French AI startup Mistral has weathered a rocky period of public questioning over the last year to emerge, now here in December 2025, with new, crowd-pleasing models for enterprise and indie developers.Just days after releasing its <a href="https://venturebeat.com/ai/mistral-launches-mistral-3-a-family-of-open-models-designed-to-run-on">powerful open source, general purpose Mistral 3 LLM family</a> for edge devices and local hardware, the <a href="https://mistral.ai/news/devstral-2-vibe-cli">company returned today to debut Devstral 2</a>.The release includes a new pair of models optimized for software engineering tasks — again, with one small enough to run on a single laptop, offline and privately — alongside Mistral Vibe, a command-line interface (CLI) agent designed to allow developers to call the models up directly within their terminal environments. The models are fast, lean, and open—at least in theory. But the real story lies not just in the benchmarks, but in how Mistral is packaging this capability: one model fully free, another conditionally so, and a terminal interface built to scale with either.It’s an attempt not just to match proprietary systems like Claude and GPT-4 in performance, but to compete with them on developer experience—and to do so while holding onto the flag of open-source.Both models are available now for free for a limited time <a href="https://docs.mistral.ai/models/devstral-2-25-12">via Mistral’s API</a> and <a href="https://huggingface.co/collections/mistralai/devstral-2">Hugging Face</a>. The full Devstral 2 model is supported out-of-the-box in the community inference provider <a href="https://x.com/vllm_project/status/1998428798891765926?s=20">vLLM</a> and on the open source agentic coding platform <a href="https://x.com/kilocode/status/1998412042357588461">Kilo Code</a>. <h2>A Coding Model Meant to Drive</h2>At the top of the announcement is Devstral 2, a 123-billion parameter dense transformer with a 256K-token context window, engineered specifically for agentic software development. Mistral says the model achieves 72.2% on SWE-bench Verified, a benchmark designed to evaluate long-context software engineering tasks in real-world repositories.The smaller sibling, Devstral Small 2, weighs in at 24B parameters, with the same long context window and a performance of 68.0% on SWE-bench. On paper, that makes it the strongest open-weight model of its size, even outscoring many 70B-class competitors.But the performance story isn’t just about raw percentages. Mistral is betting that efficient intelligence beats scale, and has made much of the fact that Devstral 2 is:<ul><li>5× smaller than DeepSeek V3.2</li><li>8× smaller than Kimi K2</li><li>Yet still matches or surpasses them on key software reasoning benchmarks.</li></ul>Human evaluations back this up. In side-by-side comparisons:<ul><li>Devstral 2 beat DeepSeek V3.2 in 42.8% of tasks, losing only 28.6%.</li><li>Against Claude Sonnet 4.5, it lost more often (53.1%)—a reminder that while the gap is narrowing, closed models still lead in overall preference.</li></ul>Still, for an open-weight model, these results place Devstral 2 at the frontier of what’s currently available to run and modify independently.<h2>Vibe CLI: A Terminal-Native Agent</h2>Alongside the models, Mistral released Vibe CLI, a command-line assistant that integrates directly with Devstral models. It’s not an IDE plugin or a ChatGPT-style code explainer. It’s a native interface designed for project-wide code understanding and orchestration, built to live inside the developer’s actual workflow.Vibe brings a surprising degree of intelligence to the terminal:<ul><li>It reads your file tree and Git status to understand project scope.</li><li>It lets you reference files with @, run shell commands with !, and toggle behavior with slash commands.</li><li>It orchestrates changes across multiple files, tracks dependencies, retries failed executions, and can even refactor at architectural scale.</li></ul>Unlike most developer agents, which simulate a REPL from within a chat UI, Vibe starts with the shell and pulls intelligence in from there. It’s programmable, scriptable, and themeable. And it’s released under the Apache 2.0 license, meaning it’s truly free to use—in commercial settings, internal tools, or open-source extensions.<h2>Licensing Structure: Open-ish — With Revenue Limitations</h2>At first glance, Mistral’s licensing approach appears straightforward: the models are open-weight and publicly available. But a closer look reveals a line drawn through the middle of the release, with different rules for different users.Devstral Small 2, the 24-billion parameter variant, is covered under a standard, enterprise- and developer-friendly <a href="https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md">Apache 2.0 license</a>. That’s a gold standard in open-source: no revenue restrictions, no fine print, no need to check with legal. Enterprises can use it in production, embed it into products, and redistribute fine-tuned versions without asking for permission.Devstral 2, the flagship 123B model, is released under what Mistral calls a “<a href="https://huggingface.co/mistralai/Devstral-2-123B-Instruct-2512/blob/main/LICENSE">modified MIT license</a>.” That phrase sounds innocuous, but the modification introduces a critical limitation: any company making more than $20 million in monthly revenue cannot use the model at all—not even internally—without securing a separate commercial license from Mistral.