GLM-5.2 vs DeepSeek V4: an honest comparison.

Quick answer: quality ceiling vs cost floor.

If you need the strongest launch-reported coding results in the open-weight world and your workload is agentic, GLM-5.2 is the default candidate. If your workload is high-volume, long-context, or cost-dominated, DeepSeek V4 is very hard to argue against: its published rates sit at roughly a tenth of GLM-5.2's on input and a fraction on output, and the quality gap on everyday tasks is far smaller than that price gap. Neither answer is universal. GLM-5.2 bills noticeably more output per task than its predecessor on at least one independent measurement, which narrows its practical value on volume work. DeepSeek V4 shipped as a preview in April 2026 and remains one, which matters to teams that want a stable target. The sections below take the dimensions one at a time, with sources.

The verified spec sheet, side by side.

Both families are open-weight MoE models released in 2026 under MIT licenses, and both are reasoning models with a thinking mode. The differences are in shape and posture: DeepSeek V4 Pro is the larger network by total parameters, GLM-5.2 activates more parameters per token, and the two vendors publish very different price points. Figures below come from Z.ai's docs and release notes, DeepSeek's API docs and launch announcement, and independent launch coverage, as of July 2026.

Coding: GLM-5.2 holds the reported edge.

Z.ai built GLM-5.2's launch story around coding agents, and the launch-coverage numbers back the positioning: MarkTechPost reported a SWE-bench Pro score of 62.1 for GLM-5.2 against 58.6 for GPT-5.5, which would make it the open-weight leader on that benchmark at release. Z.ai's own release notes claim open-source state of the art on coding and long-horizon task benchmarks. DeepSeek's counterclaim is broader and vendor-made: the V4 announcement says Pro rivals top closed-source models and leads open models on math, STEM, and coding evaluations. Both sets of numbers are launch-season claims from interested parties, one relayed by press, one first-party, and neither substitutes for your own evaluation set. The honest read: for hard, agentic coding work, GLM-5.2 has the stronger published case right now. For everyday coding at volume, refactors, test generation, boilerplate, review comments, DeepSeek V4 Flash finishing 90% of the same tickets at roughly a fiftieth of the token price is frequently the better engineering decision. Where the line sits for your repo is an afternoon of A/B runs.

Agents and long-horizon work: two different bets.

A pattern several teams land on: GLM-5.2 as the planner or hard-step model, DeepSeek V4 Flash as the high-volume executor. Behind one endpoint that split is two model strings in the same loop, not two vendor integrations.

• GLM-5.2 inherits the family's autonomy focus: Z.ai claimed up to 8-hour independent runs for GLM-5.1, and 5.2 extends that long-horizon positioning with tunable thinking effort for deciding how hard each step reasons.
• DeepSeek V4's agent story is throughput: a 384K max output for long tool loops, published concurrency ceilings of 2,500 parallel requests on Flash and 500 on Pro, and automatic prefix caching on its direct platform that discounts repeated context.
• Output-volume caution on GLM-5.2: one independent reviewer measured about 43K output tokens per task against roughly 26K for GLM-5.1 on the same agent benchmark. More thinking per step is a feature on hard problems and a tax on easy ones.
• Preview caution on DeepSeek V4: the vendor still labels the release a preview with no announced date for a stable version, so pin behavior with your own regression evals rather than assuming a frozen target.

Long context: check what your route actually serves.

Both vendors publish 1M-token contexts. Through the APIsRouter catalog the DeepSeek V4 ids serve a 1M window, while the glm-5.2 entry lists 200K, so as called from here, DeepSeek is the long-context pick: whole-repository questions, large document sets, and marathon conversations fit natively where GLM-5.2 traffic needs chunking or summarization above 200K. Two operational notes apply to both. Long contexts bill as input on every request, so a 500K-token conversation resent each turn dominates any per-token price difference between the two families. And max output is not symmetric: DeepSeek's published 384K output ceiling gives long generation tasks, transcripts, codebases, data files, room that GLM-5.2's 128K cap does not. Caching posture is the third long-context variable, and both vendors have one on their own platforms: DeepSeek caches prompt prefixes automatically with a published cache-hit rate far below its miss rate, and Z.ai publishes a cached-input rate of $0.26 per million for GLM-5.2. Both discounts apply on direct platform calls rather than through the gateway, so for repeated-prefix workloads at extreme scale they belong in the total-cost comparison alongside the base rates.

Pay-as-you-go · transparent per-model pricing

Selected models are priced below official list prices. Exact input, output, cache, and per-request prices are shown for each model.

Which one, for which workload.

When the A/B runs, read three numbers per side rather than one. Quality on your rubric decides whether a model is admissible at all. completion_tokens per completed task converts quality into cost honestly, because both families bill reasoning as output and their thinking volumes differ. And wall-clock latency decides which interactive seats each model can hold, independent of price. A model that wins two of three usually wins the seat; a model that wins only the benchmark you did not run yourself wins nothing.

• Hard agentic coding, complex refactors, long-horizon plans: GLM-5.2 first. Its launch-reported benchmark edge is exactly on this ground.
• High-volume chat, extraction, summarization, batch pipelines: DeepSeek V4 Flash first. The price floor is the feature, and quality is competitive on this class of task.
• Deep reasoning at volume: DeepSeek V4 Pro is the middle path, well under GLM-5.2's rate with the vendor's strongest quality claims behind it.
• Contexts past 200K as served here: DeepSeek V4, by catalog window.
• Tightly budgeted output: watch both. Each family runs thinking modes that bill reasoning as output; set max_tokens with headroom and log completion_tokens either way.
• Undecided: run both. The A/B below is the whole setup.

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["APISROUTER_API_KEY"],
    base_url="https://api.apisrouter.com/v1",
)

PROMPT = "Refactor this function and explain the change: ..."

for model in ("glm-5.2", "deepseek-v4-pro", "deepseek-v4-flash"):
    r = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": PROMPT}],
        max_tokens=4000,
    )
    print(model, r.usage.completion_tokens, "output tokens")

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