GLM-5.2 vs DeepSeek-V4-Pro: Competitive Programming and Long Horizon Coding

Here is the comparison first, because the single number everyone quotes hides the only thing that matters: these two models are strong at different jobs. DeepSeek-V4-Pro wins self contained algorithm problems. GLM-5.2 wins long, messy, multi file work. Read the table with that split in mind.

Metric	GLM-5.2	DeepSeek-V4-Pro
License / context	MIT, 1M tokens	MIT, 1M tokens
Max output tokens	128K	384K
LiveCodeBench	strong, behind DeepSeek	93.5%
Codeforces rating	strong, behind DeepSeek	~3206
SWE-Bench Pro (open weights aggregate)	62.1% (top open weights)	~55.4% (V4-Pro-Max)
Terminal-Bench 2.1	leads by ~17 points	trails
Long horizon (FrontierSWE, DeepSWE)	leads by 38 to 45 points	trails
Price per 1M output tokens (first party)	~$4.40	~$0.87
Best fit	agents iterating over a codebase for hours	one shot hard algorithm problems, token heavy batch work

DeepSeek released the V4-Pro-0813 build on August 13, 2026, and Zhipu shipped GLM-5.3 the next day as an evolution of GLM-5.2. Both are 1M token context, both are MIT licensed, and both are genuinely open weights. That is where the similarity ends.

Two benchmarks, two different jobs

The reason a single leaderboard misleads you is that "coding" is not one skill. Competitive programming rewards a model that can read a tightly specified problem and emit a correct, efficient solution in one pass. Agentic software engineering rewards a model that can hold a large unfamiliar repository in its head, plan a change across several files, run tests, read the failures, and keep going without losing the thread. A model can be excellent at one and merely fine at the other, and DeepSeek-V4-Pro and GLM-5.2 are the cleanest example of that split we have seen this year.

DeepSeek-V4-Pro owns the competitive side. A LiveCodeBench score of 93.5 percent and a Codeforces rating around 3206 put it in the territory of a strong human competitor. If your workload looks like discrete, well specified problems, a parser here, a data structure there, a tricky algorithm with clear inputs and outputs, this is the model that will one shot the most of them. The 384K output token ceiling matters here too, because it lets the model emit long, complete solutions and detailed reasoning without truncation.

GLM-5.2 owns the long horizon side. On the open weights SWE-Bench Pro aggregate it lands at 62.1 percent, the top open weights score, ahead of Qwen3.7 Max at 60.6, MiniMax M3 at 59.0, and Kimi K2.6 at 58.6. The gap over DeepSeek widens on the benchmarks built specifically for multi hour agent runs: GLM-5.2 leads on Terminal-Bench 2.1 by around 17 points and on FrontierSWE and DeepSWE by 38 to 45 points. Those are not rounding errors. They are the difference between an agent that finishes a real feature and one that stalls halfway through.

For the same reason SWE-Bench Verified stopped separating frontier models once everyone cleared seventy percent, the interesting signal here lives on the harder, agentic distributions. We made the same point in our GPT-5.6 Sol vs Grok 4.6 SWE-Bench Pro comparison: the benchmark that fails most models tells you more than the one that flatters them.

The price gap changes the calculation

DeepSeek-V4-Pro costs roughly $0.87 per million output tokens at first party rates, against roughly $4.40 for GLM-5.2, about a five times difference. On competitive style problems, where the answer is short and the model wins in one pass, that price gap is close to free money: you get the best competitive programming model and you pay the least per solution.

The math inverts on long horizon agent work. A model that resolves more tasks in fewer steps can be cheaper per resolved issue even at a higher token price, because it buys fewer tokens per outcome. GLM-5.2 costs more per token but tends to reach green in fewer trajectories on the multi hour tasks it is built for. So the honest way to compare cost is not the pricing page, it is cost per resolved issue on your actual task distribution. On competitive problems DeepSeek wins that too. On repository scale work it is a real contest, and GLM-5.2 often comes out ahead despite the sticker price.

What each model is actually for

Reach for DeepSeek-V4-Pro when the work is self contained and token economics matter. Batch code generation, algorithm heavy problems, data transformation pipelines, anything where the task is well defined and you are running a lot of them. The competitive programming pedigree and the low output price make it the default for high volume, well scoped generation.

Reach for GLM-5.2 when an agent has to live inside a codebase. Repository wide refactors, migrations, multi file features, long test and fix loops. This is the same distinction we drew in the Qwen3 Coder vs Claude Opus 4.8 SWE-Bench comparison: the model that scores well on isolated problems is not always the one that survives a two hour agent run.

One caveat on GLM-5.3. It shipped a day after DeepSeek-V4-Pro as an incremental improvement on 5.2, so if you are standing up a new deployment, benchmark 5.3 against 5.2 on your own tasks before committing. Point releases this close together tend to move a few benchmarks and leave others flat, and the only distribution that matters is yours.

When this applies to your stack

If you are choosing an open weights coding model to self host or route to, do not pick on a single leaderboard number. Profile your real workload first. Count how much of it is self contained generation versus long horizon agent work, because that ratio, not the top of a leaderboard, decides which model wins on both quality and cost. For most enterprise engineering teams the split is uneven and predictable, which means the right answer is often to route: DeepSeek-V4-Pro for the cheap, well scoped, high volume generation, GLM-5.2 for the agent that has to reason across the repo. The same routing logic we apply to closed models in the GPT-5.5 vs Gemini 3.1 Pro enterprise workload analysis applies here, with the added freedom that both of these are MIT licensed and yours to run.

If you want help building that routing layer, deciding what to self host versus call through an API, and instrumenting cost per resolved issue so the decision stays honest as new point releases land, that is the kind of applied AI integration work we do at Contra Collective.

FAQ

Is GLM-5.2 or DeepSeek-V4-Pro better for coding overall? Neither, and that is the point. DeepSeek-V4-Pro is the stronger competitive programming model, with 93.5 percent on LiveCodeBench and a Codeforces rating around 3206. GLM-5.2 is the stronger long horizon agent, leading on SWE-Bench Pro, Terminal-Bench 2.1, FrontierSWE, and DeepSWE. Match the model to the shape of your work.

How much cheaper is DeepSeek-V4-Pro? Roughly five times cheaper per output token at first party rates, about $0.87 per million versus $4.40 for GLM-5.2. That advantage is largest on short, one shot tasks and shrinks on long agent runs where GLM-5.2 can reach a result in fewer trajectories.

Are both models really open weights? Yes. Both carry MIT licenses and 1M token context windows, so you can self host, fine tune, and deploy either one. DeepSeek-V4-Pro supports up to 384K output tokens, GLM-5.2 up to 128K.

Should I use GLM-5.3 instead of GLM-5.2? GLM-5.3 shipped on August 14, 2026 as an incremental step over 5.2. Benchmark it against 5.2 on your own tasks before switching, because point releases this close together usually move a handful of benchmarks rather than all of them.

What about GPT-5.6 or the other closed models? Closed frontier models still lead several benchmarks, but they are not open weights, so you cannot self host or fine tune them. If your requirement is a model you fully control, GLM-5.2 and DeepSeek-V4-Pro are the two to weigh against each other first.