DeepSeek V4 vs GLM-5.1: Open-Source Coding Models From China Compared (2026)

China’s open-source AI labs are shipping frontier-level coding models at a pace that keeps the rest of the industry on its toes. Two of the biggest releases in early 2026 are DeepSeek V4 Pro and GLM-5.1 from Zhipu AI. Both are open-weight, both target developers, and both claim state-of-the-art results on coding and reasoning benchmarks.

So how do they actually stack up? DeepSeek published a head-to-head comparison alongside the V4 launch. Below we break down the numbers, highlight where each model wins, and discuss what it means for developers picking between them.

The models at a glance

DeepSeek V4 Pro is a 1.6-trillion-parameter Mixture-of-Experts (MoE) model. Only a fraction of those parameters activate per token, which keeps inference costs manageable despite the massive total size. DeepSeek has a track record of pushing MoE scaling further than most labs, and V4 Pro continues that trend.

GLM-5.1 comes from Zhipu AI (the team behind the ChatGLM series). Zhipu has not disclosed the full architecture details for GLM-5.1, but the model targets a similar niche: strong coding, math, and tool-use performance with open weights. For a deeper look at how GLM-5.1 compares to other Chinese models, see our GLM-5.1 vs DeepSeek vs Qwen breakdown.

Both models are available under permissive licenses and can be self-hosted or accessed through their respective APIs.

It is worth noting that these two labs represent different philosophies. DeepSeek has consistently bet on scaling MoE architectures to extreme sizes while keeping active parameters manageable. Zhipu has focused on practical deployment and building a broad product ecosystem around its models, including search, agents, and enterprise tools.

The result is two models that look similar on paper but may feel quite different in practice depending on your use case.

Benchmark comparison

The numbers below come from DeepSeek’s own evaluation published with the V4 release. Keep in mind that self-reported benchmarks always carry some bias, but they still give a useful relative picture when both models are tested under the same harness.

*GLM-5.1’s LiveCodeBench score was not included in DeepSeek’s comparison table, so a direct comparison is not possible for that benchmark.

Where DeepSeek V4 Pro leads

V4 Pro takes the top spot on seven of the eight benchmarks listed. A few highlights:

• On GPQA Diamond, V4 Pro scores 90.1% versus GLM-5.1’s 86.2%. That is a nearly four-point gap on graduate-level science questions, which suggests stronger reasoning depth.
• HMMT (competition math) shows the widest gap: 95.2% vs 89.4%. Almost six points of separation on problems designed to challenge top math students.
• Toolathlon is another standout. V4 Pro hits 51.8% while GLM-5.1 lands at 40.7%, an 11-point difference. This benchmark tests sustained, multi-turn tool use over long sessions, so V4 Pro appears to hold up better in extended agentic workflows.
• LiveCodeBench at 93.5% is an impressive number on its own, though without a GLM-5.1 score we cannot draw a direct comparison.

The pattern is consistent: V4 Pro tends to pull ahead on tasks that require deep reasoning chains or long-horizon planning.

It is also worth noting the MCPAtlas result. This benchmark tests how well models interact with tools through the Model Context Protocol, which is becoming a standard for agentic AI workflows. V4 Pro’s 73.6% versus GLM-5.1’s 71.8% is a smaller gap than some of the others, but it still favors DeepSeek for developers building MCP-based agent pipelines.

Where GLM-5.1 wins

GLM-5.1 edges out V4 Pro on Terminal-Bench, scoring 69.2% to V4 Pro’s 67.9%. The margin is small (1.3 points), but Terminal-Bench specifically tests a model’s ability to complete tasks inside a terminal environment. That includes navigating file systems, running CLI tools, parsing output, and chaining shell commands.

This result suggests GLM-5.1 may have received targeted training or fine-tuning on terminal interaction patterns. For developers who primarily need an AI assistant for shell-based workflows, this is worth noting.

The Terminal-Bench win is a reminder that overall benchmark averages do not tell the whole story. A model that is slightly weaker on aggregate can still be the better choice for a specific domain. If your daily work involves heavy terminal usage, writing bash scripts, managing containers, or debugging CI pipelines, GLM-5.1’s edge here could save you real time.

Real-world adoption: GLM-5.1 has users

Benchmarks only tell part of the story. One interesting signal for GLM-5.1 is its real-world traction. On FounderMath, a competitive leaderboard where developers race to solve problems using AI-assisted tools, GLM-5.1 already has 12 active users competing with it. That is a small but meaningful sign that developers are finding it practical enough to use in timed, high-pressure scenarios.