<blockquote>“You are not authorized to exercise any rights under this license if the global consolidated monthly revenue of your company […] exceeds $20 million,” the license reads.</blockquote>The clause applies not only to the base model, but to derivatives, fine-tuned versions, and redistributed variants, regardless of who hosts them. In effect, it means that while the weights are “open,” their use is gated for large enterprises—unless they’re willing to engage with Mistral’s sales team or use the hosted API at metered pricing.To draw an analogy: Apache 2.0 is like a public library—you walk in, borrow the book, and use it however you need. Mistral’s modified MIT license is more like a corporate co-working space that’s free for freelancers but charges rent once your company hits a certain size.<h2>Weighing Devstral Small 2 for Enterprise Use</h2>This division raises an obvious question for larger companies: can Devstral Small 2 with its more permissive and unrestricted Apache 2.0 licensing serve as a viable alternative for medium-to-large enterprises?The answer depends on context. Devstral Small 2 scores 68.0% on SWE-bench, significantly ahead of many larger open models, and remains deployable on single-GPU or CPU-only setups. For teams focused on:<ul><li>internal tooling,</li><li>on-prem deployment,</li><li>low-latency edge inference,…it offers a rare combination of legality, performance, and convenience.</li></ul>But the performance gap from Devstral 2 is real. For multi-agent setups, deep monorepo refactoring, or long-context code analysis, that 4-point benchmark delta may understate the actual experience difference.For most enterprises, Devstral Small 2 will serve either as a low-friction way to prototype—or as a pragmatic bridge until licensing for Devstral 2 becomes feasible. It is not a drop-in replacement for the flagship, but it may be “good enough” in specific production slices, particularly when paired with Vibe CLI.But because Devstral Small 2 can be run entirely offline — including on a single GPU machine or a sufficiently specced laptop — it unlocks a critical use case for developers and teams operating in tightly controlled environments. Whether you’re a solo indie building tools on the go, or part of a company with strict data governance or compliance mandates, the ability to run a performant, long-context coding model without ever hitting the internet is a powerful differentiator. No cloud calls, no third-party telemetry, no risk of data leakage — just local inference with full visibility and control.This matters in industries like finance, healthcare, defense, and advanced manufacturing, where data often cannot leave the network perimeter. But it’s just as useful for developers who prefer autonomy over vendor lock-in — or who want their tools to work the same on a plane, in the field, or inside an air-gapped lab. In a market where most top-tier code models are delivered as API-only SaaS products, Devstral Small 2 offers a rare level of portability, privacy, and ownership.In that sense, Mistral isn’t just offering open models—they’re offering multiple paths to adoption, depending on your scale, compliance posture, and willingness to engage.<h2>Integration, Infrastructure, and Access</h2>From a technical standpoint, Mistral’s models are built for deployment. Devstral 2 requires a minimum of 4× H100-class GPUs, and is already available on build.nvidia.com. Devstral Small 2 can run on a single GPU or CPU such as those in a standard laptop, making it accessible to solo developers and embedded teams alike.Both models support quantized FP4 and FP8 weights, and are compatible with vLLM for scalable inference. Fine-tuning is supported out of the box.API pricing—after the free introductory window—follows a token-based structure:<ul><li>Devstral 2: $0.40 per million input tokens / $2.00 for output</li><li>Devstral Small 2: $0.10 input / $0.30 output</li></ul>That pricing sits just below OpenAI’s GPT-4 Turbo, and well below Anthropic’s Claude Sonnet at comparable performance levels.