Abstract
The industrial adoption of large language models in 2026 has pushed open-weight frontier models into real production environments. Among Chinese AI vendors, DeepSeek-V4-Pro and GLM-5.1 are two highly representative models. Both are built on Mixture-of-Experts architectures. Both target enterprise AI agents, software engineering, mathematical reasoning and long-context workloads. Yet their strengths are clearly different.
DeepSeek-V4-Pro emphasizes ultra-long context, faster inference, lower token consumption and flexible private deployment. GLM-5.1 focuses more on long-horizon autonomous task execution and strong repository-level code repair. This article compares the two models from five angles: architecture, benchmark performance, engineering task results, pricing and deployment value. Instead of treating benchmark scores as isolated numbers, the analysis connects each result with practical business scenarios, such as large codebase refactoring, API-based production services, private deployment and AI agent workflows.
The goal is not to declare one model as universally better. The real question is which model fits which workload.
1. Model Architecture and Basic Specifications
Before comparing benchmark scores, it is important to understand the basic architecture of both models. Architecture defines context capacity, inference cost, expert activation efficiency and deployment complexity. These factors directly affect real-world performance.
GLM-5.1 was released on March 27, 2026. It is positioned as a long-horizon agentic reasoning model based on a Mixture-of-Experts design. The model has 754 billion total parameters, with around 40 billion parameters activated during a single forward pass. This sparse activation structure helps reduce inference cost while keeping strong reasoning capacity.
Its native context window supports 200,000 input tokens. The maximum output length can reach 125,000 tokens. One of its most distinctive features is the official claim of an 8-hour autonomous task closed-loop capability. In practical terms, GLM-5.1 is designed to maintain task consistency during long multi-turn agent workflows. This makes it suitable for scenarios where the model needs to plan, execute, revise and continue working for an extended period.
DeepSeek-V4-Pro was released on April 24, 2026. It is paired with a lighter DeepSeek-V4-Flash version for cost-sensitive workloads. Its most obvious advantage is the 1,000,000-token native context window. This is five times larger than GLM-5.1’s context capacity. For large code repositories, long technical documents and multi-year conversation histories, this difference is significant.
With a million-token context, DeepSeek-V4-Pro can load far more information without manual segmentation. This reduces context fragmentation. It also lowers the risk of losing key dependencies during long reasoning chains.
The two models also differ in deployment flexibility. GLM-5.1 requires at least 8×H100 GPUs for stable FP8 local inference. DeepSeek-V4-Pro supports deployment on 4×H200 or 8×H100 clusters. For enterprises evaluating private deployment, this difference affects both hardware planning and long-term operating cost.
| Dimension | DeepSeek-V4-Pro | GLM-5.1 |
|---|---|---|
| Release date | April 24, 2026 | March 27, 2026 |
| Architecture | MoE | MoE |
| Native context window | 1,000,000 tokens | 200,000 tokens |
| Max output length | Not specified in the source data | 125,000 tokens |
| Local deployment reference | 4×H200 or 8×H100 | 8×H100 |
| License | MIT | Apache 2.0 with additional commercial clauses |
| Main advantage | Long context, efficiency, cost control | Long autonomous task execution, code repair |
From this basic comparison, the positioning difference is already clear. DeepSeek-V4-Pro is more suitable for long-context and high-throughput industrial workloads. GLM-5.1 is more attractive for autonomous agent workflows and repository-level software engineering tasks.
2. Benchmark Comparison
Benchmark results are useful, but they should not be read in isolation. A high score in mathematical reasoning does not always mean better code repair. A strong agent benchmark does not necessarily mean lower production cost. The following sections break down the most important benchmark categories.
2.1 Mathematical Reasoning: AIME 2026 and HMMT
Mathematical reasoning is one of the key indicators for frontier model capability. It tests symbolic reasoning, multi-step deduction and the ability to maintain logical consistency.
On AIME 2026, GLM-5.1 scores 95.3%, while DeepSeek-V4-Pro scores 95.2%. The difference is only 0.1 percentage point. In practical terms, both models perform at a similar level on high-level competition math.
The gap becomes more visible on HMMT, a more demanding mathematical benchmark. DeepSeek-V4-Pro reaches 95.2%, while GLM-5.1 records 89.4%. This gives DeepSeek a 5.8 percentage point lead.
| Benchmark | DeepSeek-V4-Pro | GLM-5.1 | Difference |
|---|---|---|---|
| AIME 2026 | 95.2% | 95.3% | GLM +0.1 |
| HMMT | 95.2% | 89.4% | DeepSeek +5.8 |
This result suggests that both models are strong in standard competition math. However, DeepSeek-V4-Pro performs better when the problem requires longer intermediate reasoning. The larger context window helps it retain more variables, proof steps and constraints during complex multi-stage calculations.
2.2 Graduate-Level Scientific Reasoning: GPQA-Diamond
GPQA-Diamond evaluates graduate-level knowledge in fields such as physics, chemistry and biology. It is harder than general knowledge tests because it requires both domain knowledge and deductive reasoning.
