# GLM-5

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Z.ai

Canonical URL: https://benchize.com/models/glm-5
Last updated: Wed, 21 Oct 2026 18:52:20 GMT

Reproduction and quotation must name Benchize, link to the canonical HTML URL, and retain individual source citations.

Z.ai

## Benchmarks

| Benchmark | Score | ± | Rank | Cost | Source |  |
| --- | --- | --- | --- | --- | --- | --- |
| [HMMT Feb 2026](https://benchize.com/benchmarks/matharena%3Ahmmt--hmmt_feb_2026.md) | 86.36% | 95% CI 80.51–92.21% | 17 / 21 | 0.11usd per problem | [matharena · hmmt--hmmt_feb_2026](https://matharena.ai/competition_tables/hmmt--hmmt_feb_2026) |  |
| ### Original source values \| Metric \| Value \| Unit \| Scope \| Direction \| \| --- \| --- \| --- \| --- \| --- \| \| `accuracy` \| 86.36 \| percent \| competition \| Higher is better \| \| `cost_per_problem` \| 0.11 \| usd_per_problem \| competition \| Lower is better \| ### Configuration competition hmmt--hmmt_feb_2026 predicted false sourceModelName GLM 5 sourceModelVersion glm/glm-5 ### Reported statistics Uncertainty 95% confidence interval: 80.51–92.21% confidenceInterval95 5.85 problems 33 ### Source [Original source](https://matharena.ai/competition_tables/hmmt--hmmt_feb_2026) Source matharena Original model label GLM 5 Source version hmmt--hmmt_feb_2026 Observed 2026-10-21T16:52:38.351Z Evidence SHA-256 `0dce922c33a54d29d4a1872a42d74077b491915c80d8433f4b379ea0b78bb979` Source locator `/table/rows/21` |  |  |  |  |  |  |
| [ArXivMath Overall](https://benchize.com/benchmarks/matharena%3Aoverall--final_answer_comps.md) | 65.65% | 95% CI 63.05–68.25% | 17 / 20 | 0.16usd per problem | [matharena · overall--final_answer_comps](https://matharena.ai/competition_tables/overall--final_answer_comps) |  |
| ### Original source values \| Metric \| Value \| Unit \| Scope \| Direction \| \| --- \| --- \| --- \| --- \| --- \| \| `accuracy` \| 65.65 \| percent \| competition \| Higher is better \| \| `cost_per_problem` \| 0.16 \| usd_per_problem \| competition \| Lower is better \| ### Configuration competition overall--final_answer_comps predicted false sourceModelName GLM 5 sourceModelVersion glm/glm-5 ### Reported statistics Uncertainty 95% confidence interval: 63.05–68.25% confidenceInterval95 2.60 problems Not reported ### Source [Original source](https://matharena.ai/competition_tables/overall--final_answer_comps) Source matharena Original model label GLM 5 Source version overall--final_answer_comps Observed 2026-10-21T16:52:34.395Z Evidence SHA-256 `8b08b20c1ac5aec9a829d234469bc39d40b0eb189790d84b7264c2756ffa161e` Source locator `/table/rows/19` |  |  |  |  |  |  |
| [EQ-Bench 3](https://benchize.com/benchmarks/eq-bench%3Aeqbench3.md) | 1,526elo | CI 1,493.7–1,558.3 | 15 / 50 | — | [eq bench · eqbench3](https://eqbench.com/eqbench3.js) |  |
| ### Original source values \| Metric \| Value \| Unit \| Scope \| Direction \| \| --- \| --- \| --- \| --- \| --- \| \| `elo_norm` \| 1526.0 \| elo \| overall \| Higher is better \| \| `rubric_0_100` \| 75.0 \| percent \| overall \| Higher is better \| ### Configuration interactiveAdjustments none protocol eqbench3 sourceModelVersion zai-org/GLM-5 ### Reported statistics Uncertainty Confidence interval: 1,493.7–1,558.3 confidenceIntervalHigh 1558.3 confidenceIntervalLow 1493.7 ### Source [Original source](https://eqbench.com/eqbench3.js) Source eq bench Original model label zai-org/GLM-5 Source version eqbench3 Observed 2026-10-21T16:35:00.608Z Evidence SHA-256 `3672ff093e739f07ff664a2e6013bc38a6977a146e4092b4cef80ba1780542b2` Source locator `/leaderboardDataEQBench3/21` |  |  |  |  |  |  |
| [AIME 2025](https://benchize.com/benchmarks/aime-2025.md) | 96.67% | 95% CI 93.46–99.88% | 5 / 35 | 0.08usd per problem | [matharena · aime--aime_2025](https://matharena.ai/competition_tables/aime--aime_2025) |  |
