The open-source LLM ecosystem has evolved rapidly, and two models stand out as leaders in capability, efficiency, and practical usability: GLM-4.6, Zhipu AI’s high-capacity reasoning model with a 200k-token context window, and DeepSeek-V3.2, a sparsely activated Mixture-of-Experts architecture engineered for exceptional performance per dollar.

Both models are powerful. Both are versatile. Both are widely adopted across coding, chat assistants, RAG systems, and agent frameworks. But they are built with very different design philosophies, and those differences determine where each model shines.

This article explores what distinguishes these models, how they perform on real workloads, and — most importantly — how they behave on DeepInfra’s high-performance inference platform.

What Each Model Is Designed to Do

GLM-4.6: Dense reasoning with extreme context length

GLM-4.6 sits in the category of “high-capacity reasoning models,” designed to provide stable, step-by-step thought processes, strong coding behavior, and consistent agent performance. Its signature feature is the 200,000-token context window, which is large enough to ingest entire repositories, long research papers, or multi-file business documents in a single prompt. The model handles complex layouts, multiple sections, and detailed reference material without chunking — and without losing coherence during long-range reasoning.

Beyond context size, GLM-4.6 shows improvement over its predecessor (GLM-4.5) in coding quality, multi-step reasoning, preference alignment, and agent skill, making it a reliable choice for workflows that depend on accuracy, consistency, and interpretability.

DeepSeek-V3.2: MoE power with sparse attention efficiency

DeepSeek-V3.2 represents a different approach: instead of relying on a dense transformer, it uses a Mixture-of-Experts (MoE) system combined with Dynamic Sparse Attention (DSA). Only a subset of experts is activated per token, meaning the model can behave like a large LLM while consuming significantly less compute than a dense model with a similar total parameter count.

This architecture gives DeepSeek-V3.2 impressive performance across reasoning and coding tasks despite a more constrained context window (~128k tokens). It also results in higher throughput, lower latency, and reduced inference cost on typical workloads — making it one of the most compute-efficient open-source models currently available.

Where GLM-4.6 optimizes for capability, DeepSeek-V3.2 optimizes for efficiency. These philosophies overlap, but they do not compete directly; they complement each other.

Reasoning, Coding, and Real-World Task Performance

Reasoning strength and reliability

Based on the most recent benchmark from ArtificialAnalysis (AA), DeepSeek‑V3.2 actually scores 66 on the “Intelligence Index,” outperforming GLM‑4.6, which scores 56.

https://artificialanalysis.ai/models/glm-4-6-reasoning?models=deepseek-v3-2-reasoning%2Cglm-4-6-reasoning#artificial-analysis-intelligence-inde

This suggests DeepSeek-V3.2 currently holds an edge on overall benchmarked reasoning performance. In practical terms, DeepSeek-V3.2 shows very strong performance on reasoning tasks, including multi-step reasoning and agent-like workflows, and appears competitive — if not better — than GLM-4.6 under AA’s evaluation criteria.

That said, GLM-4.6 remains strong in other dimensions — particularly in handling very long contexts (with its 200 k-token window) and in providing the dense-model determinism some use cases require. Those characteristics can still make GLM-4.6 preferable for workloads emphasizing context size, document-scale understanding, or stable reasoning over extremely long inputs.

Coding performance and developer workflows

Both models excel at coding, but they shine in different scenarios:

• GLM-4.6 is better suited for repository-scale work, multi-file analysis, and cross-reference reasoning, thanks to its 200k-token context. IDEs, code review tools, and agent-driven debugging systems benefit from this.
  
• DeepSeek-V3.2 is exceptionally strong in real-time coding assistance, boilerplate generation, and logic correction tasks, where very fast token throughput and low TTFT produce a more responsive developer experience.
  

Developers working with large monorepos will see more value from GLM-4.6, while those building interactive coding assistants will appreciate DeepSeek-V3.2’s speed.

Agent workflows and tool use

GLM-4.6 tends to perform more predictably in agent orchestration. Its step timing is more consistent, its reasoning transitions are smoother, and its p95/p99 latency spikes are smaller — crucial metrics for complex verification or retrieval loops.

DeepSeek-V3.2 also works well in agent settings, but its dynamic sparsity means inference time can fluctuate more depending on the token patterns and the number of experts activated. In agent systems that execute hundreds of calls per session, these small fluctuations can accumulate.

Context Length and Long-Document Handling

One of the most practical differences between the two models is context size.

GLM-4.6’s 200k-token window allows it to handle long books, legal documents, code repositories, academic writing, or multi-chapter project specifications without any chunking or RAG preprocessing. This reduces complexity and preserves narrative flow.

