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Models › GLM-4.7 family

GLM-4.7-Flash

Hugging Face ↗ Follow changes

zai-org MIT out 9 months ago

31.2B parameters (4 of 64 experts per token), text in, text out, 198K tokens of context. 5 providers serve it from $0.060 per million input tokens.

Verified 2026-10-26 07:27 UTC Rank #19 in LLMs · score 119.5 method v1 Sources 4 Downloads 30d 953.7k · #116 of 619 tracked

Changed this week: 1 new quantized build on the Hub (107 in all).2026-10-24

Can I run it?19GB at INT4Estimatefits a 24 GB cardmethodCommunity: GLM 4.7 Flash — Parameters, Context, Benchmarks & Pricing | AI Atlas →
What does it cost?$0.060/1M incheapest of 2DeepInfra2026-10-26
Can I use it?MITPermissivelicence file2026-10-26
Do people use it?953.7kdownloads, 30 days#116 of 619 we track · top 19%+8 likes, downloads −19% this week1,888 likesHugging Face2026-10-26

Worth reading

Picked by rule from 20 tracked pieces: the lab's own post, the most shared, a field report, the latest.

Most discussed · 16 HN pts

Open-Weight Models Are Getting Serious: GLM 4.7 vs. MiniMax M2.1

GLM 4.7 and MiniMax M2.1 successfully built a CLI task runner with 20 features in a multi-phase test.

  • MiniMax M2.1 used batch-based execution where tasks are grouped by dependency level.
  • GLM 4.7 implemented argument parsing manually to avoid external dependencies.
blog.kilo.ai 2026-01-09 16 HN pts 1.5k words about the GLM 4.7 family family
Latest · 6 months ago

Claude Code Uses GLM 4.7

old.reddit.com 2026-04-27 2 HN pts · 4 comments about the GLM 4.7 family family

20 best of 20 tracked

The 4 picks above, plus these 16. Top is the radar's score: discussion, source authority and the lab's own posts, fading with age.

  1. ArticleGLM-4.7: Advancing the Coding Capabilityz.ai · 2025-12-22 · 437 HN pts · official
  2. ArticleGLM-4.7docs.z.ai · 2025-12-22 · 25 HN pts · official
  3. ArticleUnsloth: GLM-4.7-Flashunsloth.ai · 2026-01-20 · 4 HN pts
  4. ArticleGLM-4.7: Frontier intelligence at record speed – now available on Cerebrascerebras.ai · 2026-01-08 · 9 HN pts
  5. CodeGLM Proxy: transforms Anthropic Messages API requests to Z.ai GLM-4.7 API formatgithub.com · 2025-12-27 · 2 HN pts
  6. ArticleGLM 4.7 Flash — Parameters, Context, Benchmarks & Pricing | AI Atlasai-atlas.co · undated
  7. VideoGLM4.7-Flash vs. other Local LLM. Tested also with OpenCode [video]youtube.com · 2026-01-25
  8. ArticleGLM-4.7-Flash 30B-A3B MoExcancel.com · 2026-01-19
  9. ArticleGLM-4.7-Flash | Awesome Agentsawesomeagents.ai · 2026-02-24
  10. ArticleGLM4.7-Flash the new Local LLM king at 30B A3B and OpenCode?grigio.org · 2026-01-25
  11. ArticleGLM 4.7 Flash LLM Review [2026] - Dot Net Perls Blogdotnetperls.com · 2026-01-24
  12. ArticleWe Tested GLM-4.7 Flash 30B MoE — Here’s the GPU You Actually Need | Hardware Cornerhardware-corner.net · 2026-01-22
  13. ArticleGLM-4.7-Flash Review: The Ultra-Fast, Ultra-Cheap, High-Performance Price-Disruption Model Finally Arrives | HonoGear｜ガジェット研究所honogear.com · 2026-01-22
  14. ArticleGLM-4.7 Flash Review: High-Performance Coding on a Budgetllm-stats.com · 2026-01-19
  15. ArticleGLM-4.7-Flash: Proven 30B-A3B, Benchmarks, Local Setup, Costbinaryverseai.com · 2026-01-19
  16. ArticleGLM-4.7-Flash Review: The New Standard for Local 30B Models | SynapseWiresynapsewire.com · 2025-12-26

Where to run it

Live hosted endpoints, cheapest first, USD per million tokens. Ties break on output price, then provider name.