| ### Original source values \| Metric \| Value \| Unit \| Scope \| Direction \| \| --- \| --- \| --- \| --- \| --- \| \| `accuracy` \| 96.67 \| percent \| competition \| Higher is better \| \| `cost_per_problem` \| 0.081 \| usd_per_problem \| competition \| Lower is better \| ### Configuration competition aime--aime_2025 predicted false sourceModelName GLM 5 sourceModelVersion glm/glm-5 ### Reported statistics Uncertainty 95% confidence interval: 93.46–99.88% confidenceInterval95 3.21 problems 30 ### Source [Original source](https://matharena.ai/competition_tables/aime--aime_2025) Source matharena Original model label GLM 5 Source version aime--aime_2025 Observed 2026-10-21T16:52:35.082Z Evidence SHA-256 `fab0abcecc4032852ef2cbd2a84d34ffc42202be358b2994bfaba44ae7a0cd9a` Source locator `/table/rows/4` |  |  |  |  |  |  |
| [USAMO 2026](https://benchize.com/benchmarks/matharena%3Ausamo--usamo_2026.md) | 35.12% | 95% CI 16.02–54.22% | 8 / 8 | 0.24usd per problem | [matharena · usamo--usamo_2026](https://matharena.ai/competition_tables/usamo--usamo_2026) |  |
| ### Original source values \| Metric \| Value \| Unit \| Scope \| Direction \| \| --- \| --- \| --- \| --- \| --- \| \| `accuracy` \| 35.12 \| percent \| competition \| Higher is better \| \| `cost_per_problem` \| 0.24 \| usd_per_problem \| competition \| Lower is better \| ### Configuration competition usamo--usamo_2026 predicted false sourceModelName GLM 5 sourceModelVersion glm/glm-5 ### Reported statistics Uncertainty 95% confidence interval: 16.02–54.22% confidenceInterval95 19.10 problems 6 ### Source [Original source](https://matharena.ai/competition_tables/usamo--usamo_2026) Source matharena Original model label GLM 5 Source version usamo--usamo_2026 Observed 2026-10-21T16:52:41.374Z Evidence SHA-256 `2c6f85fa189ac482d54d597e1cbbe64803cda65cd85e78add44087d94799756b` Source locator `/table/rows/8` |  |  |  |  |  |  |
| [CMIMC 2025](https://benchize.com/benchmarks/matharena%3Acmimc--cmimc_2025.md) | 93.12% | 95% CI 89.2–97.04% | 4 / 21 | 0.11usd per problem | [matharena · cmimc--cmimc_2025](https://matharena.ai/competition_tables/cmimc--cmimc_2025) |  |
| ### Original source values \| Metric \| Value \| Unit \| Scope \| Direction \| \| --- \| --- \| --- \| --- \| --- \| \| `accuracy` \| 93.12 \| percent \| competition \| Higher is better \| \| `cost_per_problem` \| 0.11 \| usd_per_problem \| competition \| Lower is better \| ### Configuration competition cmimc--cmimc_2025 predicted false sourceModelName GLM 5 sourceModelVersion glm/glm-5 ### Reported statistics Uncertainty 95% confidence interval: 89.2–97.04% confidenceInterval95 3.92 problems 40 ### Source [Original source](https://matharena.ai/competition_tables/cmimc--cmimc_2025) Source matharena Original model label GLM 5 Source version cmimc--cmimc_2025 Observed 2026-10-21T16:52:36.936Z Evidence SHA-256 `d5d48c1f54a744d7ff2244bae3258963afdb1ab36fb20464ea9cf47138a1555e` Source locator `/table/rows/4` |  |  |  |  |  |  |
| [ArXivMath 02/2026](https://benchize.com/benchmarks/matharena%3Aarxiv--february.md) | 41.41% | 95% CI 32.88–49.94% | 10 / 20 | 0.18usd per problem | [matharena · arxiv--february](https://matharena.ai/competition_tables/arxiv--february) |  |
| ### Original source values \| Metric \| Value \| Unit \| Scope \| Direction \| \| --- \| --- \| --- \| --- \| --- \| \| `accuracy` \| 41.41 \| percent \| competition \| Higher is better \| \| `cost_per_problem` \| 0.18 \| usd_per_problem \| competition \| Lower is better \| ### Configuration competition arxiv--february predicted false sourceModelName GLM 5 sourceModelVersion glm/glm-5 ### Reported statistics Uncertainty 95% confidence interval: 32.88–49.94% confidenceInterval95 8.53 problems 32 ### Source [Original source](https://matharena.ai/competition_tables/arxiv--february) Source matharena Original model label GLM 5 Source version arxiv--february Observed 2026-10-21T16:52:27.963Z Evidence SHA-256 `3bc5b0a1eb91a0bdaa3d0942ebf82327a8664334b225ae3b2dc1d8b4f57efa51` Source locator `/table/rows/10` |  |  |  |  |  |  |
