DeepSeek V4 Pro vs GLM-5.1: The Closest Open-Weight Fight of 2026

If you want a comparison where the headline number tells you almost nothing, this is it.

BenchLM's provisional leaderboard has DeepSeek V4 Pro at 83 and GLM-5.1 at 82. One point. Both from Chinese labs, both frontier-tier reasoning models, both released in 2026. From the aggregate they look nearly identical.

Look closer and they are not.

Benchmark Comparison

Coding: V4 Pro Wins, With One Exception

On coding overall, V4 Pro leads clearly — category average 73.8 against GLM-5.1's 60.9. That's a meaningful gap across most coding benchmarks.

The exception: Terminal-Bench. GLM-5.1 scores 69.2% versus V4 Pro's 67.9%. For workflows involving long-running terminal tasks, shell automation, or CLI-driven pipelines, GLM-5.1 edges ahead. For everything else in coding — algorithmic tasks, code repair, synthesis — V4 Pro wins.

Knowledge and Agentic Tasks

V4 Pro leads on knowledge at 62.6 versus GLM-5.1's 52.3 — consistent across benchmarks, reflecting stronger knowledge recall and synthesis. Agentic tasks follow: V4 Pro averages 70.0, GLM-5.1 averages 65.3. Not a blowout, but a steady advantage.

Context Window and Architecture

This is the clearest technical differentiator. V4 Pro has a 1-million-token context window. GLM-5.1 gives you 200K. For codebase-scale tasks, long document pipelines, or applications that benefit from fitting everything in one pass, V4 Pro's 1M window changes what you can build.

Architecture-wise, V4 Pro is a 1.6-trillion-parameter Mixture-of-Experts model with 49B parameters activated per token. The MoE design means trillion-parameter capacity at manageable inference cost per token. GLM-5.1 is a dense model — consistent compute per token, no sparse routing. Dense architecture has one practical upside users mention: output quality feels more stable turn to turn, without the occasional odd responses that can come from sparse activation.

GLM-5.1 Found an Interesting Niche

Despite losing on most benchmark metrics, GLM-5.1 has carved out a specific community. Shortly after its release, SillyTavern — the open-source frontend for running language models that has a devoted following among creative writing and roleplay users — added GLM-5.1 to their official supported models list. That's a meaningful signal: the community chose to integrate it, which takes real engineering effort and reflects genuine user demand.

Why GLM-5.1 specifically? Users report it handles long-form narrative generation and character consistency particularly well. For document-heavy workflows where you're loading full chapters or policy documents and asking nuanced questions about them, multiple developers have noted that GLM-5.1 handles the 200K window effectively. It doesn't have the dramatic advantages over GPT-era models on raw code, but for reading-intensive tasks within its context limit, the dense architecture apparently delivers consistent quality.

One developer described a specific use case: "I run document Q&A pipelines where users ask nuanced questions about long compliance reports. GLM-5.1 handles this well within 200K, and for my workload the per-token cost difference versus V4 Pro is real and meaningful." That's a concrete data point, not a synthetic benchmark.

The Content Filtering Question

This is something that surfaces repeatedly in any discussion about GLM-5.1, and it's worth addressing directly. GLM-5.1 is built by Zhipu AI, a Chinese lab, and the filtering behavior varies depending on which endpoint you're using.

The international API is noticeably less restrictive than some competing Chinese models. But compared to DeepSeek V4 Pro, GLM-5.1 has different thresholds on different topics. Developers building anything near politically sensitive content or working with data that touches on Chinese regulatory requirements have reported mixed experiences depending on the specific task.

"The filtering is inconsistent in a way that's hard to predict," one developer noted in a discussion thread. "For pure coding tasks it's transparent. For anything involving geopolitics or certain historical topics, behavior varies more than I expected." This is not unique to GLM-5.1 — it's a known tradeoff with models built under Chinese regulatory frameworks. Worth knowing before you commit to a production use case.

What Developers Are Actually Saying

The reception to GLM-5.1 has been quieter than V4 Pro's launch, but the gap in community conversation isn't entirely about capability.

"V4 Pro had a discount at launch that made it impossible to ignore," one developer wrote in a community newsletter. "GLM-5.1 launched at normal pricing and for most workloads V4 Pro had already been benchmarked and validated by the time GLM-5.1 came around. The positioning just wasn't as compelling at that moment."

The Terminal-Bench lead is a real talking point among CLI automation developers. "For shell scripting and long-running terminal tasks, GLM-5.1 just seems to understand what I'm trying to do better than V4 Pro does," one developer noted. "I can't explain why from the architecture, but it shows up consistently across different projects." That's a pattern that appeared in multiple independent reports — the Terminal-Bench number reflects something real in user experience.

A batch of developers who revisited the comparison after V4 Pro's promotional pricing ended reported that the value gap closed somewhat. At standard pricing, GLM-5.1 becomes more competitive for certain workloads. The promotional economics that made V4 Pro an obvious choice at launch don't last forever.

Pricing

DeepSeek V4 Pro at discounted API pricing comes in around $0.43 per million input tokens. GLM-5.1 is approximately 141% more expensive on input. For high-volume workloads running millions of tokens daily, this is a real cost difference. If V4 Pro's promotional period ends and pricing normalizes, the gap narrows.

The Honest Summary

V4 Pro wins on most categories here. The one exception is Terminal-Bench — if shell automation and CLI-driven tasks are your core use case, GLM-5.1 is worth evaluating.

For everything else: broader knowledge, general coding, agentic tasks, context window, and current price — V4 Pro is the stronger choice. The one-point leaderboard gap understates the practical differences you'll find when you dig into specifics.

GLM-5.1 isn't a mistake to run — SillyTavern adoption, the document Q&A use case, and the Terminal-Bench result are all real. But against V4 Pro specifically, you need a concrete reason to choose it. Without that reason, V4 Pro's combination of price, context window, and benchmark breadth is hard to argue against.

Sources: BenchLM, Artificial Analysis, LLMReference