%PDF-1.4 %âãÏÓ 1 0 obj << /Type /Catalog /Pages 2 0 R >> endobj 2 0 obj << /Type /Pages /Count 5 /Kids [5 0 R 7 0 R 9 0 R 11 0 R 13 0 R] >> endobj 3 0 obj << /Type /Font /Subtype /Type1 /BaseFont /Helvetica >> endobj 4 0 obj << /Type /Font /Subtype /Type1 /BaseFont /Helvetica-Bold >> endobj 5 0 obj << /Type /Page /Parent 2 0 R /MediaBox [0 0 595.28 841.89] /Resources << /Font << /F1 3 0 R /F2 4 0 R >> >> /Contents 6 0 R >> endobj 6 0 obj << /Length 5594 >> stream BT /F2 22 Tf 0.06 0.08 0.12 rg 1 0 0 1 46 789.89 Tm (An In-Depth Comparative Analysis of GLM-5,) Tj ET BT /F2 22 Tf 0.06 0.08 0.12 rg 1 0 0 1 46 762.89 Tm (MiniMax M2.5, and Kimi K2.5) Tj ET BT /F2 11 Tf 0.72 0.14 0.18 rg 1 0 0 1 46 725.89 Tm (TechRounder PDF Edition) Tj ET BT /F1 9.5 Tf 0.36 0.39 0.46 rg 1 0 0 1 46 709.89 Tm (Live article:) Tj ET BT /F1 9.5 Tf 0.36 0.39 0.46 rg 1 0 0 1 46 697.39 Tm (https://www.techrounder.com/ai/an-in-depth-comparative-analysis-of-glm-5-minimax-m2-5-and-kimi-k2-5/) Tj ET q 0.82 0.85 0.9 RG 1 w 46 678.89 m 549.28 678.89 l S Q BT /F1 10 Tf 0.24 0.27 0.32 rg 1 0 0 1 46 666.89 Tm (By Vipin PG | Published February 14, 2026 | Updated February 14, 2026 | Format: Analysis | 5 min read) Tj ET BT /F2 13 Tf 0.72 0.14 0.18 rg 1 0 0 1 46 643.89 Tm (In brief) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 623.89 Tm (GLM-5, MiniMax M2.5, and Kimi K2.5 are 2026 open-weight frontier models built for long-horizon,) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 608.89 Tm (agentic work, not just chat. Choose GLM-5 for heavy systems reasoning with sparse attention, M2.5) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 593.89 Tm (for fast, low-cost high-volume execution, and K2.5 for strongest native multimodal \(text+vision\)) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 578.89 Tm (performance with a huge 256k context.) Tj ET BT /F2 13 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 551.89 Tm (Key points) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 531.89 Tm (The article argues that the early 2026 release cycle marks a structural shift in AI, with open-weight) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 516.89 Tm (flagships like Zhipu AI's GLM-5, MiniMax M2.5, and Moonshot AI's Kimi K2.5 pushing the industry) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 501.89 Tm (beyond simple conversational copilots toward autonomous, long-horizon agentic engineering. It says) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 486.89 Tm (these aren't incremental upgrades but a coordinated leap in architecture, reinforcement learning,) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 471.89 Tm (multimodal integration, and cost efficiency, with benchmark performance that can match or even) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 456.89 Tm (surpass proprietary systems such as Claude Opus 4.6 and GPT-5.2. The piece frames itself as a) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 441.89 Tm (technical and strategic comparison spanning architecture, post-training reinforcement systems,) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 426.89 Tm (benchmark results, deployment economics, and enterprise use cases. As a concrete example of the) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 411.89 Tm (new scale-and-efficiency focus, it highlights GLM-5 as a 744B-parameter Mixture-of-Experts model) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 396.89 Tm (with 40B active parameters per token, a 200,000-token context window, 28.5T training tokens, and) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 381.89 Tm (DeepSeek Sparse Attention to make massive contexts practical without extreme latency or memory) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 366.89 Tm (cost.) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 344.89 Tm (The early 2026 AI release cycle has triggered a structural shift across the global artificial) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 329.89 Tm (intelligence landscape. Three flagship open-weight models - GLM-5 from Zhipu AI, MiniMax M2.5, and) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 314.89 Tm (Kimi K2.5 from Moonshot AI - have redefined what frontier intelligence looks like. This is no longer) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 299.89 Tm (about conversational "copilots." The industry is transitioning toward autonomous, long-horizon agentic) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 284.89 Tm (engineering.) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 262.89 Tm (These models are not incremental upgrades. They represent a coordinated leap in architecture,) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 247.89 Tm (reinforcement learning, multimodal integration, and economic efficiency. Across several standardized) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 232.89 Tm (benchmark suites, they match or surpass proprietary systems such as Claude Opus 4.6 and GPT-5.2.) