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GLM-5.2 vs GLM-5.1: Every Generation-Over-Generation Gain

GLM-5.2 vs GLM-5.1 is the one comparison on this site with no ambiguity: across all 19 published evaluations where both models have a score, the newer GLM model wins every row. What makes the comparison interesting is not who wins but by how much, because the margins vary from four tenths of a point to more than forty.

That spread is the clearest available evidence of what the generation actually changed. Standard reasoning moved a little, standard coding moved moderately, and long-horizon agentic execution moved enormously — which is exactly what you would expect from a release whose headline changes are a five-fold context increase and a rebuilt sparse-attention stack. All figures are as published by the model authors.

The Verdict

GLM-5.2 wins 19 of 19GLM-5.1 wins 0

GLM-5.2 wins 19 of 19 published rows against GLM-5.1. The small margins are on benchmarks that were already near ceiling — HMMT Nov. 2025 moves 94.0 to 94.4 and HLE with tools 52.3 to 54.7. The large ones are on long-horizon work: FrontierSWE Dominance 30.5 to 74.4, SWE-Marathon 1.0 to 13.0, DeepSWE 18.0 to 46.2 and CritPt 4.6 to 20.9. Terminal-Bench 2.1 moves 63.5 to 81.0 under Terminus-2 and 69.0 to 82.7 under Claude Code. If you are running GLM-5.1 today and your workload is agentic, this is not a marginal upgrade.

GLM-5.2 vs GLM-5.1: Specs at a Glance

The structural changes explain the benchmark spread. The context ceiling moved from 200,000 to 1,000,000 tokens, sparse attention was rebuilt so that every four layers share one lightweight indexer, the multi-token-prediction layer's training and inference KV-cache mismatch was removed, and explicit effort levels were introduced. Both releases remain MIT-licensed open-weight GLM models on the same public hosts.

GLM-5.2 vs GLM-5.1: Specs at a Glance
FeatureGLM-5.2GLM-5.1
Context window1,000,000 tokens200,000 tokens
Attention architectureSparse attention with IndexShareSparse attention, indexer per layer
MTP acceptance length5.47 (+20%)4.56 (baseline)
Effort controlExplicit — Non-Thinking, High, MaxNot exposed
LicenseMIT licenseMIT license

Benchmark-by-Benchmark: GLM-5.2 vs GLM-5.1

Read the coding block first: nine rows, nine wins, and the increases grow with task length. SWE-bench Pro gains 3.7 points, ProgramBench 12.8, Terminal-Bench 2.1 17.5, DeepSWE 28.2 — a 2.6× improvement — and FrontierSWE Dominance 43.9. The reasoning block is a quieter sweep: HLE gains 9.5, AIME 2026 3.9, IMOAnswerBench 7.2, GPQA-Diamond 5.0 and CritPt jumps from 4.6 to 20.9, more than four times its previous score. The ordering of those deltas is the whole argument for the release. If the generation had simply been trained longer on more data you would expect roughly uniform gains across the table; instead the increases track how long each evaluation's tasks run, which is what you would predict from a five-fold context increase and an attention rebuild aimed squarely at long-horizon work.

Benchmark-by-Benchmark: GLM-5.2 vs GLM-5.1
BenchmarkGLM-5.2GLM-5.1Winner
Coding & long-horizon
FrontierSWE Dominance74.430.5GLM-5.2
PostTrainBench34.320.1GLM-5.2
SWE-Marathon13.01.0GLM-5.2
SWE-bench Pro62.158.4GLM-5.2
NL2Repo48.942.7GLM-5.2
ProgramBench63.750.9GLM-5.2
DeepSWE46.218.0GLM-5.2
Terminal-Bench 2.1 (Terminus-2)81.063.5GLM-5.2
Terminal-Bench 2.1 (Claude Code)82.769.0GLM-5.2
Reasoning & agentic
MCP-Atlas (public set)76.871.8GLM-5.2
Tool-Decathlon48.240.7GLM-5.2
HLE40.531.0GLM-5.2
HLE w/ Tools54.752.3GLM-5.2
CritPt20.94.6GLM-5.2
AIME 202699.295.3GLM-5.2
HMMT Nov. 202594.494.0GLM-5.2
HMMT Feb. 202692.582.6GLM-5.2
IMOAnswerBench91.083.8GLM-5.2
GPQA-Diamond91.286.2GLM-5.2

* score on the full set.

All figures on this page are results as published by the model authors. glmmodel.com reports them; it does not run these evaluations.

See all 17 GLM benchmarks in detail →

When to Pick Each Model

When to pick GLM-5.2

For any new deployment, GLM-5.2 is the default. It wins every published row, carries five times the context, exposes effort levels so you can spend compute deliberately, and serves that larger window with 2.9× lower per-token FLOPs thanks to IndexShare — so the bigger model is not proportionally more expensive per token.

  • You are starting a new project, where there is no published reason to choose the older release.
  • Your agents run for hours — the long-horizon rows improved by tens of points, not fractions.
  • You need more than 200,000 tokens of context and want the window to stay usable across its full length.
  • You want per-request effort control, which GLM-5.1 does not expose at all.

When GLM-5.1 still makes sense

There is exactly one honest argument for staying on GLM-5.1, and it is operational rather than qualitative. A 200,000-token window costs far less KV-cache memory to serve than a million-token one, so if your tasks genuinely fit and your hardware is tight, the older model has a smaller footprint. On bounded coding work the published gap is only a few points.

  • Your serving hardware is memory-constrained and your prompts fit comfortably inside 200,000 tokens.
  • You already have a validated GLM-5.1 deployment and no long-horizon workload to justify revalidation.
  • Your tasks are bounded, where the published SWE-bench Pro gap is 62.1 to 58.4 rather than something dramatic.
  • You need a stable pinned model and prefer to schedule the migration rather than take it now.

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GLM-5.2 vs GLM-5.1 FAQ

What changed between GLM-5.1 and GLM-5.2?
Four things: the context ceiling moved from 200,000 to 1,000,000 tokens, sparse attention was rebuilt around IndexShare so every four layers share one indexer, the multi-token-prediction layer's KV-cache mismatch was removed to raise acceptance length from 4.56 to 5.47, and explicit Non-Thinking, High and Max effort levels were introduced.
How much bigger is the GLM-5.2 context window?
Five times bigger — 1,000,000 tokens against 200,000 — with up to 128,000 output tokens. The model authors describe the larger window as solid rather than merely accepted, meaning it was trained on long coding-agent trajectories so quality holds deep into the context.
What is IndexShare and why does it matter here?
IndexShare lets every four sparse-attention layers share one lightweight indexer, computing top-k indices once and reusing them. That removes indexer work in three of every four layers and cuts per-token FLOPs by 2.9× at 1M context length, which is what makes serving a million-token window practical rather than theoretical.
Should I upgrade from GLM-5.1 to GLM-5.2?
If your workload is agentic or long-horizon, yes — the published gains there are tens of points, not a few. If your tasks are bounded and your serving hardware is memory-constrained, the older model's smaller KV-cache footprint is a legitimate reason to wait.
Are both GLM models still open source?
Yes. Both GLM-5.1 and GLM-5.2 are MIT-licensed open-weight releases published on HuggingFace and ModelScope with no regional restrictions. A newer release does not withdraw an older one — weights you already hold stay usable indefinitely.

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