GLM-5.2 vs DeepSeek V4 Pro: Benchmarks, Pricing, and Which One to Actually Use (2026)

Specs Side by Side

On paper these two look like siblings: both Chinese, both MIT-licensed open-weight Mixture-of-Experts models, both carrying a genuine 1M-token context window. The architectural philosophies underneath are different, though, and the difference explains a lot of the benchmark pattern you'll see in a moment.

Why does architecture matter here? Because a 1M-token window is only useful if the model can afford to use it. Z.ai's answer is IndexShare — reusing the same attention indexer across every four sparse attention layers — which the model card says cuts per-token compute by 2.9× at full 1M context. DeepSeek's answer is a hybrid attention system that, per its model card, runs at 27% of the FLOPs and 10% of the KV cache of its predecessor V3.2 at the same length. In plain terms: GLM-5.2 spent its efficiency budget on staying coherent across very long agent trajectories; DeepSeek spent it on making long context cheap to serve. That's the whole comparison in miniature, honestly.

Benchmarks: What the Numbers Actually Say

Start with the one independent, apples-to-apples number available. Artificial Analysis runs its Intelligence Index v4.1 — nine evaluations including GDPval-AA, Terminal-Bench v2.1, Humanity's Last Exam, and GPQA Diamond — under identical conditions across models. GLM-5.2 at max effort scores 51, the highest of any open-weights model. DeepSeek V4 Pro at max reasoning effort scores 44, tied with MiniMax-M3 for second place among open models. For reference, Claude Opus 4.8 sits at 56 and GPT-5.5 at 55, so the gap between these two open models is real but neither is far off the proprietary frontier. That's fact, independently measured.

Everything below this line is vendor-reported, and I'll treat it that way.

Where the two vendors' own tables overlap, GLM-5.2 leads: SWE-bench Pro 62.1 vs 55.4, MCP-Atlas 77.0 vs 73.6, Humanity's Last Exam with tools 54.7 vs 48.2. Worth knowing: the DeepSeek SWE-bench Pro figure has no independent entry on Scale's SEAL leaderboard, so that 6.7-point gap lives entirely inside vendor reporting.

Then there's DeepSeek's uncontested territory — benchmarks GLM-5.2 simply hasn't published. DeepSeek V4 Pro reports 93.5% on LiveCodeBench, the highest score of any model, open or closed. A 3206 Codeforces rating. 90.1% on GPQA Diamond. 95.2% on HMMT. And 80.6% on SWE-bench Verified, the highest open-weights entry. Z.ai skipped Verified entirely and went straight to the harder SWE-bench Pro. My read is that both labs benchmarked to their strengths and stayed quiet where they'd lose — which is exactly why the independent AA number matters more than any single vendor table.

One trap I haven't seen a single other comparison flag: the Terminal-Bench numbers floating around are not comparable. Z.ai reports GLM-5.2 at 81.0 on Terminal-Bench 2.1. DeepSeek's model card reports 67.9 on Terminal-Bench 2.0 — a different version of the benchmark. If you see an article putting those two numbers in the same column, close the tab.

Coding: Two Different Brains

The honest summary is that these models are good at different kinds of coding, and the split is clean enough to route on.

GLM-5.2 is built for the long game. On Z.ai's reported numbers, it hits 74.4% on FrontierSWE — a benchmark measuring open-ended engineering projects at the scale of hours to tens of hours — edging out GPT-5.5 (72.6%) and landing within 1% of Claude Opus 4.8. Its 81.0 on Terminal-Bench 2.1 is a 19-point jump over its own predecessor GLM-5.1. The pattern across every long-horizon eval: this model was trained to hold a plan across a messy, multi-step trajectory without unraveling.

DeepSeek V4 Pro is the algorithmic specialist. LiveCodeBench tests competitive-programming problems — precise, self-contained, correctness-or-nothing — and no model on earth has published a higher score. The 3206 Codeforces rating and 95.2% HMMT tell the same story: given a complete spec, produce a correct, tight solution.

So picture the two workloads. "Read this 200-file service, understand the conventions, propose and execute a refactor" — that's GLM-5.2's home turf, and at roughly 3K tokens per file, 200 files is 600K tokens of context it can actually hold. "Here's a fully specified problem, give me a correct 200-line patch" — DeepSeek does that all day at a fraction of the cost.

The community argument on Hacker News captures the trade-off better than any benchmark. One camp: DeepSeek is cheap enough and good enough for 95% of daily work. The other camp's rebuttal: on long-horizon tasks, failures compound — a model that's "enough for 95%" turns a multi-hour agent run into a mess, because the 5% errors stack. Both are right. They're just describing different jobs.