2 providers from $0.060

2 providers with a live price, from $0.060 to $0.060 per million input tokens; the median is $0.060. One dot per provider, cheapest in teal.

$0.010$0.10$1.00$10.00DeepInfra: $0.060 in / $0.40 out per 1M · observed 2026-10-26 · https://huggingface.co/api/models/zai-org/GLM-4.7-Flash?expand[]=inferenceProviderMappingCloudflare: $0.060 in / $0.40 out per 1M · observed 2026-10-26 · https://openrouter.ai/z-ai/glm-4.7-flashcheapest $0.060median $0.060
$0.010$0.10$1.00$10.00DeepInfra: $0.060 in / $0.40 out per 1M · observed 2026-10-26 · https://huggingface.co/api/models/zai-org/GLM-4.7-Flash?expand[]=inferenceProviderMappingCloudflare: $0.060 in / $0.40 out per 1M · observed 2026-10-26 · https://openrouter.ai/z-ai/glm-4.7-flashcheapest $0.060 · median $0.060 (white tick)
#Cheapest 4In /1MOut /1MContextQuantSeen
1 DeepInfra $0.060 $0.40 198K — Oct 26
2 Cloudflare $0.060 $0.40 128K unknown Oct 26
3 Featherless — — — — Oct 26
4 Z.ai — — — — Oct 26

All 5 providers →

Where it's used

Hugging Face does not say who downloads a model or with what. These are the proxies: API providers listing it, quantized builds, fine-tunes/adapters/merges, and Spaces that load it.

Widely served (5 API providers); heavily run locally (107 quantized builds); a big base for builders (101 fine-tunes, adapters and merges).

  • As a service5Cloudflare, DeepInfra, Featherless, Novita +1
  • On own hardware107quantized builds
  • Built on10181 fine-tunes · 20 adapters · 0 merges
  • In apps100+Hugging Face Spaces

Top Spaces running it: akhaliq/anycoder ♥ 3,321 · huggingface-projects/ai-video-composer ♥ 664 · pliny-the-prompter/obliteratus ♥ 621 · embedl/hfviewer ♥ 181

Will it fit?

Weights only, an estimate: leave room for the KV cache. Method

Fits a 64 GB Mac (unified memory) at INT4

About 19 GB at INT4, 38 GB at FP8, 75 GB at BF16: weights only, so leave room for the KV cache. Every expert must be resident, so total parameters count.

16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 19 GB of weights (estimate; KV cache and activations extra)INT419 GBFP8: about 38 GB of weights (estimate; KV cache and activations extra)FP838 GBBF16: about 75 GB of weights (estimate; KV cache and activations extra)BF1675 GB
16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 19 GB of weights (estimate; KV cache and activations extra)INT419 GBFP8: about 38 GB of weights (estimate; KV cache and activations extra)FP838 GBBF16: about 75 GB of weights (estimate; KV cache and activations extra)BF1675 GB

Estimate params × bytes per param × 1.2 · how we estimate

The story so far

Weights, every dated article, and repo commits on one rail.

Most coverage came in the first two weeks

Teal is the weights (2026-01-19), amber triangles are 9 dated articles (each links to it), grey ticks are repo commits.