<h2>Developer Reception: Ground-Level Buzz</h2>On X (formerly Twitter), developers reacted quickly with a wave of positive reception, with Hugging Face&#x27;s Head of Product <a href="https://x.com/victormustar/status/1998414127400923246">Victor Mustar asking</a> if the small, Apache 2.0 licensed variant was the &quot;new local coding king,&quot; i.e., the one developers could use to run on their laptops directly and privately, without an internet connection:<div></div>Another popular AI news and rumors account, TestingCatalogNews, posted that it was &quot;SOTTA in coding,&quot; or &quot;State Of The Tiny Art&quot;<div></div>Another user, <a href="https://x.com/xlr8harder/status/1998458990565396505">@xlr8harder</a>, took issue with the custom licensing terms for Devstral 2, writing &quot;calling the Devstral 2 license &#x27;modified MIT&#x27; is misleading at best. It’s a proprietary license with MIT-like attribution requirements.&quot;<div></div>While the tone was critical, it reflected some attention Mistral’s license structuring was receiving, particularly among developers familiar with open-use norms.<h2>Strategic Context: From Codestral to Devstral and Mistral 3</h2>Mistral’s steady push into software development tools didn’t start with Devstral 2—it began in May 2024 with <a href="https://venturebeat.com/ai/mistral-announces-codestral-its-first-programming-focused-ai-model">Codestral</a>, the company’s first code-focused large language model. A 22-billion parameter system trained on more than 80 programming languages, Codestral was designed for use in developer environments ranging from basic autocompletions to full function generation. The model launched under a non-commercial license but still outperformed heavyweight competitors like CodeLlama 70B and Deepseek Coder 33B in early benchmarks such as HumanEval and RepoBench.Codestral’s release marked Mistral’s first move into the competitive coding-model space, but it also established a now-familiar pattern: technically lean models with surprisingly strong results, a wide context window, and licensing choices that invited developer experimentation. Industry partners including JetBrains, LlamaIndex, and LangChain quickly began integrating the model into their workflows, citing its speed and tool compatibility as key differentiators.One year later, the company followed up with <a href="https://venturebeat.com/ai/mistral-ai-launches-devstral-powerful-new-open-source-swe-agent-model-that-runs-on-laptops">Devstral</a>, a 24B model purpose-built for “agentic” behavior—handling long-range reasoning, file navigation, and autonomous code modification. Released in partnership with All Hands AI and licensed under Apache 2.0, Devstral was notable not just for its portability (it could run on a MacBook or RTX 4090), but for its performance: it beat out several closed models on SWE-Bench Verified, a benchmark of 500 real-world GitHub issues.Then came Mistral 3, announced in December 2025 as <a href="https://venturebeat.com/ai/mistral-launches-mistral-3-a-family-of-open-models-designed-to-run-on">a portfolio of 10 open-weight models</a> targeting everything from drones and smartphones to cloud infrastructure. This suite included both high-end models like Mistral Large 3 (a MoE system with 41 active parameters and 256K context) and lightweight “Ministral” variants that could run on 4GB of VRAM. All were licensed under Apache 2.0, reinforcing Mistral’s commitment to flexible, edge-friendly deployment.Mistral 3 positioned the company not as a direct competitor to frontier models like GPT-5 or Gemini 3, but as a developer-first platform for customized, localized AI systems. Co-founder Guillaume Lample described the vision as “distributed intelligence”—many smaller systems tuned for specific tasks and running outside centralized infrastructure. “In more than 90% of cases, a small model can do the job,” he told VentureBeat. “It doesn’t have to be a model with hundreds of billions of parameters.”That broader strategy helps explain the significance of Devstral 2. It’s not a one-off release but a continuation of Mistral’s long-running commitment to code agents, local-first deployment, and open-weight availability—an ecosystem that began with Codestral, matured through Devstral, and scaled up with Mistral 3. Devstral 2, in this framing, is not just a model. It’s the next version of a playbook that’s been unfolding in public for over a year.<h2>Final Thoughts (For Now): A Fork in the Road</h2>With Devstral 2, Devstral Small 2, and Vibe CLI, Mistral AI has drawn a clear map for developers and companies alike. The tools are fast, capable, and thoughtfully integrated. But they also present a choice—not just in architecture, but in how and where you’re allowed to use them.If you’re an individual developer, small startup, or open-source maintainer, this is one of the most powerful AI systems you can freely run today. If you’re a Fortune 500 engineering lead, you’ll need to either talk to Mistral—or settle for the smaller model and make it work.In a market increasingly dominated by black-box models and SaaS lock-ins, Mistral’s offer is still a breath of fresh air. Just read the fine print before you start building.