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 210.89 Tm (This article presents a complete technical and strategic comparison of GLM-5, MiniMax M2.5, and Kimi) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 195.89 Tm (K2.5 - covering architecture, post-training reinforcement systems, benchmarking results, deployment) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 180.89 Tm (economics, and enterprise use cases.) Tj ET BT /F2 15 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 152.89 Tm (Architectural Foundations and Pre-Training Scale) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 128.89 Tm (The February 2026 generation is defined not just by larger training corpora, but by deep architectural) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 113.89 Tm (optimization. Massive context windows must now be served efficiently without extreme latency or) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 98.89 Tm (memory cost.) Tj ET q 0.86 0.88 0.92 RG 1 w 46 42 m 549.28 42 l S Q BT /F1 8.4 Tf 0.42 0.45 0.5 rg 1 0 0 1 46 30 Tm (TechRounder | Page 1 of 5) Tj ET BT /F1 7.2 Tf 0.42 0.45 0.5 rg 1 0 0 1 46 19 Tm (https://www.techrounder.com/pdf/blog/an-in-depth-comparative-analysis-of-glm-5-minimax-m2-5-and-kimi-k2-5.pdf) Tj ET endstream endobj 7 0 obj << /Type /Page /Parent 2 0 R /MediaBox [0 0 595.28 841.89] /Resources << /Font << /F1 3 0 R /F2 4 0 R >> >> /Contents 8 0 R >> endobj 8 0 obj << /Length 4897 >> stream BT /F2 13 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 789.89 Tm (GLM-5: Massive-Scale Systems Engineering) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 767.89 Tm (- Total Parameters: 744 billion \(Mixture-of-Experts\)) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 751.09 Tm (- Active Parameters per Token: 40 billion) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 734.29 Tm (- Context Window: 200,000 tokens) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 717.49 Tm (- Training Data: 28.5 trillion tokens) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 700.69 Tm (- Attention Mechanism: DeepSeek Sparse Attention \(DSA\)) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 683.89 Tm (GLM-5 more than doubles the parameter count of GLM-4.5. It replaces standard attention with) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 668.89 Tm (DeepSeek Sparse Attention to dramatically reduce memory overhead when handling large contexts.) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 653.89 Tm (The result is a model optimized for complex systems engineering and long-horizon reasoning.) Tj ET BT /F2 13 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 625.89 Tm (MiniMax M2.5: Efficiency-First Intelligence) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 603.89 Tm (- Total Parameters: 230 billion) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 587.09 Tm (- Active Parameters per Token: 10 billion) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 570.29 Tm (- Context Window: 205,000 tokens) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 553.49 Tm (- License: MIT) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 536.69 Tm (M2.5 takes a different path. Instead of brute scaling, it focuses on throughput, task decomposition) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 521.69 Tm (efficiency, and cost optimization. It is built for speed and affordability, enabling high-volume) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 506.69 Tm (autonomous execution at scale.) Tj ET BT /F2 13 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 478.69 Tm (Kimi K2.5: Native Multimodal Swarm Intelligence) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 456.69 Tm (- Total Parameters: 1 trillion \(MoE\)) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 439.89 Tm (- Active Parameters per Token: 32 billion) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 423.09 Tm (- Context Window: 256,000 tokens) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 406.29 Tm (- Architecture: 61-layer Transformer with Multi-head Latent Attention \(MLA\)) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 389.49 Tm (- Experts: 384 experts, 8 activated per token) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 372.69 Tm (- Training Data: 15 trillion multimodal tokens) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 355.89 Tm (- Vision Encoder: MoonViT \(400M parameters\)) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 339.09 Tm (Kimi K2.5 is natively multimodal. Vision and text were trained jointly from the beginning. Unlike) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 324.09 Tm (adapter-based systems, this avoids degradation when reasoning across images, video, and text) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 309.09 Tm (simultaneously.) Tj ET BT /F2 15 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 281.09 Tm (Post-Training Reinforcement Learning Frameworks) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 257.09 Tm (The 2026 generation solves one of the biggest