JanFebMarAprMayJunJulAugSepOct2026-01-29: 2 repo commits2026-01-20: 3 repo commits2026-01-19: 7 repo commitsGLM-4.7: Advancing the Coding Capability · 2025-12-22GLM-4.7 · 2025-12-22Claude Code Uses GLM 4.7 · 2026-04-27Unsloth: GLM-4.7-Flash · 2026-01-20Open-Weight Models Are Getting Serious: GLM 4.7 vs. MiniMax M2.1 · 2026-01-09GLM-4.7: Frontier intelligence at record speed – now available on Cerebras · 2026-01-08GLM Proxy: transforms Anthropic Messages API requests to Z.ai GLM-4.7 API format · 2025-12-27GLM-4.7-Flash Review 2026: Free 30B Coding Model, Benchmarks - TokenMix Blog · 2026-06-22GLM-4.7-Flash: 30B MoE model achieves 59.2% on SWE-bench, runs on 24GB GPUs · 2026-01-20Weights 2026-01-19 · 2026-01-19Weights 2026-01-19now · 5 providers
JanFebMarAprMayJunJulAugSepOct2026-01-29: 2 repo commits2026-01-20: 3 repo commits2026-01-19: 7 repo commitsGLM-4.7: Advancing the Coding Capability · 2025-12-22GLM-4.7 · 2025-12-22Claude Code Uses GLM 4.7 · 2026-04-27Unsloth: GLM-4.7-Flash · 2026-01-20Open-Weight Models Are Getting Serious: GLM 4.7 vs. MiniMax M2.1 · 2026-01-09GLM-4.7: Frontier intelligence at record speed – now available on Cerebras · 2026-01-08GLM Proxy: transforms Anthropic Messages API requests to Z.ai GLM-4.7 API format · 2025-12-27GLM-4.7-Flash Review 2026: Free 30B Coding Model, Benchmarks - TokenMix Blog · 2026-06-22GLM-4.7-Flash: 30B MoE model achieves 59.2% on SWE-bench, runs on 24GB GPUs · 2026-01-20Weights 2026-01-19 · 2026-01-19Weights 2026-01-19now · 5 providers

What people built on it

208 derivatives on the Hub. Which build to run, by runtime: the most downloaded in each class.

208 derivatives on the Hub, most of them quantized

From the Hugging Face model tree; quantized builds in teal, fine-tunes, merges and adapters in greys.

107 quantized (Hugging Face model tree)81 fine-tunes (Hugging Face model tree)20 adapters (Hugging Face model tree)107 quantized81 fine-tunes20 adapters
107 quantized (Hugging Face model tree)81 fine-tunes (Hugging Face model tree)20 adapters (Hugging Face model tree)107 quantized81 fine-tunes20 adapters
GGUF unsloth/GLM-4.7-Flash-GGUF for llama.cpp, Ollama, LM Studio 2M downloads MLX lmstudio-community/GLM-4.7-Flash-MLX-8bit for Apple silicon 3.6M downloads AWQ cyankiwi/GLM-4.7-Flash-AWQ-4bit for vLLM, SGLang 2.6M downloads

All variants →

Reference

Everything else, folded: the full tables, every source.

Benchmarks

9 rows

Benchmarks 9 rows

measured third-party leaderboard rowauthor-reported number from the model card or eval files (9)No confidence intervals unless the source gave them; settings differ between rows, so compare within a benchmark, not across. Date = the benchmark's own date when it gives one, else when we read it.
BenchmarkValueSetting / notesReported byDate
author-reported AIME 2025 · Reason through hard problems91.6—model author2026-10-07
author-reported BrowseComp42.8—model author2026-10-07
author-reported GPQA Diamond · Reason through hard problems75.2—model author2026-10-07
author-reported Humanity's Last Exam · Reason through hard problems14.4—model author2026-10-07
author-reported LCB v664—model author2026-10-07
author-reported SWE-bench Verified · Write code with agents59.2—model author2026-10-07
author-reported τ²-Bench79.5—model author2026-10-07
author-reported diamond (Idavidrein/gpqa)75.2—Model Card2026-01-27
author-reported hle (cais/hle)14.4—Model Card2026-01-28

Use-case evidence

5 use cases

What it is good at 5 use cases

Labels are evidence, not verdicts: measured a third-party leaderboard row for this model; author-reported a number the model author published in the card or eval files; structural a deterministic signal from the name, config or chat template, or a decision-model estimate. Each chip links to its source. All use cases.