أطلقت شركة ميسترال الفرنسية الناشئة في مجال الذكاء الاصطناعي نموذج البرمجة Devstral 2، الذي يتضمن إصدارًا مناسبًا لأجهزة الكمبيوتر المحمولة، مُحسّنًا لمهام هندسة البرمجيات. يأتي هذا الإطلاق بعد تقديم عائلة نماذج Mistral 3، التي تهدف إلى تحسين قدرات الأجهزة المحلية للمطورين.

La startup francesa de IA Mistral ha lanzado el modelo de codificación Devstral 2, que incluye una versión amigable para laptops optimizada para tareas de ingeniería de software. Este lanzamiento sigue a la introducción de la familia de modelos Mistral 3, destinada a mejorar las capacidades del hardware local para desarrolladores.

La startup française d'IA Mistral a lancé le modèle de codage Devstral 2, qui comprend une version adaptée aux ordinateurs portables, optimisée pour les tâches d'ingénierie logicielle. Ce lancement fait suite à l'introduction de la famille de modèles Mistral 3, visant à améliorer les capacités du matériel local pour les développeurs.

French AI startup Mistral has launched the Devstral 2 coding model, which includes a laptop-friendly version optimized for software engineering tasks. This release follows the introduction of the Mistral 3 LLM family, aimed at enhancing local hardware capabilities for developers.

Mistral launches powerful Devstral 2 coding model including open source, laptop-friendly version

<a href="https://news.ycombinator.com/item?id=46205437">Comments</a>

أعلنت شركة ميسترال عن إطلاق Devstral2 وMistral Vibe CLI، مما يعزز عروضها في مجالات الذكاء الاصطناعي وتطوير البرمجيات. يأتي هذا الإطلاق كجزء من التزام ميسترال المستمر بتوفير أدوات مبتكرة للمطورين وتحسين كفاءة ممارسات البرمجة.

Mistral ha anunciado el lanzamiento de Devstral2 y Mistral Vibe CLI, mejorando así su oferta en los sectores de IA y desarrollo de software. Este lanzamiento es parte del compromiso continuo de Mistral de proporcionar herramientas innovadoras para desarrolladores y mejorar la eficiencia de las prácticas de codificación.

Mistral a annoncé le lancement de Devstral2 et de Mistral Vibe CLI, renforçant ainsi ses offres dans les secteurs de l'IA et du développement logiciel. Ce lancement fait partie de l'engagement continu de Mistral à fournir des outils innovants pour les développeurs et à améliorer l'efficacité des pratiques de codage.

Mistral has announced the release of Devstral2 and Mistral Vibe CLI, enhancing its offerings in the AI and software development sectors. This launch is part of Mistral's ongoing commitment to provide innovative tools for developers and improve the efficiency of coding practices.

