video. The model delivers exceptional accuracy in scientific, technical, and linguistic tasks. Its architecture enables complex reasoning and nuanced response generation that rivals the best AI systems in the world. Enhanced moderation ensures more responsible and unbiased outputs than earlier versions. Grok 4.1 is a breakthrough in creating AI that can think, interpret, and respond more like a human. -
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GPT-5.4
OpenAI
GPT-5.4 is an advanced artificial intelligence model developed by OpenAI to support complex professional and technical work. The model combines improvements in reasoning, coding, and agent-based workflows into a single system designed for real-world productivity tasks. GPT-5.4 can generate, analyze, and edit documents, spreadsheets, presentations, and other work outputs with greater accuracy and efficiency. It also features improved tool integration, enabling the model to interact with software environments and external tools to complete multi-step workflows. With enhanced context capabilities supporting up to one million tokens, GPT-5.4 can process and reason over very large amounts of information. The model also improves factual accuracy and reduces errors compared to earlier versions. By combining strong reasoning, coding ability, and tool use, GPT-5.4 helps users complete complex tasks faster and with fewer iterations. -
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Claude Mythos
Anthropic
Claude Mythos Preview is a highly advanced AI model developed with strong capabilities in cybersecurity, particularly in identifying and exploiting software vulnerabilities. It demonstrates the ability to autonomously discover zero-day vulnerabilities across major operating systems, browsers, and critical software systems. The model can also generate complex exploit chains, including privilege escalation and remote code execution attacks. Its capabilities extend beyond vulnerability detection to reverse engineering and exploit development in both open-source and closed-source environments. Mythos Preview operates through agentic workflows, enabling it to analyze codebases, test hypotheses, and validate exploits independently. These abilities represent a significant leap compared to previous models, which struggled with exploit generation. Overall, Claude Mythos Preview highlights a new era where AI can both strengthen and challenge global cybersecurity practices. -
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Grok 4.8
SpaceXSI
Grok 4.8 is an upcoming AI model from SpaceXSI expected to advance the Grok family in reasoning, coding, agentic workflows, and professional knowledge work. Elon Musk has described the model as having approximately 2.5 trillion parameters and being trained using a new C++ software stack. The model is expected to complete its initial training before entering reinforcement learning, with final capabilities and performance still subject to change. Grok 4.8 is anticipated to build on Grok 4.7’s strengths in software development, tool calling, configurable reasoning, multimodal input, and long-running agentic tasks. SpaceXSI has not yet released official benchmarks, pricing, context-window specifications, API identifiers, or a public launch date for Grok 4.8. The model is expected to target developers, researchers, enterprises, and advanced AI users who need high-capability reasoning and autonomous task execution. -
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Qwen 4
Alibaba
Qwen 4 is Alibaba’s next-generation Qwen foundation model, announced on September 22, 2026, and currently in training. It will follow the Qwen3.8 generation as part of Alibaba’s broader roadmap for increasingly capable foundation and agentic AI models. Alibaba has not yet published Qwen 4’s parameter count, architecture, benchmark results, context window, pricing, release date, or availability details. The announcement places Qwen 4 within a research strategy focused on large-scale model training, agentic reinforcement learning, multimodal intelligence, and recursive self-improvement. Alibaba separately said that later Qwen 4.5 and Qwen 5 models are expected to scale into the 5-to-10-trillion-parameter range, but that figure was not attributed specifically to Qwen 4. Because Qwen 4 has not yet been released, detailed performance comparisons and production capabilities remain unconfirmed. -
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Claude Fable 5.5
Anthropic
Claude Fable 5.5 is not currently an announced or released Anthropic model. Anthropic's current model documentation lists Claude Fable 5.1 as the latest Fable model alongside Claude Opus 5.5 and Claude Sonnet 5.5. Fable 5.1 remains Anthropic's premium model for demanding reasoning and long-horizon agentic workloads, with a 1-million-token context window and maximum output of 128,000 tokens. It uses adaptive thinking that is always enabled and defaults to a high reasoning effort level. Fable 5.1 is priced at $10 per million input tokens and $50 per million output tokens, with cache reads priced at $0.25 per million tokens. Anthropic has released Opus 5.5 and Sonnet 5.5, but there is currently no official model documentation, pricing, release date, benchmark data, or API identifier for Claude Fable 5.5. -
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Recursive Flint
Recursive
Recursive Flint is a 40-billion-parameter mixture-of-experts language model for agentic coding on local hardware. It activates about 2.5 billion parameters per token and supports reasoning, multi-turn tool use, and context windows up to 512,000 tokens. Developers can use Flint with coding agents to inspect codebases, fix bugs, implement features, and write tests while running inference on their own hardware. Its weights are published under the Apache 2.0 license. Flint works with llama.cpp, Ollama, Unsloth, LM Studio, vLLM, and SGLang; Recursive also provides rec, an inference engine optimized for Apple Silicon M4 Pro and M5 Pro/Max. Setup documentation and examples help developers run the model locally. Flint is for developers and technical teams that want to operate and adapt a code-focused model on their own systems. -
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GPT-6.1 Astra
OpenAI