| [ArXivMath 12/2025](https://benchize.com/benchmarks/matharena%3Aarxiv--december.md) | 38.24% | 95% CI 30.07–46.41% | 10 / 12 | 0.16usd per problem | [matharena · arxiv--december](https://matharena.ai/competition_tables/arxiv--december) |  |
| ### Original source values \| Metric \| Value \| Unit \| Scope \| Direction \| \| --- \| --- \| --- \| --- \| --- \| \| `accuracy` \| 38.24 \| percent \| competition \| Higher is better \| \| `cost_per_problem` \| 0.16 \| usd_per_problem \| competition \| Lower is better \| ### Configuration competition arxiv--december predicted false sourceModelName GLM 5 sourceModelVersion glm/glm-5 ### Reported statistics Uncertainty 95% confidence interval: 30.07–46.41% confidenceInterval95 8.17 problems 17 ### Source [Original source](https://matharena.ai/competition_tables/arxiv--december) Source matharena Original model label GLM 5 Source version arxiv--december Observed 2026-10-21T16:52:26.934Z Evidence SHA-256 `6bbceef2706ad55e793e81bd959f7c1ec869f0ad04c6aa9013b2ad7b1a0820e3` Source locator `/table/rows/14` |  |  |  |  |  |  |
| [Longform Creative Writing](https://benchize.com/benchmarks/eq-bench%3Alongform-writing.md) | 70.9% | — | 34 / 93 | — | [eq bench · longform-writing](https://eqbench.com/creative_writing_longform.js) |  |
| ### Original source values \| Metric \| Value \| Unit \| Scope \| Direction \| \| --- \| --- \| --- \| --- \| --- \| \| `overall_score_100` \| 70.9 \| percent \| overall \| Higher is better \| \| `chapter1_avg` \| 15.60 \| rubric_score \| overall \| Higher is better \| \| `chapter2_avg` \| 15.20 \| rubric_score \| overall \| Higher is better \| \| `chapter3_avg` \| 15.17 \| rubric_score \| overall \| Higher is better \| \| `chapter4_avg` \| 14.73 \| rubric_score \| overall \| Higher is better \| \| `chapter5_avg` \| 14.19 \| rubric_score \| overall \| Higher is better \| \| `chapter6_avg` \| 14.25 \| rubric_score \| overall \| Higher is better \| \| `chapter7_avg` \| 13.93 \| rubric_score \| overall \| Higher is better \| \| `chapter8_avg` \| 13.83 \| rubric_score \| overall \| Higher is better \| \| `final_judgement_avg` \| 13.23 \| rubric_score \| overall \| Higher is better \| ### Configuration interactiveAdjustments none protocol longform-writing sourceModelVersion zai-org/GLM-5 ### Source [Original source](https://eqbench.com/creative_writing_longform.js) Source eq bench Original model label zai-org/GLM-5 Source version longform-writing Observed 2026-10-21T16:35:02.575Z Evidence SHA-256 `3ba283ce357ac7620e16db640a961879c30fe3ddc50a27de9a8ed4290784825c` Source locator `/leaderboardDataLongformV3/9` |  |  |  |  |  |  |
| [BrokenArXiv 02/2026](https://benchize.com/benchmarks/matharena%3Aarxiv_false--february.md) | 11.69% | 95% CI 6.03–17.35% | 9 / 15 | 0.09usd per problem | [matharena · arxiv_false--february](https://matharena.ai/competition_tables/arxiv_false--february) |  |
| ### Original source values \| Metric \| Value \| Unit \| Scope \| Direction \| \| --- \| --- \| --- \| --- \| --- \| \| `accuracy` \| 11.69 \| percent \| competition \| Higher is better \| \| `cost_per_problem` \| 0.09 \| usd_per_problem \| competition \| Lower is better \| ### Configuration competition arxiv_false--february predicted false sourceModelName GLM 5 sourceModelVersion glm/glm-5 ### Reported statistics Uncertainty 95% confidence interval: 6.03–17.35% confidenceInterval95 5.66 problems 31 ### Source [Original source](https://matharena.ai/competition_tables/arxiv_false--february) Source matharena Original model label GLM 5 Source version arxiv_false--february Observed 2026-10-21T16:52:23.532Z Evidence SHA-256 `a6bc6fc18da7dd181ed727da924f9569d3e323c31c1aba9a4d9fa3bff492bee3` Source locator `/table/rows/8` |  |  |  |  |  |  |
| [BRUMO 2025](https://benchize.com/benchmarks/matharena%3Abrumo--brumo_2025.md) | 99.17% | 95% CI 97.54–100.8% | 3 / 25 | 0.07usd per problem | [matharena · brumo--brumo_2025](https://matharena.ai/competition_tables/brumo--brumo_2025) |  |