Mistral releases Devstral2 and Mistral Vibe CLI

Chinese AI startup Zhipu AI aka <a href="https://z.ai/blog/glm-4.6v">Z.ai has released its GLM-4.6V series</a>, a new generation of open-source vision-language models (VLMs) optimized for multimodal reasoning, frontend automation, and high-efficiency deployment. The release includes two models in &quot;large&quot; and &quot;small&quot; sizes: <ol><li>GLM-4.6V (106B), a larger 106-billion parameter model aimed at cloud-scale inference</li><li>GLM-4.6V-Flash (9B), a smaller model of only 9 billion parameters designed for low-latency, local applications</li></ol>Recall that generally speaking, models with more parameters — or internal settings governing their behavior, i.e. weights and biases — are more powerful, performant, and capable of performing at a higher general level across more varied tasks.However, smaller models can offer better efficiency for edge or real-time applications where latency and resource constraints are critical.The defining innovation in this series is the introduction of native function calling in a vision-language model—enabling direct use of tools such as search, cropping, or chart recognition with visual inputs. With a 128,000 token context length (equivalent to a 300-page novel&#x27;s worth of text exchanged in a single input/output interaction with the user) and state-of-the-art (SoTA) results across more than 20 benchmarks, the GLM-4.6V series positions itself as a highly competitive alternative to both closed and open-source VLMs. It&#x27;s available in the following formats:<ul><li><a href="https://docs.z.ai/guides/vlm/glm-4.6v">API access</a> via OpenAI-compatible interface</li><li><a href="https://chat.z.ai">Try the demo</a> on Zhipu’s web interface</li><li><a href="https://huggingface.co/collections/zai-org/glm-46v">Download weights</a> from Hugging Face</li><li>Desktop assistant app available on <a href="https://huggingface.co/spaces/zai-org/GLM-4.5V-Demo-App">Hugging Face Spaces</a></li></ul><h2>Licensing and Enterprise Use</h2>GLM‑4.6V and GLM‑4.6V‑Flash are distributed under the <a href="https://opensource.org/licenses/MIT">MIT license</a>, a permissive open-source license that allows free commercial and non-commercial use, modification, redistribution, and local deployment without obligation to open-source derivative works. This licensing model makes the series suitable for enterprise adoption, including scenarios that require full control over infrastructure, compliance with internal governance, or air-gapped environments.Model weights and documentation are publicly hosted on <a href="https://huggingface.co/collections/zai-org/glm-46v">Hugging Face</a>, with supporting code and tooling available on <a href="https://github.com/zai-org/GLM-V">GitHub</a>. The MIT license ensures maximum flexibility for integration into proprietary systems, including internal tools, production pipelines, and edge deployments.<h2>Architecture and Technical Capabilities</h2>The GLM-4.6V models follow a conventional encoder-decoder architecture with significant adaptations for multimodal input. Both models incorporate a Vision Transformer (ViT) encoder—based on AIMv2-Huge—and an MLP projector to align visual features with a large language model (LLM) decoder. Video inputs benefit from 3D convolutions and temporal compression, while spatial encoding is handled using 2D-RoPE and bicubic interpolation of absolute positional embeddings.A key technical feature is the system’s support for arbitrary image resolutions and aspect ratios, including wide panoramic inputs up to 200:1. In addition to static image and document parsing, GLM-4.6V can ingest temporal sequences of video frames with explicit timestamp tokens, enabling robust temporal reasoning.On the decoding side, the model supports token generation aligned with function-calling protocols, allowing for structured reasoning across text, image, and tool outputs. This is supported by extended tokenizer vocabulary and output formatting templates to ensure consistent API or agent compatibility.<h2>Native Multimodal Tool Use</h2>GLM-4.6V introduces native multimodal function calling, allowing visual assets—such as screenshots, images, and documents—to be passed directly as parameters to tools. This eliminates the need for intermediate text-only conversions, which have historically introduced information loss and complexity.The tool invocation mechanism works bi-directionally:<ul><li>Input tools can be passed images or videos directly (e.g., document pages to crop or analyze).