historical limitations of large models: long-horizon) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 242.09 Tm (credit assignment.) Tj ET BT /F2 13 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 214.09 Tm (GLM-5: Slime Asynchronous Reinforcement Learning) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 192.09 Tm (The Slime infrastructure separates generation and evaluation into asynchronous pipelines. This) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 177.09 Tm (significantly increases training throughput and reduces hallucination rates. The model is trained to) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 162.09 Tm (abstain when uncertain, especially in complex codebases.) Tj ET BT /F2 13 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 134.09 Tm (MiniMax M2.5: Forge Framework) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 112.09 Tm (Forge is an agent-native reinforcement system that decouples training engines from agent logic. It) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 97.09 Tm (uses tree-structured merging for samples, achieving up to 40x training acceleration. A process) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 82.09 Tm (reward mechanism ensures optimal task decomposition.) Tj ET q 0.86 0.88 0.92 RG 1 w 46 42 m 549.28 42 l S Q BT /F1 8.4 Tf 0.42 0.45 0.5 rg 1 0 0 1 46 30 Tm (TechRounder | Page 2 of 5) Tj ET BT /F1 7.2 Tf 0.42 0.45 0.5 rg 1 0 0 1 46 19 Tm (https://www.techrounder.com/pdf/blog/an-in-depth-comparative-analysis-of-glm-5-minimax-m2-5-and-kimi-k2-5.pdf) Tj ET endstream endobj 9 0 obj << /Type /Page /Parent 2 0 R /MediaBox [0 0 595.28 841.89] /Resources << /Font << /F1 3 0 R /F2 4 0 R >> >> /Contents 10 0 R >> endobj 10 0 obj << /Length 4844 >> stream BT /F2 13 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 789.89 Tm (Kimi K2.5: Parallel-Agent Reinforcement Learning \(PARL\)) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 767.89 Tm (PARL trains Kimi K2.5 to function as an orchestrator rather than a single agent. It can autonomously) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 752.89 Tm (spawn up to 100 sub-agents and execute 1,500 parallel tool calls. Reward shaping prevents sequential) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 737.89 Tm (fallback and useless parallelism.) Tj ET BT /F2 15 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 709.89 Tm (Hardware Infrastructure and Geopolitical Resilience) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 685.89 Tm (GLM-5 represents a landmark shift in hardware independence. It was trained entirely on Huawei) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 670.89 Tm (Ascend chips using the MindSpore framework, without NVIDIA GPUs.) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 648.89 Tm (This challenges the assumption that export controls can permanently constrain frontier AI) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 633.89 Tm (development.) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 611.89 Tm (However, GLM-5's open weights require approximately 1,490GB of memory for deployment. Running) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 596.89 Tm (it locally demands datacenter-grade infrastructure, often requiring eight H200-class accelerators for) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 581.89 Tm (efficient FP8 inference.) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 559.89 Tm (MiniMax M2.5, by contrast, operates with far lower hardware overhead and can sustain 100 tokens) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 544.89 Tm (per second efficiently.) Tj ET BT /F2 15 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 516.89 Tm (Comprehensive Benchmarking Methodology) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 492.89 Tm (Evaluation was conducted using the Artificial Analysis Intelligence Index v4.0. The index includes:) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 470.89 Tm (- Agentic Workflows) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 454.09 Tm (- Coding and Software Engineering) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 437.29 Tm (- General Knowledge and Hallucination) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 420.49 Tm (- Scientific and Mathematical Reasoning) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 403.69 Tm (All evaluations used zero-shot prompting in Ubuntu 22.04 LTS with Python 3.12. Scores are based on) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 388.69 Tm (pass@1 metrics.) Tj ET BT /F2 15 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 360.69 Tm (Software Engineering Performance \(SWE-bench Verified\)) Tj ET BT /F1 10 Tf 0.18 0.2 0.24 rg 1 0 0 1 46 336.69 Tm (Model: Claude Opus 4.6 | SWE-bench Verified: 80.9% | SWE-bench Multilingual: 77.5%) Tj ET BT /F1 10 Tf 0.18 0.2 0.24 rg 1 0 0 1 46 319.69 Tm (Model: MiniMax M2.5 | SWE-bench Verified: 80.2% | SWE-bench Multilingual: -) Tj ET BT /F1 10 Tf 0.18 0.2 0.24 rg 1 0 0 1 46 302.69 Tm (Model: GLM-5 | SWE-bench Verified: 77.8% | SWE-bench Multilingual: 73.3%) Tj ET BT /F1 10 Tf 0.18 0.2 0.24 rg 1 0 0 1 46 285.69 Tm (Model: Kimi K2.5 | SWE-bench Verified: 76.8% | SWE-bench Multilingual: 73.0%) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 268.69 Tm (M2.5 reaches near parity with Claude Opus 4.6 while consuming fewer tokens and operating at 10%) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 253.69 Tm (of the cost. GLM-5 demonstrates strong multilingual robustness. Kimi K2.5 continues to improve in) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 238.69 Tm (structured code generation and debugging.) Tj ET BT /F2 15 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 210.69 Tm (Scientific and Mathematical Reasoning) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 186.69 Tm (In GPQA-Diamond and advanced mathematics benchmarks, Kimi K2.5 and GLM-5 show competitive) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 171.69 Tm (results against GPT-5.2 and Claude Opus 4.6.) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 149.69 Tm (When tool use is enabled in Humanity's Last Exam \(HLE\), both GLM-5 and Kimi K2.5 show massive) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 134.69 Tm (score jumps. This confirms the importance of agentic reinforcement frameworks in boosting) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 119.69 Tm (practical reasoning.) Tj ET BT /F2 15 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 91.69 Tm (Agentic Workflows and Office Productivity) Tj ET q 0.86 0.88 0.92 RG 1 w 46 42 m 549.28 42 l S Q BT /F1 8.4 Tf 0.42 0.45 0.5 rg 1 0 0 1 46 30 Tm (TechRounder | Page 3 of 5) Tj ET BT /F1 7.2 Tf 0.42 0.45 0.5 rg 1 0 0 1 46 19 Tm (https://www.techrounder.com/pdf/blog/an-in-depth-comparative-analysis-of-glm-5-minimax-m2-5-and-kimi-k2-5.pdf) Tj ET endstream endobj 11 0 obj << /Type /Page /Parent 2 0 R /MediaBox [0 0 595.28 841.89] /Resources << /Font << /F1 3 0 R /F2 4 0 R >> >> /Contents 12 0 R >> endobj 12 0 obj << /Length 4273 >> stream BT /F2 13 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 789.89 Tm (MiniMax M2.5) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 767.89 Tm (- 59% win rate in Cowork Agent evaluation) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 751.09 Tm (- 76.3% on BrowseComp) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 734.29 Tm (- Strong performance in Excel World Championship simulations) Tj ET BT /F2 13 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 711.49 Tm (Kimi K2.5) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 689.49 Tm (- 59.3% improvement over predecessor in AI Office Benchmark) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 672.69 Tm (- Excels in Word annotations, pivot tables, and long document synthesis) Tj ET BT /F2 13 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 649.89 Tm (GLM-5) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 627.89 Tm (- First among open models on Vending Bench 2) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 611.09 Tm (- Demonstrates long-horizon planning and macroeconomic simulation strength) Tj ET BT /F2 15 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 588.29 Tm (Long Context Processing) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 564.29 Tm (- GLM-5: 200K tokens) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 547.49 Tm (- MiniMax M2.5: 205K tokens) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 530.69 Tm (- Kimi K2.5: 256K tokens) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 513.89 Tm (Chinese-origin models demonstrate stronger robustness in Chinese long-context scenarios, while GPT) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 498.89 Tm (and Claude series show stronger English long-context alignment.) Tj ET BT /F2 15 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 470.89 Tm (Native Multimodality Advantage) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 446.89 Tm (Kimi K2.5 leads in multimodal reasoning:) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 424.89 Tm (- MMMU Pro: 78.5%) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 408.09 Tm (- MathVision: 84.2%) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 391.29 Tm (- VideoMMMU: 86.6%) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 374.49 Tm (It supports autonomous UI rendering, visual debugging, and vision-to-code pipelines. GLM relies on) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 359.49 Tm (GLM-4.5V for visual tasks, while MiniMax M2.5 currently lacks native image input support.) Tj ET BT /F2 15 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 331.49 Tm (Economic Analysis and API Pricing) Tj ET BT /F1 10 Tf 0.18 0.2 0.24 rg 1 0 0 1 46 307.49 Tm (Model: MiniMax M2.5 | Input \($/1M\): $0.30 | Output \($/1M\): $2.40 | Blended Cost: $0.82) Tj ET BT /F1 10 Tf 0.18 0.2 0.24 rg 1 0 0 1 46 290.49 Tm (Model: Kimi K2.5 | Input \($/1M\): $0.60 | Output \($/1M\): $3.00 | Blended Cost: $1.20) Tj ET BT /F1 10 Tf 0.18 0.2 0.24 rg 1 0 0 1 46 