GLM-5.2 vs DeepSeek V4 Pro: Endurance or Raw Problem Solving

Unusually for a model comparison, the licence is not a differentiator here. GLM-5.2 and DeepSeek V4 Pro are both published under MIT — no revenue thresholds, no regional clauses, no separate agreements. Whatever you want to do with either set of weights, you can.

That clears the ground for the comparison that matters, which is that these two models were optimised for genuinely different tasks and their benchmark profiles barely overlap.

Two benchmark profiles, almost no intersection

DeepSeek's official V4 report puts V4-Pro-Max at 80.6% on SWE-bench Verified, 93.5 on LiveCodeBench, 90.1 on GPQA Diamond, 95.2 pass@1 on AIME 2025, and a Codeforces rating of 3206 — which would place it inside the top few dozen human competitors.

Z.ai's numbers for GLM-5.2 are somewhere else entirely: 81.0 on Terminal-Bench 2.1 under Terminus-2 (82.7 best reported), 62.1 on SWE-bench Pro, 74.4 on FrontierSWE, 46.2 on DeepSWE, 34.3 on PostTrainBench, 76.8 on the public MCP-Atlas set and 48.2 on Tool-Decathlon.

Notice what each lab chose to measure. DeepSeek picked evaluations where a problem has a checkable answer: competitive programming, mathematics, graduate science questions. Z.ai picked evaluations where an agent has to survive hundreds of tool calls in a terminal and still be pointed in the right direction at the end.

Both sets are vendor-run, and vendor scaffolds reliably score above standardised harnesses. But benchmark selection is itself informative — labs benchmark what they trained for.

The architectural difference maps onto that

DeepSeek V4 Pro is 1.6T total parameters with 49B active, an activation ratio around 3%. GLM-5.2 is roughly 744B total (Z.ai's post says 753B) with about 40B active, closer to 5%. Both offer 1M-token context windows.

The forward passes are comparable in cost; the memory footprints are not. V4 Pro needs more than twice the memory to hold, which is part of why the practical meaning of MIT differs between them — GLM-5.2 is closer to something a single well-provisioned node can serve.

Both expose two reasoning effort levels, so neither locks you into one latency profile.

The price gap is real and points one way

DeepSeek prices V4 Pro at roughly $0.435 per million input tokens and $0.87 per million output, following a permanent 75% cut applied at the end of May 2026. Artificial Analysis reports a blended rate of about $0.18 per million on a 7:2:1 cache-hit / input / output mix.

Z.ai lists GLM-5.2 at around $1.40 and $4.40 through its official API, with third-party providers cheaper. Call it three to five times V4 Pro at list rates.

Per-token price is the wrong comparison for agent work, though. What you pay is turns multiplied by tokens per turn multiplied by price. A model that grinds out a task in sixty turns at $0.87 per million output can easily cost more than one that finishes in twenty-five at $4.40 — and it costs far more in wall-clock time and in your attention. The Terminal-Bench gap is precisely a claim about turn efficiency on long tasks.

The inverse holds for short work. On a self-contained algorithm where V4 Pro succeeds first time, GLM-5.2 has nothing to amortise its price premium against.

How each one fails

Failure modes are more useful than scores, because they tell you what your review process has to catch.

Reporting on V4 Pro consistently identifies weakness in factual recall and in hard multi-step reasoning where no intermediate step is independently verifiable. That is the shadow of its training profile: strong where an answer can be checked, less reliable where it cannot. In practice you get confident, fluent, wrong hypotheses about unfamiliar systems — expensive to review precisely because they read well.

GLM-5.2's weak spot is visible in its own numbers. SWE-Marathon at 13.0 and PostTrainBench at 34.3 say that "long horizon" still has a hard ceiling. The model is markedly better than its predecessors at holding a plan together, not immune to losing it.

Choosing between them

• Algorithmic and mathematical work — competitive-programming-shaped problems, numerical code, anything with a test that decides correctness. V4 Pro, and the price makes it easy.
• Unattended repository-scale agent runs — migrations, CI fix loops, multi-hour terminal sessions. GLM-5.2, and measure whether the turn savings cover the price gap.
• Bulk cheap work — neither, really. DeepSeek V4-Flash at 284B total and 13B active, around $0.14 and $0.28 per million, undercuts both and lands within a few points of Pro on practical coding.

The measurement that settles it

Split your eval set in two: tasks with a verifiable correct answer, and tasks that require twenty or more tool calls. Run both models across both halves on an identical harness.

If the split matches the benchmark profiles, you have a routing rule rather than a winner, and routing is almost always the right answer. Record cost per completed task, not price per token — it is the only number that reconciles a five-times price gap with a turn-count advantage.