| ### Original source values \| Metric \| Value \| Unit \| Scope \| Direction \| \| --- \| --- \| --- \| --- \| --- \| \| `accuracy` \| 99.17 \| percent \| competition \| Higher is better \| \| `cost_per_problem` \| 0.065 \| usd_per_problem \| competition \| Lower is better \| ### Configuration competition brumo--brumo_2025 predicted false sourceModelName GLM 5 sourceModelVersion glm/glm-5 ### Reported statistics Uncertainty 95% confidence interval: 97.54–100.8% confidenceInterval95 1.63 problems 30 ### Source [Original source](https://matharena.ai/competition_tables/brumo--brumo_2025) Source matharena Original model label GLM 5 Source version brumo--brumo_2025 Observed 2026-10-21T16:52:35.992Z Evidence SHA-256 `d1d1c126b1feff70b2bcc20a7adbf380d94c2aaab0704704cb01593d4e1958ff` Source locator `/table/rows/3` |  |  |  |  |  |  |
| [Apex](https://benchize.com/benchmarks/matharena%3Aapex--apex_2025.md) | 10.94% | 95% CI 6.53–15.35% | 18 / 30 | 0.25usd per problem | [matharena · apex--apex_2025](https://matharena.ai/competition_tables/apex--apex_2025) |  |
| ### Original source values \| Metric \| Value \| Unit \| Scope \| Direction \| \| --- \| --- \| --- \| --- \| --- \| \| `accuracy` \| 10.94 \| percent \| competition \| Higher is better \| \| `cost_per_problem` \| 0.25 \| usd_per_problem \| competition \| Lower is better \| ### Configuration competition apex--apex_2025 predicted false sourceModelName GLM 5 sourceModelVersion glm/glm-5 ### Reported statistics Uncertainty 95% confidence interval: 6.53–15.35% confidenceInterval95 4.41 problems 12 ### Source [Original source](https://matharena.ai/competition_tables/apex--apex_2025) Source matharena Original model label GLM 5 Source version apex--apex_2025 Observed 2026-10-21T16:52:38.728Z Evidence SHA-256 `20b28370d9d7f5a15a19fc9e4226c9857cdb07bc42b32ccec508e687b7b9f3fc` Source locator `/table/rows/20` |  |  |  |  |  |  |
| [HMMT Feb 2025](https://benchize.com/benchmarks/matharena%3Ahmmt--hmmt_feb_2025.md) | 97.5% | 95% CI 94.71–100.29% | 4 / 34 | 0.09usd per problem | [matharena · hmmt--hmmt_feb_2025](https://matharena.ai/competition_tables/hmmt--hmmt_feb_2025) |  |
| ### Original source values \| Metric \| Value \| Unit \| Scope \| Direction \| \| --- \| --- \| --- \| --- \| --- \| \| `accuracy` \| 97.50 \| percent \| competition \| Higher is better \| \| `cost_per_problem` \| 0.09 \| usd_per_problem \| competition \| Lower is better \| ### Configuration competition hmmt--hmmt_feb_2025 predicted false sourceModelName GLM 5 sourceModelVersion glm/glm-5 ### Reported statistics Uncertainty 95% confidence interval: 94.71–100.29% confidenceInterval95 2.79 problems 30 ### Source [Original source](https://matharena.ai/competition_tables/hmmt--hmmt_feb_2025) Source matharena Original model label GLM 5 Source version hmmt--hmmt_feb_2025 Observed 2026-10-21T16:52:35.608Z Evidence SHA-256 `8554225c26eb7a3c2649943ee7cf6b404ec1f7441b20fbdcfccbf35b9a864864` Source locator `/table/rows/5` |  |  |  |  |  |  |
| [Judgemark v2.1](https://benchize.com/benchmarks/eq-bench%3Ajudgemark-v2.md) | 85.58% | — | 3 / 39 | — | [eq bench · judgemark-v2](https://eqbench.com/judgemark-v2.js) |  |
| ### Original source values \| Metric \| Value \| Unit \| Scope \| Direction \| \| --- \| --- \| --- \| --- \| --- \| \| `judgemark_score` \| 85.58 \| percent \| overall \| Higher is better \| \| `stability` \| 0.667 \| score_fraction \| overall \| Higher is better \| \| `separability` \| 0.916 \| score_fraction \| overall \| Higher is better \| \| `human_corr` \| 0.804 \| correlation \| overall \| Higher is better \| ### Configuration interactiveAdjustments none protocol judgemark-v2 sourceModelVersion zai-org/GLM-5 ### Source [Original source](https://eqbench.com/judgemark-v2.js) Source eq bench Original model label zai-org/GLM-5 Source version judgemark-v2 Observed 2026-10-21T16:35:03.564Z Evidence SHA-256 `070cc3fbd6f0333dde2b52bec4329ea3fba7eccbf016bbbbef95e0f8b2197781` Source locator `/leaderboardDataV2/34` |  |  |  |  |  |  |