Facts

24 fields

Listed in LLMs · MoE

Facts

Pipeline tagtext-generation
Librarytransformers
ArchitecturesGlm4MoeLiteForCausalLM
Model typeglm4_moe_lite
Dtypebfloat16
Params by dtypeBF16: 31,221,485,568 · F32: 3,008
Size bandL
MoEyes
Experts64
Experts per token4
Context length202,752 (config.json max_position_embeddings)
Languagesen, zh
PapersarXiv:2508.06471
LicenseMIT (mit)
Hub snapshot2026-10-26

Servability

vLLM · SGLang

Servability

vLLM: supported · Glm4MoeLiteForCausalLM · 4b1c04e76fe9ac3ca8b7e541dd423e7d5f38dc60SGLang: supported · Glm4MoeLiteForCausalLM · 60d941293e3af27a12c6eb64e2c3e2e1a17f100f

vLLM registry (4b1c04e76fe9ac3ca8b7e541dd423e7d5f38dc60) · SGLang registry (60d941293e3af27a12c6eb64e2c3e2e1a17f100f) · Matched by architecture class name against each project's registry on main; 'supported' means the architecture is registered, not that this checkpoint was tested.

Hardware fit

3 estimates

Hardware fit ESTIMATE

Weights-only memory for GLM-4.7-Flash: params × bytes/param × 1.2 overhead. KV cache and activations are extra. How this is computed.

BF16 (2 bytes/param)75 GB · Single high-end GPU (<80 GB)
FP8 (1 byte/param)38 GB · Single high-end GPU (<80 GB)
INT4 (0.5 bytes/param)19 GB · Single GPU (<24 GB)

Weights only (params × bytes/param × 1.2 overhead); KV cache and activations extra. MoE: all experts must be resident, so total parameters count.

All variants

40 variants

Lineage

Original model Family: zai-org/GLM-4.7 208 derivatives on the Hub

quantized: 107finetune: 81adapter: 20

Top variants (40 shown, ranked by downloads)

