[whatmodel.sh](../../README.md) / GLM-5V-Turbo # GLM-5V-Turbo Z.ai · released 2026-04 · closed weights **score 67.7** ±1.6 · #107 of 732 · from 4 benchmark results across 1 source Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at _{● marks GLM-5V-Turbo among every model on that task or test, worst to best.}

task                vs the others         rank  counts  tests
coding              ▁▁▁▃▅▇█▇●▄▃▁   #93 of 469*     46%      1
chat & writing      ▁▁▁▃▆▄▆█▆▇●▃   #79 of 402*     26%      1
vision & documents  ▁▁▂▂▁▃▄▂▆●█▄   #69 of 153*     26%      2

coding · #93 of 469 · 46% of its score

Beats [Grok 4.20 (0309 reasoning)](grok-4-20.md), [GPT-5.4 mini](gpt-5-4-mini.md), [GPT-5.1](gpt-5-1.md) here, though they rank higher overall. Trails [Llama 3.1 Nemotron Ultra 253B v1](llama-3-1-nemotron-ultra-253b.md), [Qwen3 235B-A22B Thinking 2507](qwen3-235b-a22b-thinking-2507.md), [o4-mini](o4-mini.md) here, though they rank lower overall.

test                       result  vs the others in its run   difficulty  counts  why
Arena WebDev (Code Arena)  1401.0  ▁▁▃▂▅●▃▆▅▅▁▂ beats 47/129  avg 981        46%  sharp

chat & writing · #79 of 402 · 26% of its score

Beats [Claude Haiku 5.5](claude-haiku-5-5.md), [LongCat 2.0 (1.6T-A48B)](longcat-2-0.md), [Muse Glimmer 30B](muse-glimmer-30b.md) here, though they rank higher overall. Trails [Gemma 4 12B](gemma-4-12b.md), [Gemma 4 26B-A4B](gemma-4-26b-a4b.md), [Nemotron Cascade 2 30B-A3B](nemotron-cascade-2-30b-a3b.md) here, though they rank lower overall.

test            result  vs the others in its run    difficulty  counts  why
LMArena (text)  1434.0  ▁▁▁▂▃▃▃▆▆▇●▃ beats 292/385  avg 1260       26%  blunt

vision & documents · #69 of 153 · 26% of its score

Beats [DeepSeek V4 Flash Vision (Exp)](deepseek-v4-flash-vision-exp.md), [GPT-5](gpt-5.md), [o3](o3.md) here, though they rank higher overall. Trails [Gemma 4 26B-A4B](gemma-4-26b-a4b.md), [Claude Haiku 4.5](claude-haiku-4-5.md), [Qwen3.6 Flash](qwen3-6-flash.md) here, though they rank lower overall.

test                               result  vs the others in its run   difficulty  counts  why
Arena vision (style controlled)    1230.0  ▁▁▂▂▂▂▃▄▅●█▇ beats 90/152  avg 1061       17%  blunt
Arena document (style controlled)  1400.1  ●▁▃▃▄▃▇█▅▇▄▅ beats 0/37    avg 1331      9.0%  blunt

## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its *counts* column above; here it is added up by who ran the tests. [Method](../method.md)

source                         results  share of the score
LMArena leaderboard dataset          4   99% ▇▇▇▇▇▇▇▇▇▇▇▇
prior: pull toward the middle             1% ▇
weighted average                        = 67.7

_{Licences: LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/))} ## How to run it ### Through an API api (SiliconFlow) · $1.90/M blended · $1.2 in / $4 out per 1M tokens · [siliconflow.com](https://www.siliconflow.com/models/glm-5v-turbo) _{[price source](https://www.siliconflow.com/models/glm-5v-turbo): USD per 1M tokens, SiliconFlow serverless pay-as-you-go, model zai-org/GLM-5V-Turbo (page state 'Available'; SiliconFlow names Z.ai as the model provider; served in FP8): input $1.20, output $4.00; cache-read input $0.24 excluded. Z.ai's own pricing page no longer lists GLM-5V-Turbo (its Vision Models table shows GLM-4.6V, GLM-OCR, GLM-4.6V-FlashX, GLM-4.5V and GLM-4.6V-Flash only); the same $1.20/$4.00 appears for Z.ai in models.dev @a668908. Not a Z.ai direct tariff. Currency: USD. SiliconFlow official model page, observed 2026-09-26. · [context source](https://docs.z.ai/guides/vlm/glm-5v-turbo.md): Z.ai GLM-5V-Turbo model guide: Context Length 200K (stated rounded), Maximum Output Tokens 128K. Observed 2026-09-25.} [interactive version](https://whatmodel.sh/model/glm-5v-turbo) · [JSON](https://whatmodel.sh/api?format=json&all=1)