Qwen 3.5 vs Minimax M2.5 vs GLM 5: Which is Better in 2026

Three recent Chinese-market flagship models — Alibaba Group’s Qwen 3.5, MiniMax’s MiniMax M2.5, and Zhipu AI’s GLM-5 — were each announced within weeks of one another and push different tradeoffs. Qwen 3.5 focuses on agentic multimodal capabilities at very large sparse scale and claims substantial cost-efficiency gains; MiniMax M2.5 emphasizes balanced real-world productivity (especially coding) with lower serving cost; and GLM-5 aims to be the top open-weights performer on reasoning, coding and agent tasks, engineered to run on domestically produced chips. Choosing “which is better” depends heavily on your objective: large-scale enterprise agent deployments (Qwen), developer productivity and cost-sensitivity (MiniMax), or research / open-source adoption and transparency (GLM).

What is Qwen 3.5, MiniMax M2.5, Zhipu’s GLM-5?

Qwen 3.5 — what is it?

Qwen 3.5 is Alibaba’s 2026-generation open-weight multimodal model family (notably the Qwen-3.5-397B variant) marketed for “agentic” workloads — i.e., models that can reason with tools, interact with GUIs, and act across text, image, and video inputs. Alibaba positioned Qwen 3.5 as a hybrid sparse/dense model that delivers high multimodal and agentic performance at much lower per-token cost than many western closed models. The launch was timed to Chinese New Year’s Eve, signalling an aggressive product and pricing move.

Key published specs and claims:

• Parameter class: ~397B total with a sparse Mixture-of-Experts (MoE) routing strategy and an effective activated parameter count much lower in many inference cases.
• Multimodal: Native vision + text training; supports images and extended video reasoning.
• Context window / long-form: Qwen platform variants (Plus) advertise very long context windows (targeted multi-hundred-thousand to near-million token configurations on hosted tiers).
• Business pitch: Agentic actions (app GUI interaction), low cost per token, and strong benchmarks vs prior Qwen versions and some competitor claims.

MiniMax M2.5 — what is it?

MiniMax M2.5 is the latest release from the MiniMax team (an independent AI lab/startup), positioned as a pragmatic, high-utility model optimized for coding, agentic tool use, and productivity workflows. MiniMax emphasizes reinforcement-learning-driven fine-tuning and real-world task RLHF to improve agent performance in production settings.

Key published specs and claims:

• Focus areas: coding (SWE tasks), agentic tool orchestration, and search/office automation.
• Benchmarks claimed: high marks on SWE-Bench Verified, Multi-SWE and BrowseComp style agent tests (vendor numbers report 80.2% SWE-Bench Verified; 76.3% in BrowseComp harnesses on some published runs).
• Openness: MiniMax has distributed model weights and provides access via common inference stacks and repositories (e.g., Ollama).

Zhipu’s GLM-5 — what is it?

GLM-5 is the flagship release from Zhipu (Z.AI / Zhipu AI), following a rapid cadence of GLM-4.x updates. GLM-5 is targeted as a broadly capable open-weight model that emphasizes coding, reasoning, agentic sequences, and domestic hardware compatibility (trained and optimized on China-made accelerators such as Huawei Ascend and Kunlunxin). Zhipu positions GLM-5 as best-in-class among open models on many public academic benchmarks.

Head-to-head comparison table

Interpretation: The three models occupy overlapping but distinct competitive niches. Qwen-3.5 is pitched as a broadly capable multimodal agent with infrastructure efficiency and open weights. GLM-5 volunteers strong coding and agent claims with a focus on domestic hardware supply chains. MiniMax M2.5 emphasizes runtime speed and engineering for production agent tasks.

Qwen 3.5 vs Minimax M2.5 vs GLM 5: Architectures Compare

Architectural differences strongly influence how models perform across tasks such as reasoning, coding, agentic workflows, and multimodal understanding.

Below is a side-by-side comparison of core architectural features:

Interpretation:

• Qwen 3.5’s design focuses on both scale and efficiency via hybrid sparse architectures, enabling massive context windows and rich multimodal outputs.
• MiniMax’s M2.5 prioritizes efficient inference and productivity today, achieving lower computational costs and faster tool calls, crucial for real-world agent tasks.
• GLM 5’s massive scale and extensive active parameters aim to compete in benchmarks and long-step tasks, potentially matching closed-source rivals.

Qwen 3.5 — hybrid sparse/dense, agentic plumbing

• Core idea: Qwen 3.5 uses a MoE (Mixture-of-Experts) style sparsity combined with dense routing for multimodal tokens. This gives a high total parameter count (e.g., ~397B) while only activating a subset of parameters during inference — lowering compute and memory footprints for common requests.
• Implications: Large representational capacity for knowledge + modality fusion, with inference cost control. Good for long context and heavy multimodal workloads if the hosting infrastructure supports sparse kernels.

MiniMax M2.5 — task-optimized RL + compact backbone

• Core idea: MiniMax emphasizes training via extensive RLHF/RL-in-environment pipelines and fine-tuning for tool use. M2.5 appears to favor a dense but efficient backbone tuned for coding and agentic sequences.
• Implications: Less focus on extreme parameter scale; more focus on behavior alignment, developer ergonomics, and agent reliability. Often yields better real-world agentic behavior per compute dollar in coding workflows.

