Zhipu AI · mit

GLM-4.7-Flash

GLM-4.7-Flash by Zhipu AI appears in 1 source with Reasoning at 31.38. Best read for Code quality, Math, Open weights.

CreatorZhipu AI

Release date2026-01-19

Knowledge cutoffNot published

ContextUnknown

Input priceUnknown

Output priceUnknown

Modalitytext

CountryCN

Metrics

All source-backed metrics

Open in leaderboard

LLM Stats Rank70ranking · source_rank

Reasoning31.38reasoning · index_reasoning

Math29.11math · index_math

Coding8.41coding · index_code

Research3.44research · index_search

Vision7.41multimodal · index_vision

Tool calling9.64tool_calling · index_tool_calling

GPQA75.2 %reasoning · gpqa_score

AIME 202591.6 %math · aime_2025_score

SWE-bench Verified59.2 %coding · swe_bench_verified_score

Code Arena758.78 %coding · coding_arena_score

Humanity Last Exam14.4 %reasoning · hle_score

BrowseComp42.8 %research · browsecomp_score

Parameters30,000,000,000 paramsmodel · params

Evidence

Citations and source overlap

LLM StatsPublic full LLM leaderboard advanced explorer snapshot, including ranking, benchmark indexes, pricing, speed, context, license, and model metadata.Retrieved 2026-06-01

FAQ

How should I read this profile?

Treat this as a source-backed model dossier, not an EvalKit-run verification. The public values are replicated from linked sources and kept source-scoped.

Is GLM-4.7-Flash verified by EvalKit?

No. EvalKit currently shows 0 verified rows until real run evidence exists.

Why can metrics disagree?

Different sources test different tasks, dates, prompts, and aggregation methods. EvalKit keeps those differences visible instead of merging them into a fake universal score.