DeepSeek-V4-Pro scores 90.1% on GPQA-Diamond. GLM-5.1 scores 86.2%. DeepSeek leads by 3.9 percentage points.
| Benchmark | DeepSeek-V4-Pro | GLM-5.1 | Difference |
|---|---|---|---|
| GPQA-Diamond | 90.1% | 86.2% | DeepSeek +3.9 |
The difference is meaningful for research-oriented applications. DeepSeek-V4-Pro appears stronger in cross-disciplinary questions, especially when several concepts must be connected across a long reasoning path. GLM-5.1 remains stable on well-bounded domain questions, but its advantage is less obvious in broad scientific reasoning.
2.3 Agent Tool Use: Toolathlon and MCPAtlas
Agent capability is becoming a major evaluation standard for enterprise LLM adoption. Many production systems now require models to call APIs, read tool outputs, use search results, execute code and continue working across many steps.
On Toolathlon, DeepSeek-V4-Pro scores 51.8%. GLM-5.1 scores 40.7%. This is an 11.1 percentage point gap.
| Benchmark | DeepSeek-V4-Pro | GLM-5.1 | Difference |
|---|---|---|---|
| Toolathlon | 51.8% | 40.7% | DeepSeek +11.1 |
| MCPAtlas | 73.6% | 71.8% | DeepSeek +1.8 |
The Toolathlon result reflects DeepSeek’s advantage in long multi-turn tool workflows. Since tool responses can accumulate quickly, the million-token context window helps preserve more historical tool outputs. This reduces repeated calls and prevents the model from losing earlier information.
The gap is much smaller on MCPAtlas. DeepSeek-V4-Pro scores 73.6%, and GLM-5.1 scores 71.8%. This means both models are already reasonably compatible with mainstream agent middleware and Model Context Protocol style integrations. For most enterprise middleware access scenarios, neither model presents a major compatibility barrier.
2.4 Software Engineering: SWE-Bench Pro
SWE-Bench Pro is one of the most important benchmarks for evaluating coding agents. It uses real GitHub issues to test whether a model can understand a repository, locate bugs and generate working fixes.
Here, GLM-5.1 shows a clear advantage. It achieves 58.4% on SWE-Bench Pro. DeepSeek-V4-Pro scores 49.8%. GLM leads by 8.6 percentage points.
| Benchmark | DeepSeek-V4-Pro | GLM-5.1 | Difference |
|---|---|---|---|
| SWE-Bench Pro | 49.8% | 58.4% | GLM +8.6 |
This result is important because it reflects production-level code repair ability. GLM-5.1 performs especially well in multi-file repository tasks. It is better suited for complex bug fixing, dependency-aware modifications and long-running code repair workflows.
DeepSeek-V4-Pro still performs strongly in single-file or isolated module generation. The reported LiveCodeBench result shows DeepSeek-V4-Pro reaching 93.5% on single-file code generation tasks. However, no directly comparable GLM-5.1 result is available for that benchmark in the source data. Based on the available numbers, GLM-5.1 has the stronger advantage in repository-level repair, while DeepSeek-V4-Pro is more efficient for large-context code reading and lighter development tasks.
3. Real-World Engineering Task Results
Benchmarks are valuable, but production teams care more about task completion, latency, token usage and manual revision cost. In the controlled engineering test set, both models were tested on 10 standardized front-end and back-end development tasks. These tasks included landing page generation, data visualization pages, algorithm module refactoring and API debugging.
The test conditions were consistent. Both models used the same prompt templates, the same temperature setting and the same top_p value:
temperature = 1
top_p = 0.95
Both models reached a 100% functional pass rate. This means all generated projects could run without critical compilation errors. Neither model failed the basic functionality requirements.
The difference appears in efficiency.
| Indicator | DeepSeek-V4-Pro | GLM-5.1 | Result |
|---|---|---|---|
| Functional pass rate | 100% | 100% | Tie |
| Average inference latency | 25.2s | 61.5s | DeepSeek faster |
| Average token consumption | 2,092 tokens | 3,414 tokens | DeepSeek lower |
| Manual revision rounds | 1.1 | 3.2 | DeepSeek fewer revisions |
DeepSeek-V4-Pro averages 25.2 seconds per task. GLM-5.1 averages 61.5 seconds. Under the same test conditions, DeepSeek is about 2.44 times faster.
DeepSeek also uses fewer tokens. Its average token consumption is 2,092 tokens per task, compared with 3,414 tokens for GLM-5.1. This is a 38.7% reduction in token overhead.
Manual revision cost also differs. GLM-5.1 requires around 3.2 rounds of manual adjustment after generation. These adjustments mainly involve UI layout, dependency matching and cross-file details. DeepSeek-V4-Pro requires only 1.1 rounds on average. For lightweight development tasks, DeepSeek produces cleaner first-pass results.
However, this does not mean DeepSeek is always better for software engineering. The scenario matters.
GLM-5.1 has stronger value in long-cycle autonomous development. Its 8-hour closed-loop capability is useful when the model needs to continuously plan, generate, test and revise a project. DeepSeek-V4-Pro is stronger when the task requires loading a very large repository or processing a massive amount of context at once.