GLM-5 — dense architecture with engineering for throughput

• Core idea: GLM-5 is a dense large model optimized for training throughput and incremental post-training iterations using asynchronous RL infrastructure (reported as “slime” in some model cards). Zhipu also explicitly optimized for domestic accelerator stacks.
• Implications: Strong generalist reasoning and coding performance, with engineering choices aimed at fast iteration and compatibility with China’s silicon ecosystem.

How Do They Compare on Benchmarks?

Direct cross-model benchmarking is one of the most useful ways to assess performance across core capabilities like reasoning, coding, and comprehensive understanding.

Below are key reported results with context.

Overall Reasoning & Knowledge

Agentic Workflows & Coding

• MiniMax M2.5 has strong real-world coding benchmarks with 80.2% on SWE-Bench Verified and robust multi-step task management.
• GLM 5 reportedly approaches closed-source leaders and beats some benchmarks like Gemini 3 Pro on certain coding and agentic metrics.
• Qwen 3.5 is widely reported to perform on par with top closed-source models such as Gemini 3 Pro and GPT-5.2, although comprehensive third-party benchmark sheets are still emerging.

Multimodal Performance

Overall, Qwen 3.5’s multimodal support and extended context window give it a potential edge in long-form chat, video understanding, and agent tasks requiring sustained context.

Benchmarks and where each model shines:

• Qwen3.5: excels at multimodal agentic tasks (VITA, BFCL, TAU2), strong on multimodal document/video understanding and competitive for coding and general reasoning. Qwen’s business advantage is smooth integration into Alibaba’s ecosystem and a product strategy emphasizing agent-enabled commerce and tooling.
• MiniMax M2.5: pitched on cost and throughput with solid, pragmatic performance across agentic tasks; its edge is economics for high-volume agent loops. Independent rebench snapshots show MiniMax is competitive on productivity indices but not necessarily the absolute top on every academic leaderboard.
• GLM-5 (Zhipu): standout on coding and SWE suites (SWE-bench Verified ~77.8, Terminal-Bench ~56.2), with a very large context window and strong open-weight performance — GLM-5 is likely the top open-weight choice for heavy coding/engineering agent workloads as of early Feb 2026.

Practical recommendation

If your primary workload is agentic multimodal orchestration (tool calling, GUI automation, multimodal documents, e-commerce agent integration), Qwen3.5 is among the best choices and offers platform advantages in Asia. If you need the best open-weight coding engineer model, GLM-5 currently looks stronger on developer-centric coding benchmarks. If cost/throughput is the single biggest constraint for massive agent loops, MiniMax M2.5 offers a clear value play. Use a hybrid approach where you pick the model matched to each component (e.g., GLM-5 for heavy code generation, Qwen3.5 for multimodal agent front-end orchestration, Minimax M2.5 for high-volume, low-latency agent loops).

So — which is better: Qwen 3.5, MiniMax M2.5, or GLM-5?

Short answer

There is no single “better” model — each model leads in different axes:

• Qwen 3.5: best candidate for multimodal agentic applications and very cost-sensitive large deployments (strong vendor pricing and native vision + action focus).
• MiniMax M2.5: best for coding and practical agentic tool chains where developer ergonomics and real-world coding benchmarks matter.
• GLM-5: best broad open-model generalist, especially appealing for China-centric deployments and organizations valuing domestic hardware compatibility and open-weight flexibility.

Practical Capability Comparison

Beyond raw benchmark scores, real-world utility depends on how well a model performs tasks that matter to businesses and developers, such as coding, reasoning, handling multimodal inputs, and executing chain-of-thought operations.

Below is a summary of relative strengths and typical use cases:

Key Insights:

• MiniMax M2.5 shines for production workflows — it’s fast, cheap, and highly competitive in coding and agentic benchmarks.
• Qwen 3.5 excels in multimodal deep understanding and very long context computations, which are essential for complex research tasks.
• GLM 5 projects strong agentic reasoning suitable for enterprise engineering tasks.

Price and Cost Comparison

Cost efficiency is a major differentiator for enterprise adoption — especially for high-volume users.

Interpretation:

• MiniMax M2.5 leads in pricing efficiency per million tokens, making it attractive for high-volume deployments.
• Qwen 3.5’s pricing undercuts many major competitors, including closed-source models and even some open-source ones.
• GLM 5 carries a higher token cost but may justify this with stronger long-horizon agentic performance and engineering capabilities.

CometAPI currently integrates these three models, and its API price is always discounted. If you don't want to switch vendors and adapt to different vendors' pricing strategies, CometAPI is the best choice. It only requires a key to access via chat format.

Conclusion

In the context of early 2026, Qwen 3.5, MiniMax M2.5, and GLM 5 are each compelling models with differentiated strengths. All three signal the continuing evolution of open-weight, high-performance AI:

• Qwen 3.5 leads in multimodal, long-context reasoning and global multilingual support.
• MiniMax M2.5 pushes efficient real-world productivity and agent workflows.
• GLM 5 scales to high engineering tasks with a large active parameter base.

Choosing the right model depends on the precise requirements of your project — whether that’s the ability to handle multimodal reasoning, coding performance, context scale, or cost efficiency.

Developers can access Qwen 3.5 API, MiniMax M2.5 and GLM-5 (Zhipu)  via CometAPI now.To begin, explore the model’s capabilities in the Playground and consult the API guide for detailed instructions. Before accessing, please make sure you have logged in to CometAPI and obtained the API key. CometAPI offer a price far lower than the official price to help you integrate.

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