273.49 Tm (Model: GLM-5 | Input \($/1M\): $1.00 | Output \($/1M\): $3.20 | Blended Cost: $1.55) Tj ET BT /F1 10 Tf 0.18 0.2 0.24 rg 1 0 0 1 46 256.49 Tm (Model: Claude Opus 4.6 | Input \($/1M\): $5.00+ | Output \($/1M\): $25.00 | Blended Cost: ~$10.00) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 239.49 Tm (M2.5 is the clear economic leader. It can operate continuously at roughly $1 per hour. Kimi K2.5) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 224.49 Tm (introduces aggressive context caching, reducing repeated input costs by 83%.) Tj ET BT /F2 15 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 196.49 Tm (Latency and Token Efficiency) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 172.49 Tm (GLM-5 generates more reasoning tokens but processes them faster. In tests generating 500 tokens:) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 150.49 Tm (- GLM-5: 39.7 seconds) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 133.69 Tm (- Kimi K2.5: 62.3 seconds) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 116.89 Tm (GLM-5's faster internal reasoning throughput offsets its verbosity.) Tj ET BT /F2 15 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 88.89 Tm (Enterprise Use Case Mapping) Tj ET q 0.86 0.88 0.92 RG 1 w 46 42 m 549.28 42 l S Q BT /F1 8.4 Tf 0.42 0.45 0.5 rg 1 0 0 1 46 30 Tm (TechRounder | Page 4 of 5) Tj ET BT /F1 7.2 Tf 0.42 0.45 0.5 rg 1 0 0 1 46 19 Tm (https://www.techrounder.com/pdf/blog/an-in-depth-comparative-analysis-of-glm-5-minimax-m2-5-and-kimi-k2-5.pdf) Tj ET endstream endobj 13 0 obj << /Type /Page /Parent 2 0 R /MediaBox [0 0 595.28 841.89] /Resources << /Font << /F1 3 0 R /F2 4 0 R >> >> /Contents 14 0 R >> endobj 14 0 obj << /Length 2762 >> stream BT /F2 13 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 789.89 Tm (Kimi K2.5: Multimodal Research Orchestrator) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 767.89 Tm (- Competitive intelligence synthesis) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 751.09 Tm (- Vision-to-code pipelines) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 734.29 Tm (- Parallel browser automation) Tj ET BT /F2 13 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 711.49 Tm (MiniMax M2.5: Persistent Codebase Worker) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 689.49 Tm (- CI/CD automation) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 672.69 Tm (- Repository-wide refactoring) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 655.89 Tm (- Autonomous financial modeling) Tj ET BT /F2 13 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 633.09 Tm (GLM-5: Backend Architect and Macro Simulator) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 611.09 Tm (- Microservice architecture design) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 594.29 Tm (- Multilingual enterprise stacks) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 577.49 Tm (- Long-horizon operational planning) Tj ET BT /F2 15 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 554.69 Tm (Strategic and Market Implications) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 530.69 Tm (The rise of GLM-5, MiniMax M2.5, and Kimi K2.5 marks the erosion of proprietary dominance.) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 515.69 Tm (Open-weight models now match or exceed closed systems across coding, reasoning, and agentic) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 500.69 Tm (execution.) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 478.69 Tm (The industry is entering the Swarm Era. Human operators are no longer step-by-step copilots. They) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 463.69 Tm (become strategic directors of autonomous digital labor.) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 441.69 Tm (Finally, GLM-5 proves that frontier AI no longer depends exclusively on a single global semiconductor) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 426.69 Tm (supply chain. Parallel hardware ecosystems are now viable.) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 404.69 Tm (For enterprises in 2026, the message is clear. The advantage no longer lies in owning the largest) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 389.69 Tm (conversational model. It lies in orchestrating autonomous, cost-efficient agentic systems at scale.) Tj ET q 0.86 0.88 0.92 RG 1 w 46 42 m 549.28 42 l S Q BT /F1 8.4 Tf 0.42 0.45 0.5 rg 1 0 0 1 46 30 Tm (TechRounder | Page 5 of 5) Tj ET BT /F1 7.2 Tf 0.42 0.45 0.5 rg 1 0 0 1 46 19 Tm (https://www.techrounder.com/pdf/blog/an-in-depth-comparative-analysis-of-glm-5-minimax-m2-5-and-kimi-k2-5.pdf) Tj ET endstream endobj xref 0 15 0000000000 65535 f 0000000015 00000 n 0000000064 00000 n 0000000147 00000 n 0000000217 00000 n 0000000292 00000 n 0000000434 00000 n 0000006079 00000 n 0000006221 00000 n 0000011169 00000 n 0000011312 00000 n 0000016208 00000 n 0000016352 00000 n 0000020677 00000 n 0000020821 00000 n trailer << /Size 15 /Root 1 0 R >> startxref 23635 %%EOF