| [ArXivMath 01/2026](https://benchize.com/benchmarks/matharena%3Aarxiv--january.md) | 54.35% | 95% CI 47.15–61.55% | 12 / 18 | 0.17usd per problem | [matharena · arxiv--january](https://matharena.ai/competition_tables/arxiv--january) |  |
| ### Original source values \| Metric \| Value \| Unit \| Scope \| Direction \| \| --- \| --- \| --- \| --- \| --- \| \| `accuracy` \| 54.35 \| percent \| competition \| Higher is better \| \| `cost_per_problem` \| 0.17 \| usd_per_problem \| competition \| Lower is better \| ### Configuration competition arxiv--january predicted false sourceModelName GLM 5 sourceModelVersion glm/glm-5 ### Reported statistics Uncertainty 95% confidence interval: 47.15–61.55% confidenceInterval95 7.20 problems 23 ### Source [Original source](https://matharena.ai/competition_tables/arxiv--january) Source matharena Original model label GLM 5 Source version arxiv--january Observed 2026-10-21T16:52:27.447Z Evidence SHA-256 `e895f6ccd0711bb1a512b580bab39de8f977c57f83bf492527e4fc710315d706` Source locator `/table/rows/15` |  |  |  |  |  |  |
| [SMT 2025](https://benchize.com/benchmarks/matharena%3Asmt--smt_2025.md) | 91.04% | 95% CI 87.19–94.89% | 6 / 25 | 0.08usd per problem | [matharena · smt--smt_2025](https://matharena.ai/competition_tables/smt--smt_2025) |  |
| ### Original source values \| Metric \| Value \| Unit \| Scope \| Direction \| \| --- \| --- \| --- \| --- \| --- \| \| `accuracy` \| 91.04 \| percent \| competition \| Higher is better \| \| `cost_per_problem` \| 0.077 \| usd_per_problem \| competition \| Lower is better \| ### Configuration competition smt--smt_2025 predicted false sourceModelName GLM 5 sourceModelVersion glm/glm-5 ### Reported statistics Uncertainty 95% confidence interval: 87.19–94.89% confidenceInterval95 3.85 problems 53 ### Source [Original source](https://matharena.ai/competition_tables/smt--smt_2025) Source matharena Original model label GLM 5 Source version smt--smt_2025 Observed 2026-10-21T16:52:36.547Z Evidence SHA-256 `1b4f6f8bafc1688e4557a024513f68befa31aff48fd0623fb4892a61263433b0` Source locator `/table/rows/7` |  |  |  |  |  |  |
| [ARC-AGI-2](https://benchize.com/benchmarks/arc-prize%3Av2_Semi_Private.md) | 4.86% | — | 138 / 212 | 0.27usd per task | [arc prize · v2](https://arcprize.org/media/data/leaderboard/v2.json) |  |
| ### Original source values \| Metric \| Value \| Unit \| Scope \| Direction \| \| --- \| --- \| --- \| --- \| --- \| \| `score` \| 0.0486 \| score_fraction \| v2_Semi_Private \| Higher is better \| \| `cost_per_task` \| 0.27 \| usd_per_task \| v2_Semi_Private \| Lower is better \| ### Configuration evaluationSplit v2_Semi_Private harness Not reported modelGroup Not reported modelType CoT sourceModelVersion glm-5 ### Source [Original source](https://arcprize.org/media/data/leaderboard/v2.json) Source arc prize Original model label GLM-5 Source version v2 Observed 2026-10-21T16:34:20.750Z Evidence SHA-256 `bca68e12cc9a01954ae21b641844c1ecbabe146cb24b87f0f8e9b1db27433c29` Source locator `/evaluations/130` |  |  |  |  |  |  |
| [AIME 2026](https://benchize.com/benchmarks/aime-2026.md) | 96.67% | 95% CI 93.46–99.88% | 6 / 21 | 0.08usd per problem | [matharena · aime--aime_2026](https://matharena.ai/competition_tables/aime--aime_2026) |  |
| ### Original source values \| Metric \| Value \| Unit \| Scope \| Direction \| \| --- \| --- \| --- \| --- \| --- \| \| `accuracy` \| 96.67 \| percent \| competition \| Higher is better \| \| `cost_per_problem` \| 0.075 \| usd_per_problem \| competition \| Lower is better \| ### Configuration competition aime--aime_2026 predicted false sourceModelName GLM 5 sourceModelVersion glm/glm-5 ### Reported statistics Uncertainty 95% confidence interval: 93.46–99.88% confidenceInterval95 3.21 problems 30 ### Source [Original source](https://matharena.ai/competition_tables/aime--aime_2026) Source matharena Original model label GLM 5 Source version aime--aime_2026 Observed 2026-10-21T16:52:37.816Z Evidence SHA-256 `58b3ac1e0805975eba566c493b8e0165d41eb7ca517218106e112529fb6342de` Source locator `/table/rows/7` |  |  |  |  |  |  |