</li><li>Output tools such as chart renderers or web snapshot utilities return visual data, which GLM-4.6V integrates directly into the reasoning chain.</li></ul>In practice, this means GLM-4.6V can complete tasks such as:<ul><li>Generating structured reports from mixed-format documents</li><li>Performing visual audit of candidate images</li><li>Automatically cropping figures from papers during generation</li><li>Conducting visual web search and answering multimodal queries</li></ul><h2>High Performance Benchmarks Compared to Other Similar-Sized Models</h2>GLM-4.6V was evaluated across more than 20 public benchmarks covering general VQA, chart understanding, OCR, STEM reasoning, frontend replication, and multimodal agents. According to the benchmark chart released by Zhipu AI:<ul><li>GLM-4.6V (106B) achieves SoTA or near-SoTA scores among open-source models of comparable size (106B) on MMBench, MathVista, MMLongBench, ChartQAPro, RefCOCO, TreeBench, and more.</li><li>GLM-4.6V-Flash (9B) outperforms other lightweight models (e.g., Qwen3-VL-8B, GLM-4.1V-9B) across almost all categories tested.</li><li>The 106B model’s 128K-token window allows it to outperform larger models like Step-3 (321B) and Qwen3-VL-235B on long-context document tasks, video summarization, and structured multimodal reasoning.</li></ul>Example scores from the leaderboard include:<ul><li>MathVista: 88.2 (GLM-4.6V) vs. 84.6 (GLM-4.5V) vs. 81.4 (Qwen3-VL-8B)</li><li>WebVoyager: 81.0 vs. 68.4 (Qwen3-VL-8B)</li><li>Ref-L4-test: 88.9 vs. 89.5 (GLM-4.5V), but with better grounding fidelity at 87.7 (Flash) vs. 86.8</li></ul>Both models were evaluated using the vLLM inference backend and support SGLang for video-based tasks.<h2>Frontend Automation and Long-Context Workflows</h2>Zhipu AI emphasized GLM-4.6V’s ability to support frontend development workflows. The model can:<ul><li>Replicate pixel-accurate HTML/CSS/JS from UI screenshots</li><li>Accept natural language editing commands to modify layouts</li><li>Identify and manipulate specific UI components visually</li></ul>This capability is integrated into an end-to-end visual programming interface, where the model iterates on layout, design intent, and output code using its native understanding of screen captures.In long-document scenarios, GLM-4.6V can process up to 128,000 tokens—enabling a single inference pass across:<ul><li>150 pages of text (input)</li><li>200 slide decks</li><li>1-hour videos</li></ul>Zhipu AI reported successful use of the model in financial analysis across multi-document corpora and in summarizing full-length sports broadcasts with timestamped event detection.<h2>Training and Reinforcement Learning</h2>The model was trained using multi-stage pre-training followed by supervised fine-tuning (SFT) and reinforcement learning (RL). Key innovations include:<ul><li>Curriculum Sampling (RLCS): Dynamically adjusts the difficulty of training samples based on model progress</li><li>Multi-domain reward systems: Task-specific verifiers for STEM, chart reasoning, GUI agents, video QA, and spatial grounding</li><li>Function-aware training: Uses structured tags (e.g., &lt;think&gt;, &lt;answer&gt;, &lt;|begin_of_box|&gt;) to align reasoning and answer formatting</li></ul>The reinforcement learning pipeline emphasizes verifiable rewards (RLVR) over human feedback (RLHF) for scalability, and avoids KL/entropy losses to stabilize training across multimodal domains<h2>Pricing (API)</h2>Zhipu AI offers competitive pricing for the GLM-4.6V series, with both the flagship model and its lightweight variant positioned for high accessibility.<ul><li>GLM-4.6V: $0.30 (input) / $0.90 (output) per 1M tokens</li><li>GLM-4.6V-Flash: Free</li></ul>Compared to major vision-capable and text-first LLMs, GLM-4.6V is among the most cost-efficient for multimodal reasoning at scale. Below is a comparative snapshot of pricing across providers:USD per 1M tokens — sorted lowest → highest total cost<table><tbody><tr><td>Model</td><td>Input</td><td>Output</td><td>Total Cost</td><td>Source</td></tr><tr><td>Qwen 3 Turbo</td><td>$0.05</td><td>$0.20</td><td>$0.25</td><td><a href="https://www.alibabacloud.com/en/campaign/qwen-ai-landing-page?