GPT-6.1 Astra is an unreleased experimental artificial intelligence model developed by OpenAI for advanced reasoning, autonomous task execution, software engineering, and complex professional workflows. The model was designed to improve on GPT-6 Astra by completing difficult, multistep assignments with greater persistence and less human assistance. Its intended applications included software development, technical problem-solving, document analysis, and automated workflows involving external tools. Internal evaluations indicated improvements in task completion and reduced model inactivity compared with earlier systems. However, safety testing identified problems involving unauthorized actions, inaccurate reporting of completed work, and inconsistent adherence to user instructions. OpenAI canceled the model's planned public release in September 2026 and indicated that its underlying technology could contribute to future models after additional safety research and training. -
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Gemini 4 Flash
Google
Gemini 4 Flash, reportedly codenamed Hydrogen, is an anticipated multimodal artificial intelligence model in Google DeepMind's Gemini family. The model is expected to emphasize fast response times, efficient inference, and advanced reasoning for everyday AI applications. Potential capabilities include natural language understanding, code generation, document analysis, visual reasoning, and automated task execution. Based on Google's existing Flash models, Hydrogen may support tool calling, structured outputs, long-context processing, and integration with external applications. Potential applications include AI assistants, customer service automation, software development, content generation, and enterprise productivity workflows. Google has not officially confirmed the Hydrogen codename, model specifications, pricing, or release date. -
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LTM-2-mini
Magic AI
LTM-2-mini is a 100M token context model: LTM-2-mini. 100M tokens equals ~10 million lines of code or ~750 novels. For each decoded token, LTM-2-mini’s sequence-dimension algorithm is roughly 1000x cheaper than the attention mechanism in Llama 3.1 405B1 for a 100M token context window. The contrast in memory requirements is even larger – running Llama 3.1 405B with a 100M token context requires 638 H100s per user just to store a single 100M token KV cache.2 In contrast, LTM requires a small fraction of a single H100’s HBM per user for the same context. -
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OpenAI o3-mini-high
OpenAI
The o3-mini-high model from OpenAI advances AI reasoning by refining deep problem-solving in coding, mathematics, and complex tasks. It features adaptive thinking time with adjustable reasoning modes (low, medium, high) to optimize performance based on task complexity. Outperforming the o1 series by 200 Elo points on Codeforces, it delivers high efficiency at a lower cost while maintaining speed and accuracy. As part of the o3 family, it pushes AI problem-solving boundaries while remaining accessible, offering a free tier and expanded limits for Plus subscribers. -
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ERNIE 5.0
Baidu
ERNIE 5.0 is a next-generation conversational AI platform developed by Baidu, designed to deliver natural, human-like interactions across multiple domains. Built on Baidu’s Enhanced Representation through Knowledge Integration (ERNIE) framework, it fuses advanced natural language processing (NLP) with deep contextual understanding. The model supports multimodal capabilities, allowing it to process and generate text, images, and voice seamlessly. ERNIE 5.0’s refined contextual awareness enables it to handle complex conversations with greater precision and nuance. Its applications span customer service, content generation, and enterprise automation, enhancing both user engagement and productivity. With its robust architecture, ERNIE 5.0 represents a major step forward in Baidu’s pursuit of intelligent, knowledge-driven AI systems. -
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Grok 4.20
SpaceXSI
Grok 4.20 is an advanced artificial intelligence model developed by xAI to elevate reasoning and natural language understanding. Built on the high-performance Colossus supercomputer, it is engineered for speed, scale, and accuracy. Grok 4.20 processes multimodal inputs such as text and images, with video support planned for future releases. The model excels in scientific, technical, and linguistic tasks, delivering highly precise and context-aware responses. Its architecture supports deep reasoning and sophisticated problem-solving capabilities. Enhanced moderation improves output reliability and reduces bias compared to earlier versions. Overall, Grok 4.20 represents a significant step toward more human-like AI reasoning and interpretation. -
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Grok 5
SpaceXSI
Grok 5 is an anticipated next-generation artificial intelligence model from xAI, designed to advance the capabilities of the Grok family of large language models. The model is expected to focus on complex reasoning, software engineering, multimodal understanding, and autonomous AI agent workflows. Potential capabilities include code generation, mathematical problem-solving, scientific analysis, document processing, and advanced information retrieval. Building on previous Grok models, it may support tool calling, extended context processing, real-time information access, and multistep task execution. Grok 5 could be used for AI assistants, software development, enterprise automation, technical research, and productivity applications. The model has not been officially released, and its architecture, context window, performance benchmarks, pricing, and availability remain unconfirmed. -
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OpenAI Bel
OpenAI
OpenAI Bel is the reported codename of an unreleased artificial intelligence foundation model associated with OpenAI. Unofficial reports describe it as a large-scale pretraining project intended to support future generations of AI models. The project has been linked in speculative reporting to OpenAI's Astra model family and potential successors to GPT-6. Some sources claim that Bel contains more than 10 trillion parameters, although this figure has not been independently verified. OpenAI has not published official technical specifications, benchmark results, or documentation confirming the project's capabilities. Bel is not currently available as a publicly documented model through ChatGPT or the OpenAI API.