| [BrokenArXiv 03/2026](https://benchize.com/benchmarks/matharena%3Aarxiv_false--march.md) | 12.05% | 95% CI 6.02–18.08% | 8 / 14 | 0.11usd per problem | [matharena · arxiv_false--march](https://matharena.ai/competition_tables/arxiv_false--march) |  |
| ### Original source values \| Metric \| Value \| Unit \| Scope \| Direction \| \| --- \| --- \| --- \| --- \| --- \| \| `accuracy` \| 12.05 \| percent \| competition \| Higher is better \| \| `cost_per_problem` \| 0.11 \| usd_per_problem \| competition \| Lower is better \| ### Configuration competition arxiv_false--march predicted false sourceModelName GLM 5 sourceModelVersion glm/glm-5 ### Reported statistics Uncertainty 95% confidence interval: 6.02–18.08% confidenceInterval95 6.03 problems 56 ### Source [Original source](https://matharena.ai/competition_tables/arxiv_false--march) Source matharena Original model label GLM 5 Source version arxiv_false--march Observed 2026-10-21T16:52:24.043Z Evidence SHA-256 `b2ea8c773acad8b54736938938d6ee37fa533bfc5b985eefc303733bfcb0c6f0` Source locator `/table/rows/8` |  |  |  |  |  |  |
| [SWE-bench Multilingual](https://benchize.com/benchmarks/swe-bench%3Amultilingual.md) | 69.7% | — | 3 / 9 | — | [SWE-bench](https://www.swebench.com/) |  |
| ### Original source values \| Metric \| Value \| Unit \| Scope \| Direction \| \| --- \| --- \| --- \| --- \| --- \| \| `resolved` \| 69.7 \| percent \| multilingual \| Higher is better \| ### Configuration agent mini-SWE-agent agentOrganization SWE-agent models ["glm-5"] Reasoning effort Not reported sourceSubmission 20260213_mini-v2.0.0a0_glm-5 subset Multilingual system ["System: Attempts - 1"] ### Reported statistics instanceCalls 67.56333333333333 instanceCost 0.6424743668666667 sourceWarning Not reported ### Source [Original source](https://www.swebench.com/) Source SWE-bench Original model label GLM 5 Observed 2026-10-21T14:54:06.913Z Evidence SHA-256 `c862011a4ee1d1a7199fad6ca0905fa5c64f58216f7217ac1eddf37f28788433` Source locator `/leaderboard-data/0/results/3` |  |  |  |  |  |  |
| [ToneBench Scriptwriting](https://benchize.com/benchmarks/tonebench%3Ascripts.md) | 1,596.6 | Range 1,561.9–1,629.4 | 69 / 126 | — | [tonebench](https://benchmark.towardsai.com/data/leaderboard.json) |  |
| ### Original source values \| Metric \| Value \| Unit \| Scope \| Direction \| \| --- \| --- \| --- \| --- \| --- \| \| `elo` \| 1596.6 \| rating \| reported \| Higher is better \| \| `overall` \| 76.68 \| percent \| reported \| Higher is better \| \| `anti_slop_numbers` \| 63.86 \| source_score \| rubric \| Higher is better \| \| `continuity_emotion` \| 74.88 \| source_score \| rubric \| Higher is better \| \| `cue_quality` \| 75.71 \| source_score \| rubric \| Higher is better \| \| `hook_strength` \| 78.16 \| source_score \| rubric \| Higher is better \| \| `length_adherence` \| 88.55 \| source_score \| rubric \| Higher is better \| \| `slop` \| 79.49 \| source_score \| rubric \| Higher is better \| \| `slop_score` \| 95.12 \| source_score \| rubric \| Higher is better \| \| `substance_accuracy` \| 79.67 \| source_score \| rubric \| Higher is better \| \| `tone_voice` \| 73.26 \| source_score \| rubric \| Higher is better \| \| `writing_quality` \| 72.46 \| source_score \| rubric \| Higher is better \| \| `youtube_best_practices` \| 75.06 \| source_score \| rubric \| Higher is better \| ### Configuration provider openrouter Reasoning effort unknown scoreScope 10_task_current sourceConfiguration glm-5-or ### Reported statistics Uncertainty Reported interval: 1,561.9–1,629.4 interval [1561.9,1629.4] rank 70 runCount 5 ### Source [Original source](https://benchmark.towardsai.com/data/leaderboard.json) Source tonebench Original model label GLM-5 (thinking on) Evaluated 2026-10-08 Observed 2026-10-21T16:32:48.862Z Evidence SHA-256 `4264338337f418d3ded7ace250cad64fe12cacbd4b79a312bfaf0ae9268a01c3` Source locator `/rows/69` |  |  |  |  |  |  |