DeepSeek V4 Pro is newer, so its community adoption numbers are still ramping up. But DeepSeek’s existing user base from V3 and earlier releases is large, and V4 Pro is likely to see rapid uptake given the benchmark results.

Both models benefit from strong ecosystems. DeepSeek has its API platform and a growing set of integrations. Zhipu has ChatGLM’s established user base in China and increasingly in international markets.

Community momentum matters for open-source models. More users means more fine-tunes, more tooling, and faster bug discovery. Both models are well-positioned on this front, but the FounderMath activity around GLM-5.1 is an early indicator that Zhipu’s model is gaining traction with power users who push models to their limits.

The bigger picture: Chinese open-source AI in 2026

The fact that two Chinese labs are releasing models that compete with (and in some cases surpass) the best Western models on coding benchmarks is a significant shift. A year ago, the conversation was mostly about whether Chinese models could catch up. Now the question is which Chinese model to choose.

For developers outside China, both models are accessible. DeepSeek and Zhipu both offer international API access, and the open weights mean you can run them anywhere. The competitive pressure between these labs is driving rapid improvement, which benefits everyone regardless of which model you pick.

Which one should you use?

It depends on your workload:

• For coding competitions, math-heavy tasks, or agentic tool use, V4 Pro has a clear edge based on these benchmarks.
• For terminal-centric workflows (DevOps, sysadmin automation, CLI scripting), GLM-5.1’s slight advantage on Terminal-Bench might translate to better real-world performance.
• For general knowledge tasks, both models are close. The 1.5-point gap on MMLU-Pro is unlikely to matter in practice.
• For long-context workloads, V4 Pro’s lead on HLE (37.7% vs 34.7%) suggests it handles extended contexts more reliably, though both scores are relatively low, indicating this remains a hard problem for all models.

Cost is another factor. DeepSeek’s MoE architecture means that despite the 1.6T total parameter count, the per-token compute cost can be competitive. Zhipu’s pricing for GLM-5.1 API access has been aggressive, often undercutting Western alternatives. Check each provider’s current pricing page for up-to-date numbers.

If you want the broadest coverage across tasks, DeepSeek V4 Pro is the safer pick based on current data. If you are already in the Zhipu ecosystem or need strong terminal performance, GLM-5.1 is a solid choice. And if you want to see how both compare to Qwen’s latest, check our three-way comparison.

FAQ

Is DeepSeek V4 Pro better than GLM-5.1 for coding?

Based on DeepSeek’s published benchmarks, V4 Pro leads on most coding and reasoning tasks. It scores 93.5% on LiveCodeBench (GLM-5.1 score not reported) and wins on tool-use benchmarks like Toolathlon (51.8% vs 40.7%) and MCPAtlas (73.6% vs 71.8%). The one exception is Terminal-Bench, where GLM-5.1 scores slightly higher. For a broader comparison that includes Qwen, see our three-way comparison.

Are these benchmarks trustworthy since they come from DeepSeek?

Self-reported benchmarks should always be taken with a grain of salt. Labs tend to optimize for the benchmarks they publish. That said, DeepSeek tested both models under the same evaluation harness, which makes the relative comparison more reliable than comparing numbers from separate reports. Independent evaluations from the community will provide a fuller picture as they become available. We will update this article as third-party results come in.

One thing working in DeepSeek’s favor on credibility: they included the Terminal-Bench result where GLM-5.1 wins. A lab trying to cherry-pick results would likely have omitted that data point.

Can I run DeepSeek V4 Pro or GLM-5.1 locally?

Both models are open-weight, so self-hosting is possible. However, V4 Pro’s 1.6T parameter count (even with MoE sparsity) requires significant GPU memory for full-precision inference. You are looking at multi-GPU setups for the full model, though the active parameter count per forward pass is much lower than 1.6T thanks to the MoE routing.

Quantized versions and optimized serving frameworks like vLLM or SGLang can bring the hardware requirements down substantially. 4-bit quantized variants of V4 Pro are already appearing in the community.

GLM-5.1’s requirements depend on its undisclosed architecture size, but early reports suggest it is more accessible for single-node setups. Some users have reported running it on a single 8xH100 node without issues.

Check the V4 Pro guide and GLM-5.1 guide for detailed hardware recommendations and setup instructions.

Bottom line

DeepSeek V4 Pro wins the numbers game on most benchmarks, but GLM-5.1 is not far behind and takes Terminal-Bench. Both are strong open-source options for coding workflows in 2026. The best approach is to try both on your actual tasks and see which one fits your workflow better.