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GLM-5 (Reasoning)

Z.AICurrentReleased Mar 1, 2026
Overall Score
Est. 82Prov. #17 of 115
Arena Elo
1456
Categories Ranked
8of 8
Price (1M tokens)
$1 in / $3.2 out
Speed
N/A
Context
200K
Open WeightSelf-hostReasoning
Confidence
reasoning

According to BenchLM.ai, GLM-5 (Reasoning) ranks #17 out of 115 models on the provisional leaderboard with an overall score of 82/100. It does not yet have enough sourced coverage for BenchLM's verified leaderboard. This places it in the mid-tier of AI models, with strengths in specific benchmark categories.

GLM-5 (Reasoning) is a open weight model with a 200K token context window. It uses explicit chain-of-thought reasoning, which typically improves performance on math and complex reasoning tasks at the cost of higher latency and token usage.

GLM-5 (Reasoning) sits inside the GLM-5 family alongside GLM-5, GLM-5.1, GLM-5V-Turbo, GLM-5-Turbo. This profile currently has 1 of 193 tracked benchmarks. BenchLM only exposes non-generated benchmark rows publicly, so missing categories stay blank until a sourced evaluation is available.

Its strongest category is Mathematics (#10), while its weakest is Instruction Following (#29). This performance profile makes it particularly strong for mathematical reasoning, scientific computing, and quantitative analysis.

Ranking Distribution

Category rank across 8 benchmark categories — sorted by best rank

Category Performance

Scores across all benchmark categories (0-100 scale)

Category Breakdown

Agentic

#11
82.1/ 100
Weight: 22%0 benchmarks
Terminal-Bench 2.0BrowseCompOSWorld-VerifiedGAIATAU-benchWebArena

Coding

#29
74.7/ 100
Weight: 20%1 benchmark
SWE-bench VerifiedLiveCodeBenchSWE-bench ProSWE-RebenchSciCode

Reasoning

#11
87.1/ 100
Weight: 17%0 benchmarks
MuSRLongBench v2MRCRv2ARC-AGI-2

Knowledge

#14
83.0/ 100
Weight: 12%0 benchmarks
GPQASuperGPQAMMLU-ProHLEFrontierScienceSimpleQA

Math

#10
92.4/ 100
Weight: 5%0 benchmarks
AIME 2025BRUMO 2025MATH-500FrontierMath

Multilingual

#19
81.7/ 100
Weight: 7%0 benchmarks
MGSMMMLU-ProX

Multimodal

#23
71.9/ 100
Weight: 12%0 benchmarks
MMMU-ProOfficeQA ProCharXivCharXiv w/o tools

Inst. Following

#29
82.2/ 100
Weight: 5%0 benchmarks
IFEvalIFBench

Chatbot Arena Performance

Text Overall1456CI: ±6.011,101 votes
Coding1491CI: ±11.62,492 votes
Math1455CI: ±21.0717 votes
Instruction Following1445CI: ±10.43,132 votes
Creative Writing1442CI: ±14.61,725 votes
Multi-turn1457CI: ±13.91,747 votes
Hard Prompts1476CI: ±7.66,159 votes
Hard Prompts (English)1480CI: ±10.92,816 votes
Longer Query1462CI: ±10.43,140 votes

Benchmark Details

Only benchmark rows with an attached exact-source record are shown here. Source-unverified manual rows and generated rows are hidden from model pages.

Compare This Model

See how GLM-5 (Reasoning) stacks up against similar models

Frequently Asked Questions

How does GLM-5 (Reasoning) perform overall in AI benchmarks?

GLM-5 (Reasoning) currently ranks #17 out of 115 models on BenchLM's provisional leaderboard with an overall score of 82 (estimated). It is created by Z.AI and features a 200K context window.

Is GLM-5 (Reasoning) good for coding and programming?

GLM-5 (Reasoning) ranks #29 out of 115 models in coding and programming benchmarks with an average score of 74.7. There are stronger options in this category.

Is GLM-5 (Reasoning) open source?

Yes, GLM-5 (Reasoning) is an open weight model created by Z.AI, meaning it can be downloaded and run locally or fine-tuned for specific use cases.

Which sibling models are related to GLM-5 (Reasoning)?

GLM-5 (Reasoning) belongs to the GLM-5 family. Related variants on BenchLM include GLM-5, GLM-5.1, GLM-5V-Turbo, GLM-5-Turbo.

Does GLM-5 (Reasoning) have full benchmark coverage on BenchLM?

Not yet. GLM-5 (Reasoning) currently has 1 published benchmark scores out of the 193 benchmarks BenchLM tracks. BenchLM only exposes non-generated public benchmark rows, so missing categories stay blank until a sourced evaluation is available.

What is the context window size of GLM-5 (Reasoning)?

GLM-5 (Reasoning) has a context window of 200K, which determines how much text it can process in a single interaction.

Last updated: May 13, 2026 · Runtime metrics stay blank until BenchLM has a sourced snapshot.

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