Relation
Quant
Size
40 rows
Variant Relation Quant Params Band License Author Likes Downloads
lmstudio-community/GLM-4.7-Flash-MLX-8bit quantized MLX 29.9B L mit lmstudio-community 13 3.6M
lmstudio-community/GLM-4.7-Flash-MLX-6bit quantized MLX 29.9B L mit lmstudio-community 10 3.5M
cyankiwi/GLM-4.7-Flash-AWQ-4bit quantized AWQ 31.2B L mit cyankiwi 56 2.6M
unsloth/GLM-4.7-Flash finetune — 31.2B L mit unsloth 18 2.3M
unsloth/GLM-4.7-Flash-GGUF quantized GGUF — — mit unsloth 730 2M
QuantTrio/GLM-4.7-Flash-AWQ quantized AWQ 31.2B L mit QuantTrio 12 1.3M
GadflyII/GLM-4.7-Flash-NVFP4 quantized NVFP4 31.2B L apache-2.0 GadflyII 71 1.2M
unsloth/GLM-4.7-Flash-FP8-Dynamic quantized -FP8 30.0B L mit unsloth 28 755.5k
lmstudio-community/GLM-4.7-Flash-GGUF quantized GGUF — — mit lmstudio-community 6 578.1k
ggml-org/GLM-4.7-Flash-GGUF quantized GGUF — — — ggml-org 16 380.7k
bartowski/zai-org_GLM-4.7-Flash-GGUF quantized GGUF — — mit bartowski 57 168.3k
cerebras/GLM-4.7-Flash-REAP-23B-A3B finetune — 23.0B L mit cerebras 82 156.9k
marksverdhei/GLM-4.7-Flash-FP8 quantized -FP8 31.2B L mit marksverdhei 19 157.1k
mlx-community/GLM-4.7-Flash-4bit quantized -4bit 29.9B L mit mlx-community 72 128.8k
mlx-community/GLM-4.7-Flash-8bit quantized -8bit 29.9B L mit mlx-community 25 100.8k
TheHouseOfTheDude/GLM-4.7-Flash_AWQ quantized AWQ — — other TheHouseOfTheDude 3 91.6k
mlx-community/GLM-4.7-Flash-6bit quantized — 29.9B L mit mlx-community 7 89k
mlx-community/GLM-4.7-Flash-5bit quantized — 29.9B L mit mlx-community 0 86.9k
whitecircle/GLM-4.7-Flash-Coder finetune — 29.9B L mit whitecircle 17 85.7k
lmstudio-community/GLM-4.7-Flash-MLX-4bit quantized MLX 29.9B L mit lmstudio-community 14 52.7k
huihui-ai/Huihui-GLM-4.7-Flash-abliterated finetune — 31.2B L mit huihui-ai 96 25.2k
mudler/GLM-4.7-Flash-APEX-GGUF quantized GGUF — — apache-2.0 mudler 17 29k
mradermacher/GLM-4.7-Flash-i1-GGUF quantized GGUF — — mit mradermacher 5 28.7k
noctrex/GLM-4.7-Flash-MXFP4_MOE-GGUF quantized GGUF — — — noctrex 37 23.3k
GadflyII/GLM-4.7-Flash-MTP-NVFP4 quantized NVFP4 18.6B L apache-2.0 GadflyII 5 23k
cyankiwi/GLM-4.7-Flash-AWQ-8bit quantized AWQ 31.2B L mit cyankiwi 19 20.1k
FayeQuant/GLM-4.7-Flash-GPTQ-4bit quantized GPTQ 29.9B L mit FayeQuant 4 19.3k
ngxson/GLM-4.7-Flash-GGUF quantized GGUF — — — ngxson 20 18.2k
DavidAU/GLM-4.7-Flash-NEO-CODE-Imatrix-MAX-GGUF quantized Imatrix — — mit DavidAU 9 18k
Intel/GLM-4.7-Flash-int4-AutoRound quantized int4 1.14B S — Intel 10 15.7k
unsloth/GLM-4.7-Flash-NVFP4 quantized NVFP4 16.7B L mit unsloth 14 13.7k
GadflyII/GLM-4.7-Flash-MXFP4 quantized MXFP4 17.9B L apache-2.0 GadflyII 8 13.4k
huihui-ai/Huihui-GLM-4.7-Flash-abliterated-mlx-4bit quantized mlx 29.9B L mit huihui-ai 13 12.6k
MuXodious/GLM-4.7-Flash-impotent-heresy finetune — 29.9B L mit MuXodious 14 11.4k
ubergarm/GLM-4.7-Flash-GGUF quantized GGUF — — mit ubergarm 24 10.2k
evilfreelancer/GLM-4.7-Flash-GGUF quantized GGUF — — mit evilfreelancer 4 10.2k
wangzhang/GLM-4.7-Flash-abliteratex quantized GGUF 29.9B L mit wangzhang 3 8.8k
meshllm/GLM-4.7-Flash-MTP-GGUF quantized GGUF — — mit meshllm 0 8.7k
tiny-random/glm-4.7-flash finetune — 5.77M S — tiny-random 0 8.1k
ThalisAI/GLM-4.7-Flash-heretic quantized GGUF — — other ThalisAI 0 7.9k

Repository changelog

12 commits
changed in the last 7 daysRank band moves: 2026-10-23Other: 2026-10-24all changes