In simple terms: GLM is better for long autonomous coding workflows; DeepSeek is better for large-context and high-efficiency development tasks.
4. Pricing and Commercial Cost Analysis
For enterprise adoption, model capability is only one part of the decision. Token pricing can have a larger impact over time, especially for products with high daily API usage.
The pricing data can be compared by cost per million tokens.
| Model | Input price / 1M tokens | Output price / 1M tokens | Main cost advantage |
|---|---|---|---|
| DeepSeek-V4-Pro | $1.74 | $3.48 | Lower output cost |
| DeepSeek-V4-Flash | $0.20 | $1.00 | Low-cost mass invocation |
| GLM-5.1 | $1.40 | $4.40 | Lower input cost |
GLM-5.1 has a slightly lower input price than DeepSeek-V4-Pro. However, its output price is higher. GLM-5.1 costs $4.40 per million output tokens, while DeepSeek-V4-Pro costs $3.48. That makes GLM’s output pricing about 26.4% higher.
For workloads with long generated outputs, DeepSeek-V4-Pro is usually more cost-efficient. This includes code generation, report writing, long agent responses and multi-step reasoning outputs.
GLM-5.1 may still be cheaper for short-input, short-output tasks. Examples include text classification, brief summarization and simple intent recognition. But for most software engineering workflows, output tokens often dominate the bill. In those cases, DeepSeek’s pricing structure becomes more attractive.
The license also matters. DeepSeek-V4-Pro uses the MIT license. This lowers the compliance burden for private deployment and commercial product integration. GLM-5.1 uses Apache 2.0 with additional commercial clauses. For external-facing commercial products, enterprises may need to evaluate extra licensing requirements.
5. Deployment Scenarios and Model Selection
There is no universal winner between DeepSeek-V4-Pro and GLM-5.1. The better choice depends on the workload.
GLM-5.1 is more suitable for teams that need long autonomous agent execution. It is also a strong option for repository-level code repair, complex bug fixing and multi-file engineering tasks. Its SWE-Bench Pro score shows clear strength in practical software maintenance.
DeepSeek-V4-Pro is more suitable for ultra-long context workloads. It is a better fit for large legacy codebase analysis, long document processing, multi-turn tool workflows and high-frequency API services. It also has advantages in latency, token efficiency and private deployment flexibility.
| Scenario | Better fit | Reason |
|---|---|---|
| Large codebase refactoring above 200k tokens | DeepSeek-V4-Pro | 1M-token context window |
| Long autonomous AI agent workflow | GLM-5.1 | 8-hour closed-loop capability |
| Repository-level bug fixing | GLM-5.1 | Higher SWE-Bench Pro score |
| High-frequency API calls | DeepSeek-V4-Pro / V4-Flash | Lower latency and lower token cost |
| Graduate-level scientific reasoning | DeepSeek-V4-Pro | Higher GPQA-Diamond score |
| Private deployment with fewer license constraints | DeepSeek-V4-Pro | MIT license |
| Short prompt and short output tasks | GLM-5.1 | Lower input token price |
In real enterprise systems, teams often use more than one model. A coding platform may use GLM-5.1 for complex bug repair and DeepSeek-V4-Pro for long-context repository reading. A customer support system may use DeepSeek-V4-Flash for simple high-frequency requests and reserve DeepSeek-V4-Pro or GLM-5.1 for harder tasks.
This is also where an API aggregation layer becomes useful. Instead of binding business code directly to each model provider, teams can place a unified access layer between applications and model endpoints. For example, TreeRouter can serve as a single API entry point for managing model addresses, keys and compatible request formats across DeepSeek, GLM and other models. It does not change the model’s native capability, but it reduces repeated integration work. It also makes later model switching and cost comparison easier for development teams.
This type of setup is especially practical when a company needs to evaluate multiple models over time. Model capability changes quickly. Pricing also changes frequently. Keeping the application layer independent from a single provider helps reduce migration cost.
6. Final Comparison
DeepSeek-V4-Pro and GLM-5.1 represent two different technical routes.
DeepSeek-V4-Pro focuses on long context, inference efficiency and deployment flexibility. Its million-token context window is a major advantage for large repositories, long documents and tool-heavy agent workflows. Its lower average latency and lower token consumption also make it attractive for production systems with frequent API calls.
GLM-5.1 focuses on autonomous long-task execution and repository-level coding capability. Its SWE-Bench Pro result is stronger. Its 8-hour autonomous closed-loop design also gives it a unique position in AI agent development, especially when tasks require continuous planning and revision.
For developers and enterprise AI teams, the right decision should be based on workload structure, not brand preference. If the main challenge is large-context processing and cost control, DeepSeek-V4-Pro is the better fit. If the main challenge is complex code repair and long autonomous execution, GLM-5.1 deserves serious consideration.
In many production environments, the most realistic solution is not choosing only one model. A mixed-model architecture can provide better flexibility. DeepSeek-V4-Pro can handle long-context and high-throughput workloads. GLM-5.1 can handle difficult repository repair and autonomous coding tasks. With a unified access layer, teams can keep their system architecture stable while continuing to compare and adopt stronger models as they emerge.