| [ArXivMath 03/2026](https://benchize.com/benchmarks/matharena%3Aarxiv--march.md) | 45.83% | 95% CI 36.91–54.75% | 12 / 14 | 0.14usd per problem | [matharena · arxiv--march](https://matharena.ai/competition_tables/arxiv--march) |  |
| ### Original source values \| Metric \| Value \| Unit \| Scope \| Direction \| \| --- \| --- \| --- \| --- \| --- \| \| `accuracy` \| 45.83 \| percent \| competition \| Higher is better \| \| `cost_per_problem` \| 0.14 \| usd_per_problem \| competition \| Lower is better \| ### Configuration competition arxiv--march predicted false sourceModelName GLM 5 sourceModelVersion glm/glm-5 ### Reported statistics Uncertainty 95% confidence interval: 36.91–54.75% confidenceInterval95 8.92 problems 30 ### Source [Original source](https://matharena.ai/competition_tables/arxiv--march) Source matharena Original model label GLM 5 Source version arxiv--march Observed 2026-10-21T16:52:28.322Z Evidence SHA-256 `9cb6d9cc26949fcb2b6affed792584b4bfe9d6168e1fc5cf2c13b38d3479715b` Source locator `/table/rows/12` |  |  |  |  |  |  |
| [Vending-Bench 2](https://benchize.com/benchmarks/vendingbench%3Av2.md) | — | — | — | $4,432.12 | [vendingbench](https://andonlabs.com/_app/immutable/chunks/Cjn0_qj9.js) |  |
| ### Original source values \| Metric \| Value \| Unit \| Scope \| Direction \| \| --- \| --- \| --- \| --- \| --- \| \| `final_balance` \| 4432.122000000001 \| USD \| one_simulated_year \| Higher is better \| \| `geometric_mean_balance` \| 4353.616706861484 \| USD \| positive_final_balances \| Higher is better \| ### Configuration Reasoning effort unknown Model configuration GLM-5 thinkingEnabled Not reported ### Reported statistics confidenceHalfWidth95 771.3583582433929 positiveBalanceRuns 5 runCount 5 standardDeviation 880.0049612473784 standardError 393.5501827772413 ### Source [Original source](https://andonlabs.com/_app/immutable/chunks/Cjn0_qj9.js) Source vendingbench Original model label GLM-5 Observed 2026-10-21T16:36:40.592Z Evidence SHA-256 `1a0b043147050298c405bcb743d73e425d532464202231cd18c27f836fa5f223` Source locator `/rows/22` |  |  |  |  |  |  |
| [NC Bench](https://benchize.com/benchmarks/nc-bench%3Aoverall.md) | 89.6% | — | 64 / 144 | — | [nc bench](https://www.nc-bench.com/models) |  |
| ### Original source values \| Metric \| Value \| Unit \| Scope \| Direction \| \| --- \| --- \| --- \| --- \| --- \| \| `score` \| 89.6 \| percent \| reported \| Higher is better \| \| `tooling` \| 98.5 \| percent \| category \| Higher is better \| \| `creative_writing` \| 83.63 \| percent \| category \| Higher is better \| \| `language` \| 92.06 \| percent \| category \| Higher is better \| \| `utility` \| 94.11 \| percent \| category \| Higher is better \| \| `reasoning` \| 84.09 \| percent \| category \| Higher is better \| \| `text_editing` \| 98.59 \| percent \| category \| Higher is better \| \| `rule_following` \| 67.78 \| percent \| category \| Higher is better \| \| `hallucination` \| 98.04 \| percent \| category \| Higher is better \| ### Configuration Reasoning effort unknown Model configuration Z.AI GLM 5 thinkingEnabled Not reported ### Source [Original source](https://www.nc-bench.com/models) Source nc bench Original model label Z.AI GLM 5 Observed 2026-10-21T16:31:48.370Z Evidence SHA-256 `ec261022458543624b08a136d01f5a2f0666cd52ffb49c64d6ca983e8543d1b5` Source locator `/rows/81` |  |  |  |  |  |  |
| [Apex Shortlist](https://benchize.com/benchmarks/matharena%3Aapex--shortlist_2025.md) | 68.62% | 95% CI 61.99–75.25% | 14 / 24 | 0.22usd per problem | [matharena · apex--shortlist_2025](https://matharena.ai/competition_tables/apex--shortlist_2025) |  |