DeepSeek-V3.2 supports ~128k tokens — still large, but not enough for very long and dense contexts. Users processing legal corpora or multi-file inputs may need to chunk content or use retrieval strategies.

For most applications (chat, coding, summarization, support agents), 128k is more than sufficient. But for extremely large contexts, GLM-4.6 remains among the strongest open-source options available.

Open-Source Licensing and Deployability

Both models are fully open-source, with permissive MIT-style licensing. This allows:

• on-premise deployments,
  
• fine-tuning,
  
• weight inspection,
  
• enterprise data governance,
  
• model customization.
  

However, DeepSeek-V3.2’s MoE architecture can be more complex to parallelize efficiently across GPUs. GLM-4.6, while heavier, benefits from simpler scaling and more predictable GPU memory usage.

How the Models Perform on DeepInfra: Long-Context Performance, Throughput, and Cost

DeepInfra continuously benchmarks models to provide developers with low-latency, high-throughput, cost-efficient inference. When comparing GLM-4.6 and DeepSeek-V3.2 on our infrastructure, several clear performance patterns emerge — each shaped by the model’s architecture and optimized runtime.

Streaming Throughput (Tokens per Second

Throughput — how fast the model generates tokens — is where DeepSeek-V3.2’s efficiency really shines on DeepInfra. Its sparse-activation and expert routing architecture enables high tokens-per-second generation rates under load. According to OpenRouter’s model comparison page, DeepSeek-V3.2 offers a highly competitive throughput metric of roughly 14 tokens per second on DeepInfra. This makes it especially suitable for high-volume inference pipelines, multi-user or concurrent applications, real-time generation tasks, or large-batch processing.

GLM-4.6 exceeds this throughput. On DeepInfra, its measured throughput is around 22 tokens per second under typical conditions. While generation speed naturally degrades as input size grows — especially when context edges toward the model’s maximum — GLM-4.6 retains a stable and predictable streaming behavior, which is important when deterministic performance matters or when processing longer prompts.

Long-Context Performance (100k+ Tokens)

Long-context handling is an area where GLM-4.6 particularly stands out. OpenRouter documentation for GLM-4.6 indicates a maximum context window of around 203 k tokens when run on DeepInfra. This makes it especially well-suited for applications such as large-document summarization, retrieval-augmented generation over extensive corpora, multi-file codebase reviews, or deep research workflows — all of which benefit from processing large context in a single pass.

In contrast, DeepSeek-V3.2 supports a context length up to about 164k tokens according to its OpenRouter page. While this is still substantial and sufficient for many long-ish tasks, it does impose a limit: extremely long inputs, very large documents, or deeply nested reference material may sometimes approach or exceed the window. Under those conditions, GLM-4.6’s larger context capacity delivers a more robust, chunk-free experience.

Cost Efficiency

According to the rates on the DeepInfra model pages:

• DeepSeek-V3.2 costs ≈ $0.27 per 1M input tokens and ≈ $0.40 per 1M output tokens under one pricing plan.
  
• GLM-4.6’s listed input price is $0.43 per 1M tokens, with output at ≈ $1.75 per 1M tokens.

Given these numbers, DeepSeek-V3.2 offers significantly greater cost-efficiency — especially for short or mid-length prompts, high-throughput workloads, or use-cases with many repeated calls. Because it uses less compute per token and sustains high throughput, it helps maximize GPU utilization and throughput-per-dollar on DeepInfra.

GLM-4.6 remains more expensive per token — but its cost is often justified in workloads where long context, reasoning stability, and single-pass large-input processing are critical. For large-document RAG, multi-file code review, or deep analysis tasks, the value delivered per token can offset the higher unit cost.

Two Strong Models, Two Different Strengths — Both Optimized on DeepInfra

GLM-4.6 and DeepSeek-V3.2 are not direct competitors. They are optimized for different kinds of intelligence:

• GLM-4.6 excels in long-context reasoning, analytical depth, stable agent performance, and complex multi-file understanding.
  
• DeepSeek-V3.2 excels in throughput, cost efficiency, fast interaction loops, and lightweight reasoning tasks.
  

On DeepInfra, both models benefit from accelerated hardware, fast TTFT pipelines, and optimized batching, making them top choices for users deploying open-source LLMs at scale.

GLM-4.6 gives our users unmatched long-context capability and reliable reasoning. DeepSeek-V3.2 delivers outstanding performance-per-dollar with exceptional streaming speed.

Together, they represent two of the best open models available today — and DeepInfra is optimized to run both at their full potential.