| GLM-5.2 | DeepSeek V4-Pro | |
|---|---|---|
| Intelligence Indexcomposite · Artificial Analysis v4.1 · independent | 51open-weight #1 | 44 |
| Real-world agentic workGDPval-AA v2, Elo · independent | 1524 | 1328 |
| Repo-scale codingSWE-bench Pro · vendor-reported | 62.1% | 55.4% |
| Terminal agentsTerminal-Bench Hard · independent | 50.8 | 46.2 |
| Reasoning with toolsHumanity's Last Exam · vendor-reported | 54.7 | 48.2 |
| Long-context reasoningAA-LCR · independent | 71.3 | 66.3 |
| Instruction followingIFBench · independent | 73.3 | 76.5 |
| Competition mathHMMT Feb 2026 · vendor-reported | 92.5 | 95.2 |
| Competitive programmingLiveCodeBench · vendor-reported | not published | 93.5#1 global claim |
| Serving speedmedian across providers · Artificial Analysis | 174 tok/sTTFT 1.4s | 62 tok/sTTFT 1.9s |
| Output priceper 1M tokens, first-party API | $4.40 | $0.87 |
Eight weeks apart this spring, the two strongest open-weight models ever released landed on Hugging Face under the same license. DeepSeek V4-Pro arrived in April: 1.6 trillion total parameters, 49B active, MIT. GLM-5.2 answered in June: 744B total, 40B active, MIT, and took the #1 open-weight slot on the Artificial Analysis Intelligence Index. If you are picking the frontier tier of a sovereign stack, this is the choice — and most of what's written about it is leaderboard soup.
So before the numbers: how to read them like an adult. Both labs publish their own benchmark tables, and both tables are marketing. The only apples-to-apples rows are the ones an independent harness measured — Artificial Analysis, mostly — and even there you have to check the version: V4-Pro scored 52 on the April index and 44 on July's v4.1, not because the model got worse but because the index changed. Terminal-Bench numbers circulate across two incompatible versions. DeepSeek's non-thinking-mode scores get quoted as headline numbers by careless aggregators. And one more asymmetry worth naming: the US standards body CAISI ran DeepSeek V4-Pro on private evals in April and found it performed below its self-reported benchmarks; GLM-5.2 simply hasn't been audited by anyone comparable yet. Absence of evidence, not evidence of absence.
With that filter on, the table above is what survives — and it splits cleanly.
Where GLM-5.2 wins: the long game
Every independent composite has GLM-5.2 ahead: Intelligence Index 51 vs 44, GDPval — the benchmark closest to actual white-collar work — by nearly 200 Elo. But the decisive numbers are the long-horizon ones. On the vendors' shared table, SWE-bench Pro goes to GLM by 6.7 points, and on repo-scale suites the gap turns silly: FrontierSWE 74.4 vs 29.0, DeepSWE 46.2 vs 8.0. Whatever Z.ai optimized for, it was agents that keep their footing across many steps — which matches what practitioners report. The most-cited Hacker News verdict called GLM-5.2 "the third model, after Opus and GPT-5.5, that sustains agentic work without drifting"; another built a working multimodal Rust agent over a weekend for $20 in tokens. It's also, by community Elo, the best frontend/UI-generation model in the world right now — closed models included.
Two operational advantages compound this. GLM-5.2 serves roughly three times faster (174 vs 62 tok/s median, with speed hosts pushing 280–420 tok/s), which matters more in agent loops than anywhere else. And Z.ai sells a flat-rate Coding Plan ($18–160/month) through an Anthropic-compatible endpoint — a drop-in for Claude Code that turns the model's one real vice, verbosity, into someone else's problem.
The vice is real, though: at max effort GLM-5.2 spends ~43k output tokens per hard task, and its output tokens cost five times DeepSeek's.
Where DeepSeek V4-Pro wins: the bill, and the sprints
DeepSeek's case starts with the price sheet and never really leaves it:
| GLM-5.2 | DeepSeek V4-Pro | |
|---|---|---|
| API list priceinput / output per 1M tokens | $1.40 / $4.40 | $0.435 / $0.87 |
| Cache-hit input | $0.26−81% | $0.0036−99% — agent loops nearly free on input |
| One agentic coding task40K in / 8K out, modeled | ~$0.09 | ~$0.024 |
| Running the full AA indexsame work, same effort | $826 | $176 |
| Verbosity at max efforttotal output tokens, AA-measured | 140M~43k per task | 180Mmore verbose — but 5× cheaper per token |
| Max output length | 128K | 384K |
| Flat-rate option | Coding Plan $18–160/moClaude Code drop-in | pay-as-you-go only |
The same modeled coding task costs roughly four times less on DeepSeek; running the entire AA index cost $176 against GLM's $826. At volume — and agentic workloads consume ~15× the tokens of chat — that ratio is the architecture decision. The cache economics deserve their own sentence: DeepSeek prices cache-hit input at a 99% discount, which makes the resend-heavy pattern of agent loops nearly free on the input side. Nothing else on the market, open or closed, matches that.