_p_lc=1&amp;src=qwenai">Alibaba Cloud</a></td></tr><tr><td>ERNIE 4.5 Turbo</td><td>$0.11</td><td>$0.45</td><td>$0.56</td><td><a href="https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Blfmc9do4">Qianfan</a></td></tr><tr><td>GLM‑4.6V</td><td>$0.30</td><td>$0.90</td><td>$1.20</td><td><a href="https://docs.z.ai/guides/overview/pricing">Z.AI</a></td></tr><tr><td>Grok 4.1 Fast (reasoning)</td><td>$0.20</td><td>$0.50</td><td>$0.70</td><td><a href="https://docs.x.ai/docs/models?cluster=us-east-1#detailed-pricing-for-all-grok-models">xAI</a></td></tr><tr><td>Grok 4.1 Fast (non-reasoning)</td><td>$0.20</td><td>$0.50</td><td>$0.70</td><td><a href="https://docs.x.ai/docs/models?cluster=us-east-1#detailed-pricing-for-all-grok-models">xAI</a></td></tr><tr><td>deepseek-chat (V3.2-Exp)</td><td>$0.28</td><td>$0.42</td><td>$0.70</td><td><a href="https://api-docs.deepseek.com/quick_start/pricing">DeepSeek</a></td></tr><tr><td>deepseek-reasoner (V3.2-Exp)</td><td>$0.28</td><td>$0.42</td><td>$0.70</td><td><a href="https://api-docs.deepseek.com/quick_start/pricing">DeepSeek</a></td></tr><tr><td>Qwen 3 Plus</td><td>$0.40</td><td>$1.20</td><td>$1.60</td><td><a href="https://www.alibabacloud.com/en/campaign/qwen-ai-landing-page?_p_lc=1&amp;src=qwenai">Alibaba Cloud</a></td></tr><tr><td>ERNIE 5.0</td><td>$0.85</td><td>$3.40</td><td>$4.25</td><td><a href="https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Blfmc9do4">Qianfan</a></td></tr><tr><td>Qwen-Max</td><td>$1.60</td><td>$6.40</td><td>$8.00</td><td><a href="https://www.alibabacloud.com/en/campaign/qwen-ai-landing-page?_p_lc=1&amp;src=qwenai">Alibaba Cloud</a></td></tr><tr><td>GPT-5.1</td><td>$1.25</td><td>$10.00</td><td>$11.25</td><td><a href="https://openai.com/pricing">OpenAI</a></td></tr><tr><td>Gemini 2.5 Pro (≤200K)</td><td>$1.25</td><td>$10.00</td><td>$11.25</td><td><a href="https://ai.google.dev/gemini-api/docs/pricing">Google</a></td></tr><tr><td>Gemini 3 Pro (≤200K)</td><td>$2.00</td><td>$12.00</td><td>$14.00</td><td><a href="https://ai.google.dev/gemini-api/docs/pricing">Google</a></td></tr><tr><td>Gemini 2.5 Pro (&gt;200K)</td><td>$2.50</td><td>$15.00</td><td>$17.50</td><td><a href="https://ai.google.dev/gemini-api/docs/pricing">Google</a></td></tr><tr><td>Grok 4 (0709)</td><td>$3.00</td><td>$15.00</td><td>$18.00</td><td><a href="https://docs.x.ai/docs/models?cluster=us-east-1#detailed-pricing-for-all-grok-models">xAI</a></td></tr><tr><td>Gemini 3 Pro (&gt;200K)</td><td>$4.00</td><td>$18.00</td><td>$22.00</td><td><a href="https://ai.google.dev/gemini-api/docs/pricing">Google</a></td></tr><tr><td>Claude Opus 4.1</td><td>$15.00</td><td>$75.00</td><td>$90.00</td><td><a href="https://docs.anthropic.com/claude/docs/models-overview">Anthropic</a></td></tr></tbody></table><h2>Previous Releases: GLM‑4.5 Series and Enterprise Applications</h2>Prior to GLM‑4.6V, Z.ai released the GLM‑4.5 family in mid-2025, establishing the company as a serious contender in open-source LLM development. The flagship GLM‑4.5 and its smaller sibling GLM‑4.5‑Air both support reasoning, tool use, coding, and agentic behaviors, while offering strong performance across standard benchmarks. The models introduced dual reasoning modes (“thinking” and “non-thinking”) and could automatically generate complete PowerPoint presentations from a single prompt — a feature positioned for use in enterprise reporting, education, and internal comms workflows. Z.ai also extended the GLM‑4.5 series with additional variants such as GLM‑4.5‑X, AirX, and Flash, targeting ultra-fast inference and low-cost scenarios.Together, these features position the GLM‑4.5 series as a cost-effective, open, and production-ready alternative for enterprises needing autonomy over model deployment, lifecycle management, and integration pipel<h2>Ecosystem Implications</h2>The GLM-4.6V release represents a notable advance in open-source multimodal AI. While large vision-language models have proliferated over the past year, few offer:<ul><li>Integrated visual tool usage</li><li>Structured multimodal generation</li><li>Agent-oriented memory and decision logic</li></ul>Zhipu AI’s emphasis on “closing the loop” from perception to action via native function calling marks a step toward agentic multimodal systems. The model’s architecture and training pipeline show a continued evolution of the GLM family, positioning it competitively alongside offerings like OpenAI’s GPT-4V and Google DeepMind’s Gemini-VL.<h2>Takeaway for Enterprise Leaders</h2>With GLM-4.6V, Zhipu AI introduces an open-source VLM capable of native visual tool use, long-context reasoning, and frontend automation. It sets new performance marks among models of similar size and provides a scalable platform for building agentic, multimodal AI systems.