Pricing: Sticker Price vs Real Bill

Here's where most comparisons are quietly wrong, because DeepSeek's pricing changed twice this year and half the articles ranking for this query still cite April numbers.

The facts first. DeepSeek launched V4 Pro on April 24 at a reference price of $1.74 input / $3.48 output per 1M tokens, with a 75% launch discount. On May 31, that discount became the permanent official price: $0.435 input / $0.87 output per 1M tokens, with cache-hit input at $0.0036 per 1M (DeepSeek's official pricing page). That cache rate matters more than it looks — DeepSeek caches repeated prefixes automatically, so an agent re-sending the same repository context and system prompt on every turn pays roughly 1% of the cache-miss rate on those tokens. For cache-heavy agent workloads, the effective input bill can land well below even the headline $0.435.

GLM-5.2's list rate on Z.ai's standalone API is $1.40 input / $4.40 output per 1M tokens. (Through our GLM-5.2 endpoint it's $1.26 / $3.96 — 10% under list.)

So per token, DeepSeek V4 Pro is roughly 3× cheaper on input and 5× cheaper on output. Case closed? Not quite — and this is the part that headline price tables miss.

Per-token price and per-task cost diverge whenever two models spend different amounts of thinking to finish the same job. GLM-5.2 is a heavy thinker: Artificial Analysis's token-consumption data puts it around 42,000 output tokens per Intelligence Index task at max effort — more than GPT-5.5 uses at its highest setting. Run the arithmetic: 42K output tokens at GLM's $4.40 list rate is about $0.18 of output per task. The same 42K at DeepSeek's $0.87 would be under $0.04 — but DeepSeek's actual per-task token consumption isn't published in comparable form, so treat any per-task cost ratio as an estimate, not a measurement. The direction is clear even if the exact multiple isn't: GLM's real cost premium per completed task is the token-price gap multiplied by its appetite for reasoning tokens. Budget for the bill, not the sticker.

My judgment, for what it's worth: if cost is your binding constraint, this section already made your decision, and it's DeepSeek. The rest of the article is for everyone whose constraint is capability.

Which Fits Your Project?

You're building a repository-scale coding agent. Choose GLM-5.2. The independent intelligence lead (51 vs 44), the long-horizon benchmark pattern, and the 1M context it was specifically trained to use coherently all point the same way. The cost: you'll pay several times more per task, and on quick, well-specified jobs you're paying for depth you don't need.

Your workload is algorithms, math, or STEM reasoning. Choose DeepSeek V4 Pro. Its LiveCodeBench and Codeforces results are the best published anywhere, and you get them at $0.87/1M output. The cost: on open-ended, multi-hour engineering tasks, its lower long-horizon scores suggest more compounding failures — exactly the failure mode the HN skeptics describe.

You're cost-bound and high-throughput. DeepSeek, without much debate — and structure your prompts with stable prefixes so the $0.0036 cache-hit rate does the heavy lifting. The cost: you're accepting the #2 open model on general intelligence, which for classification, extraction, and routine coding you will likely never notice.

How to Access Both via One API

The practical case for running these side by side: both are available through one endpoint with one key, so A/B-testing them on your actual workload is a one-line change. Both use the OpenAI-compatible chat completions format.

import requests
import json
 
url = "https://gptproto.com/v1/chat/completions"
 
def ask(model: str, prompt: str) -> str:
    payload = json.dumps({
        "model": model,
        "messages": [{"role": "user", "content": prompt}],
        "stream": False
    })
    headers = {
        "Authorization": "GPTPROTO_API_KEY",
        "Content-Type": "application/json"
    }
    response = requests.post(url, headers=headers, data=payload)
    return response.json()["choices"][0]["message"]["content"]
 
task = "Refactor this function to be idempotent: ..."
 
# Same request, two models — compare on your own workload
print(ask("glm-5.2", task))
print(ask("deepseek-v4-pro", task))

Or with cURL:

curl --location 'https://gptproto.com/v1/chat/completions' \
--header 'Authorization: GPTPROTO_API_KEY' \
--header 'Content-Type: application/json' \
--data '{
  "model": "glm-5.2",
  "messages": [{"role": "user", "content": "Who are you?"}],
  "stream": false
}'

Swap "glm-5.2" for "deepseek-v4-pro" and the same call hits the other model. The full catalog is at gptproto.com/model.