GLM vs Claude: GLM-5.2 vs Opus 4.8 Compared

GLM vs Claude: GLM-5.2 vs Opus 4.8 on 17 Benchmarks

Choosing between the open-weight GLM-5.2 and the proprietary Claude Opus 4.8 is one of the more consequential architectural decisions a team scaling AI agents makes, because the two models differ on far more than score. This GLM vs Claude comparison puts the open-source flagship against the closed frontier leader across 17 published evaluations spanning reasoning, coding and long-horizon agentic work.

Every figure below is a result as published by the model authors. glmmodel.com is an independent reference site; it did not run these evaluations, and the win and loss chips are simple arithmetic on the published numbers rather than any judgement of our own. Read them as reported capability under a specific harness, not as a guarantee of what you will see on your workload.

The Verdict

GLM-5.2 wins 3 of 17Opus 4.8 wins 14

Claude Opus 4.8 is still the frontier leader on this table, taking 14 of the 17 rows including SWE-bench Pro (69.2 against 62.1), NL2Repo (69.7 against 48.9) and SWE-Marathon (26.0 against 13.0). GLM-5.2 takes three: Terminal-Bench 2.1 under the Claude Code harness (82.7 against 78.9), AIME 2026 (99.2 against 95.7) and IMOAnswerBench (91.0 against 83.5). It also comes within a single point on FrontierSWE Dominance. For an open-weight GLM model to be that close on long-horizon agentic engineering — while carrying five times the context window and weights you own outright — is the real story of this comparison.

GLM vs Claude: Specs at a Glance

Before the scores, the structural differences matter more than they usually do, because they decide what you can build rather than how well it performs. The GLM model prioritises context capacity and ownership of the weights; Claude Opus 4.8 is available exclusively as a managed proprietary API. That single divergence determines whether repository-scale prompts, air-gapped deployment and per-request effort control are options at all.

GLM vs Claude: Specs at a Glance
Feature	GLM-5.2	Claude Opus 4.8
Context window	1,000,000 tokens	200,000 tokens
Openness	Open weights	Closed API
License	MIT license	Proprietary
Effort control	Explicit — Non-Thinking, High, Max	Implicit
Self-hosting	Yes — transformers, vLLM, SGLang, xLLM, ktransformers	No — hosted API only

Benchmark-by-Benchmark: GLM vs Claude

The table below groups the 17 evaluations into coding and long-horizon work, then reasoning and agentic tasks. The pattern is consistent: Claude Opus 4.8 leads standard coding by a clear margin, the gap narrows sharply on agentic tool use — one point on the MCP-Atlas public set — and reverses on competition mathematics and on Terminal-Bench once the harness is Claude Code rather than Terminus-2. That last row is worth dwelling on, because it shows how much of a reported score belongs to the harness rather than the model.

Benchmark-by-Benchmark: GLM vs Claude
Benchmark	GLM-5.2	Opus 4.8	Winner
Coding & long-horizon
FrontierSWE Dominance	74.4	75.1	Opus 4.8
PostTrainBench	34.3	37.2	Opus 4.8
SWE-Marathon	13.0	26.0	Opus 4.8
SWE-bench Pro	62.1	69.2	Opus 4.8
NL2Repo	48.9	69.7	Opus 4.8
ProgramBench	63.7	71.9	Opus 4.8
DeepSWE	46.2	58.0	Opus 4.8
Terminal-Bench 2.1 (Terminus-2)	81.0	85.0	Opus 4.8
Terminal-Bench 2.1 (Claude Code)	82.7	78.9	GLM-5.2
Reasoning & agentic
MCP-Atlas (public set)	76.8	77.8	Opus 4.8
Tool-Decathlon	48.2	59.9	Opus 4.8
HLE	40.5	49.8	Opus 4.8
HLE w/ Tools	54.7	57.9	Opus 4.8
AIME 2026	99.2	95.7	GLM-5.2
HMMT Feb. 2026	92.5	96.7	Opus 4.8
IMOAnswerBench	91.0	83.5	GLM-5.2
GPQA-Diamond	91.2	93.6	Opus 4.8

* 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

Picking the GLM model is fundamentally about control, context and data locality. It is the right choice when proprietary code or regulated data cannot be sent to a third-party API, and when the task genuinely needs a repository-scale window rather than a retrieval layer that decides for you which fragments the model gets to see. The published gap to Opus 4.8 on agentic tool use is small enough that for many agent harnesses the practical difference is a rounding error.

You need to run the model on your own hardware, inside a VPC, or in a fully air-gapped environment.
Your prompts genuinely need the 1,000,000-token window — repository-wide analysis, multi-day logs, long agent trajectories.
You want explicit per-task control of compute spend through the Non-Thinking, High and Max effort levels.
You want permanent ownership of the weights under an MIT license, with no risk of a model version being retired underneath you.

When to pick Claude Opus 4.8

Opus 4.8 wins this table for a reason, and pretending otherwise would be dishonest. If your optimisation target is absolute performance on the hardest software-engineering evaluations and you are comfortable building on a managed service, it is the stronger model on most published rows — decisively so on NL2Repo, SWE-Marathon and Tool-Decathlon, where the margins are ten points or more rather than one or two.

You want the highest published score on standard coding evaluations such as SWE-bench Pro, NL2Repo and ProgramBench.
You would rather consume a managed API than operate GPU infrastructure and a serving stack yourself.
Your workload looks like SWE-Marathon or Tool-Decathlon, where Opus 4.8's published lead is large rather than marginal.
Your data governance permits sending context to a third-party provider, so open weights buy you nothing operationally.

GLM vs Claude FAQ

Does GLM-5.2 beat Claude Opus 4.8?

Generally no. Opus 4.8 leads 14 of the 17 published rows on this page. GLM-5.2 wins three — Terminal-Bench 2.1 under the Claude Code harness, AIME 2026 and IMOAnswerBench — and trails by a single point on FrontierSWE Dominance. For an open-weight model, being that close to the closed frontier is the notable result.

Which has the larger context window, GLM or Claude?

GLM-5.2, by a factor of five: a published 1,000,000-token window against 200,000 for Claude Opus 4.8. That difference decides whether repository-scale prompts and very long agent trajectories are possible without a retrieval layer choosing what the model gets to see.

Are both models open source?

No. GLM-5.2 is an open-weight GLM model under an MIT license, downloadable from HuggingFace and ModelScope and runnable on your own hardware. Claude Opus 4.8 is proprietary and available only through a hosted API, so you never hold the weights.

How do GLM and Claude compare on agentic coding?

Closer than the headline table suggests. Opus 4.8 leads the MCP-Atlas public set by one point, 77.8 to 76.8, but holds a much wider lead on Tool-Decathlon at 59.9 to 48.2. On Terminal-Bench 2.1 the answer flips with the harness: 85.0 to 81.0 under Terminus-2, but 82.7 to 78.9 under Claude Code.

Can I try a live model before choosing?

Yes. The homepage carries a free AI playground that streams answers from an open-source LLM with no account and no sign-up. It is not GLM-5.2 and does not use GLM weights — it is there so you can test a live model instantly while you read the published comparison data.