On capability, DeepSeek keeps the sprints. Competition math (HMMT) and instruction-following (IFBench, independent) go to V4-Pro, and in competitive programming it stands unopposed — LiveCodeBench 93.5 and a 3206 Codeforces rating against a GLM column that is simply empty. It also publishes across far more categories (SWE-bench Verified 80.6%, MMLU-Pro, 1M-token retrieval, browsing) where Z.ai has released nothing — a transparency asymmetry that cuts in DeepSeek's favor even when the numbers can't be compared.
And then there is the wildcard: V4-Flash, the 284B/13B sibling, at $0.14/$0.28 — by June it carried 70% of DeepSeek's agentic traffic on OpenRouter, and more than one team reports it beating Pro in their own harnesses. Any GLM-vs-DeepSeek decision that ignores Flash is incomplete.
The honest headline isn't "which model is smarter." It's: GLM-5.2 is the best open-weight agent you can run today, and DeepSeek V4 is the best price-performance in the history of this market. Those are different purchases.
Self-hosting: closer than the parameter counts suggest
The 1.6T vs 744B gap looks decisive and mostly isn't, because sparsity and quantization do the work. GLM-5.2 at FP8 wants one 8×H200 node (a 4-bit build fits 8×H100); DeepSeek V4-Pro ships a ~960GB mixed-precision checkpoint that runs on 8×B300, or on 8×H200 with context capped at 800K — same class of box, one generation apart. V4-Pro's architecture is remarkably lean at long context (a tenth of the KV cache of its predecessor), which shows up as cheaper serving at 1M tokens.
Below a full node, the choice makes itself: V4-Flash runs on 2×H200, has near-lossless quantized builds in ~162GB, and is the only variant of either family you can realistically LoRA-tune on a single node. GLM-5.2's hobbyist quants exist but the practitioner consensus is blunt — usable throughput starts around $80k of hardware. For the full buy-vs-rent math, see the sovereign stack.
The verdict, by workload
| Deploy | Because | |
|---|---|---|
| Long-horizon agents, repo-scale engineering | GLM-5.2or the $18–160/mo Coding Plan | Wins every independent agentic composite; sustains multi-step work without drifting |
| High-volume agents on a budget | DeepSeek V4 — start with Flash$0.14 / $0.28 | 5× cheaper output, 99% cache discount; Flash carries 70% of DeepSeek's agentic traffic |
| Algorithmic coding, competition math | DeepSeek V4-Promax effort | LiveCodeBench 93.5, Codeforces 3206 — GLM doesn't even publish these |
| Frontend and UI generation | GLM-5.2 | Community-rated top frontend model in the world, closed models included |
| Latency-sensitive products | GLM-5.2 | ~3× faster serving, faster first token, 400+ tok/s on speed hosts |
| Self-hosting below a full 8-GPU node | DeepSeek V4-Flash2×H200 | Only variant of either family that fits — and the only one you can LoRA on one node |
If you need one sentence for the steering committee: buy GLM-5.2 when the constraint is capability per agent-step, buy DeepSeek when the constraint is cost per resolved task — and benchmark V4-Flash before assuming you need either giant.
Frequently asked questions
Which is better, GLM-5.2 or DeepSeek V4?
On independent composite benchmarks GLM-5.2 leads (Artificial Analysis Intelligence Index 51 vs 44, GDPval-AA 1524 vs 1328) and dominates long-horizon agentic coding. DeepSeek V4-Pro wins competition math, instruction following, competitive programming, and everything price-related. For sustained agents pick GLM-5.2; for cost-sensitive volume or algorithmic work pick DeepSeek.
How much cheaper is DeepSeek V4 than GLM-5.2?
First-party API prices: DeepSeek V4-Pro is $0.435/$0.87 per 1M tokens (input/output) vs GLM-5.2's $1.40/$4.40 — about 3× cheaper on input and 5× on output, with a 99% cache-hit discount ($0.0036) that GLM doesn't match. The same modeled agentic task costs ~$0.024 on DeepSeek vs ~$0.09 on GLM. GLM's counter is a flat-rate Coding Plan from $18/month.
Can you self-host GLM-5.2 and DeepSeek V4?
Yes — both are MIT-licensed. GLM-5.2 needs one 8×H200 server at FP8 (a 4-bit build fits 8×H100); DeepSeek V4-Pro needs 8×B300, or 8×H200 with context capped at 800K. The practical self-host pick is DeepSeek V4-Flash: 2×H200, quantized builds from ~92GB, and single-node LoRA fine-tuning.
Is GLM-5.2 as good as Claude or GPT-5.5?
Close on some axes, not at the frontier overall. GLM-5.2 beats GPT-5.5 on SWE-bench Pro (62.1 vs 58.6, vendor-reported) and tops the frontend arena, but the closed frontier still leads composite indexes (~60 vs 51) and long-horizon autonomy. Practitioners describe it as the third model that sustains agentic work, after Claude Opus-class and GPT-5.5 — at a fraction of the price.