| ### Original source values \| Metric \| Value \| Unit \| Scope \| Direction \| \| --- \| --- \| --- \| --- \| --- \| \| `accuracy` \| 68.62 \| percent \| competition \| Higher is better \| \| `cost_per_problem` \| 0.22 \| usd_per_problem \| competition \| Lower is better \| ### Configuration competition apex--shortlist_2025 predicted false sourceModelName GLM 5 sourceModelVersion glm/glm-5 ### Reported statistics Uncertainty 95% confidence interval: 61.99–75.25% confidenceInterval95 6.63 problems 47 ### Source [Original source](https://matharena.ai/competition_tables/apex--shortlist_2025) Source matharena Original model label GLM 5 Source version apex--shortlist_2025 Observed 2026-10-21T16:52:39.300Z Evidence SHA-256 `74513abecacb4d230d741f4d6df425ce878fb450220e88a1a6823524476c2989` Source locator `/table/rows/17` |  |  |  |  |  |  |
| [HMMT Nov 2025](https://benchize.com/benchmarks/matharena%3Ahmmt--hmmt_nov_2025.md) | 94.17% | 95% CI 89.98–98.36% | 3 / 12 | 0.1usd per problem | [matharena · hmmt--hmmt_nov_2025](https://matharena.ai/competition_tables/hmmt--hmmt_nov_2025) |  |
| ### Original source values \| Metric \| Value \| Unit \| Scope \| Direction \| \| --- \| --- \| --- \| --- \| --- \| \| `accuracy` \| 94.17 \| percent \| competition \| Higher is better \| \| `cost_per_problem` \| 0.10 \| usd_per_problem \| competition \| Lower is better \| ### Configuration competition hmmt--hmmt_nov_2025 predicted false sourceModelName GLM 5 sourceModelVersion glm/glm-5 ### Reported statistics Uncertainty 95% confidence interval: 89.98–98.36% confidenceInterval95 4.19 problems 30 ### Source [Original source](https://matharena.ai/competition_tables/hmmt--hmmt_nov_2025) Source matharena Original model label GLM 5 Source version hmmt--hmmt_nov_2025 Observed 2026-10-21T16:52:37.292Z Evidence SHA-256 `aa9287d733e27dde3646179a18070c07f558fb51926a8fe90cdf38985d6fce6d` Source locator `/table/rows/3` |  |  |  |  |  |  |
| [Creative Writing v3](https://benchize.com/benchmarks/eq-bench%3Acreative-writing-v3.md) | 1,600.6elo | — | 40 / 90 | — | [eq bench · creative-writing-v3](https://eqbench.com/creative_writing.js) |  |
| ### Original source values \| Metric \| Value \| Unit \| Scope \| Direction \| \| --- \| --- \| --- \| --- \| --- \| \| `elo_score` \| 1600.6 \| elo \| overall \| Higher is better \| \| `creative_writing_score` \| 16.09 \| rubric_score \| overall \| Higher is better \| ### Configuration interactiveAdjustments none protocol creative-writing-v3 sourceModelVersion zai-org/GLM-5 ### Source [Original source](https://eqbench.com/creative_writing.js) Source eq bench Original model label zai-org/GLM-5 Source version creative-writing-v3 Observed 2026-10-21T16:35:01.421Z Evidence SHA-256 `722ddb132e8d3a5e5c39b8d445f8475f3f7e24893a007260c6f3e3cce5c1ee18` Source locator `/leaderboardDataCreativeWritingV3/48` |  |  |  |  |  |  |
| [ARC-AGI-1](https://benchize.com/benchmarks/arc-prize%3Av1_Semi_Private.md) | 44.67% | — | 145 / 213 | 0.17usd per task | [arc prize · v1](https://arcprize.org/media/data/leaderboard/v1.json) |  |
| ### Original source values \| Metric \| Value \| Unit \| Scope \| Direction \| \| --- \| --- \| --- \| --- \| --- \| \| `score` \| 0.4467 \| score_fraction \| v1_Semi_Private \| Higher is better \| \| `cost_per_task` \| 0.17 \| usd_per_task \| v1_Semi_Private \| Lower is better \| ### Configuration evaluationSplit v1_Semi_Private harness Not reported modelGroup Not reported modelType CoT sourceModelVersion glm-5 ### Source [Original source](https://arcprize.org/media/data/leaderboard/v1.json) Source arc prize Original model label GLM-5 Source version v1 Observed 2026-10-21T16:34:20.378Z Evidence SHA-256 `302d8267c0522bf450ec1deeac4d060e2cf2fb444ba0a4269014d9535de0bd1f` Source locator `/evaluations/128` |  |  |  |  |  |  |

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Cite as: GLM-5 — Benchize, https://benchize.com/models/glm-5 (retrieved 2026-10-21)