أطلقت شركة Zhipu AI الصينية، المعروفة أيضًا باسم Z.ai، سلسلة GLM-4.6V، وهي جيل جديد من نماذج اللغة-الرؤية مفتوحة المصدر، المحسّنة للتفكير متعدد الوسائط والنشر الفعال. تشمل السلسلة نموذجين: GLM-4.6V الذي يحتوي على 106 مليار معلمة للتفسير على نطاق السحابة وGLM-4.6V-Flash الذي يحتوي على 9 مليارات معلمة للتطبيقات ذات الكمون المنخفض.

La startup china Zhipu AI, conocida también como Z.ai, ha lanzado su serie GLM-4.6V, una nueva generación de modelos de lenguaje-visual de código abierto optimizados para el razonamiento multimodal y el despliegue eficiente. La serie incluye dos modelos: GLM-4.6V con 106 mil millones de parámetros para inferencia a escala en la nube y GLM-4.6V-Flash con 9 mil millones de parámetros para aplicaciones de baja latencia.

La startup chinoise Zhipu AI, également connue sous le nom de Z.ai, a lancé sa série GLM-4.6V, une nouvelle génération de modèles de vision-langage open-source optimisés pour le raisonnement multimodal et le déploiement efficace. La série comprend deux modèles : GLM-4.6V avec 106 milliards de paramètres pour l'inférence à l'échelle du cloud et GLM-4.6V-Flash avec 9 milliards de paramètres pour des applications à faible latence.

Chinese AI startup Zhipu AI, also known as Z.ai, has launched its GLM-4.6V series, a new generation of open-source vision-language models optimized for multimodal reasoning and efficient deployment. The series includes two models: GLM-4.6V with 106 billion parameters for cloud-scale inference and GLM-4.6V-Flash with 9 billion parameters for low-latency applications.

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