GLM-5.3 vs DeepSeek-V3.2

Benchmarks

No shared benchmarks

These two models have no benchmark in common with published figures for both, so there is nothing to compare directly. The specification and price rows below are still like for like.

Price

DeepSeek-V3.2 is cheaper

On a 3:1 input-to-output mix, DeepSeek-V3.2 costs $0.315 per million tokens against $2.15 for GLM-5.3 — about 6.8× less. Remember that a reasoning model bills its thinking as output, so cost per answer can diverge much further than cost per token.

What actually differs

GLM-5.3 takes 1M tokens of context against 164K — 6.1× more room for long documents or a large codebase.
Only GLM-5.3 reads images. If your input includes screenshots, charts or documents, that decides it.

Scorecard

Which is better at what

Maths, coding, reasoning and the rest — one line each, averaged over the benchmarks both models actually report.

GLM-5.3 has no published benchmark scores

Its maker has not released figures for any of the evaluations tracked here, so there is nothing to put in a score column. Rather than estimate, infer from a sibling model, or quote the base model's numbers as if they were its own, this page leaves those rows empty and compares what genuinely can be compared: parameters, context window, modalities, licence and cost.

The moment those figures are published they go in — send them with a link to the source.

Which model scores higher in each capability category, averaged over the benchmarks all of them report.
Category	GLM-5.3	DeepSeek-V3.2	Better at this
ReasoningOnly one model reports this	—	—	Not comparable
MathsOnly one model reports this	—	—	Not comparable
CodingOnly one model reports this	—	—	Not comparable
KnowledgeOnly one model reports this	—	—	Not comparable
MultimodalNeither model reports this	—	—	Not comparable
Instruction followingNeither model reports this	—	—	Not comparable
Human preferenceNeither model reports this	—	—	Not comparable
Categories won	No category has a test both models report

Each category averages only the benchmarks every model here reports, so no one is credited for a test the other did not run. A category with no shared test is marked Not comparable rather than guessed at.

Side by side

GLM-5.3 and DeepSeek-V3.2, row by row

Attribute	GLM-5.3Z.ai (Zhipu)	DeepSeek-V3.2DeepSeek
Specification
MakerWho built it	Z.ai (Zhipu)	DeepSeek
Released	2026-08	2025-09
ParametersTotal, and active per token for a mixture of experts	753B total / 39B active	685B total / 37B active
Architecture	MoE	MoE
Context windowHow much can go in at once	1,000,000 tokens	163,840 tokens
Max output	Not reported	65,536 tokens
Input	Text, Image	Text
ReasoningSpends extra tokens thinking before it answers	Yes	Yes
Tool calling	Yes	Yes
Licence	GLM-5.3 License	MIT
Open weightsCan you download and run it yourself	Yes	Yes
Price
Input priceUSD per million tokens in	$1.40	$0.280
Output priceUSD per million tokens out	$4.40	$0.42
Cached input	$0.26	Not reported
Blended 3:1A 3-in-to-1-out million-token mix — a fairer single number than input price alone	$2.15	$0.315Cheapest
Price note	Open weights — this is a representative hosting rate, not a first-party price. Running it yourself costs only hardware.	Open weights — this is a representative hosting rate, not a first-party price. Running it yourself costs only hardware.
Published benchmarks
MMLU-Pro12,000 reasoning-heavy multiple-choice questions across 14 academic subjects, with ten options instead of four. The harder successor to MMLU.	Not reported	85%
GPQA Diamond198 graduate-level physics, chemistry and biology questions written to be Google-proof. PhD holders in the matching field score about 65%.	Not reported	79.9%
AIME 2025The American Invitational Mathematics Examination — 15 problems, integer answers, no partial credit. A standard test of multi-step maths reasoning.	Not reported	89.3%
LiveCodeBenchCompetitive-programming problems collected after each model's training cutoff, so contamination cannot inflate the score.	Not reported	74.1%
Links	Hugging Face · Full page	Hugging Face · Full page
Row verified	2026-09	2026-08

Where these numbers come from

Every score on this page is a published figure, taken from the model's own card, system card, technical report or release post, or from a public leaderboard. CorX Labs did not run these evaluations. Most are self-reported by the lab that built the model, which means they were produced under that lab's own choice of prompt, scaffold and number of attempts — so treat them as a starting point for a shortlist, not as a settled ranking.

A score someone other than the model's maker measured is marked Independent and names its measurer. Those are the stronger numbers on this page — an outside harness has no reason to flatter anyone — and there are not many of them.

Where a figure has not been published, the cell reads Not reported rather than an estimate. Nothing here is inferred, interpolated or guessed. Each model records the month its row was last checked. Full method and caveats.

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Questions

GLM-5.3 or DeepSeek-V3.2?

Is GLM-5.3 better than DeepSeek-V3.2?

These two models have no benchmark in common with published figures for both, so there is nothing to compare directly. The specification and price rows below are still like for like.

Which is cheaper, GLM-5.3 or DeepSeek-V3.2?

On a 3:1 input-to-output mix, DeepSeek-V3.2 costs $0.315 per million tokens against $2.15 for GLM-5.3 — about 6.8× less. Remember that a reasoning model bills its thinking as output, so cost per answer can diverge much further than cost per token.

What is the difference between GLM-5.3 and DeepSeek-V3.2?

GLM-5.3 takes 1M tokens of context against 164K — 6.1× more room for long documents or a large codebase. Only GLM-5.3 reads images. If your input includes screenshots, charts or documents, that decides it.