Repository changelog 12 commits

DateCommitSHA
2026-01-29 Add HLE evaluation result (#55) 7dd2089
2026-01-29 Add GPQA evaluation result (#54) 2b2bd73
2026-01-20 Merge branch 'main' of hf.co:zai-org/GLM-4.7-Flash a930807
2026-01-20 update bench 9980c03
2026-01-20 Update SGLang guide (#14) 279ecdf
2026-01-19 update 93e5632
2026-01-19 bench update 680de61
2026-01-19 Upload README.md with huggingface_hub df1e3df
2026-01-19 Upload README.md with huggingface_hub e743aac
2026-01-19 Add files using upload-large-folder tool 33faccf
2026-01-19 Add files using upload-large-folder tool 75fce70
2026-01-19 initial commit 19b973b

All providers

5 providers · 5 endpoints

Where to run 5 providers · 5 endpoints

Hosted endpoints observed for GLM-4.7-Flash, sorted by input price (USD per million tokens). Each row names its observer and date; prices change daily, so check the source before quoting. Cheapest observed input price: $0.06/M.

ProviderModel refContextQuant$/M in$/M outtok/sTTFTToolsStatusObserved
DeepInfra
hf
 zai-org/GLM-4.7-Flash 198K — $0.06 $0.40 6.6 997 ms yes live 2026-10-26
Cloudflare
openrouter
 z-ai/glm-4.7-flash 128K unknown $0.06 $0.40 — — yes live 2026-10-26
Novita
hf, openrouter
 zai-org/glm-4.7-flash 195K bf16 $0.07 $0.40 87 1663 ms yes status -5 2026-10-26
Featherless
hf
 zai-org/GLM-4.7-Flash — — — — — — — live 2026-10-26
Z.ai
hf
 glm-4.7-flashx — — — — 60.6 1873 ms yes live 2026-10-26

Articles and coverage

20 pieces

Ranked by source signals (HN, Lobsters, authority domains, official posts) and recency. Summaries are generated from a stored copy of the article text; every sentence cites a span of that text, and nothing is shown that failed the citation check.

Official4

GLM-4.7: Advancing the Coding Capability
z.ai2025-12-22officialauthorityHN front pageabout the GLM 4.7 familyHN 437 pts · 235 comments2,892 words
Z.ai released GLM-4.7, which supports thinking before acting to improve performance in complex tasks within mainstream agent frameworks. The model achieves 73.8% on SWE-bench, 66.7% on SWE-bench Multilingual, and 41% on Terminal Bench 2.0. It delivers a 42.8% score on the HLE benchmark and shows gains across 17 benchmarks compared to GLM-4.6.written, reviewed, cited
  • GLM-4.7 achieves 42.8% on the HLE benchmark with tools, a specific improvement over its 30.4% score without them.
  • The model produces cleaner webpages and generates slides with more accurate layout and sizing for UI tasks.
  • GLM-4.7 achieves significantly better performance on web browsing via BrowseComp compared to other models like Gemini 3.0 Pro.
GLM-4.7
docs.z.ai2025-12-22officialauthorityabout the GLM 4.7 familyHN 25 pts · 6 comments949 words
Z.AI released GLM-4.7, a model series featuring enhanced programming capabilities and stable multi-step reasoning. It supports 200K context length and 128K maximum output tokens, offering multiple thinking modes and real-time streaming responses for complex agent tasks. The model scores 67 points on the BrowseComp web task evaluation and achieves an open-source SOTA of 84.7 points on the τ²-Bench interactive tool invocation benchmark. Additionally, it reaches 42.8% on the HLE benchmark, representing a 41% increase over GLM-4.6.written, reviewed, cited
  • GLM-4.7 implements a 'think before acting' mechanism within programming frameworks like Claude Code, Kilo Code, TRAE, Cline, and Roo Code.
  • The model achieves 73.8% on SWE-bench Verified, marking a 5.8% improvement over GLM-4.6.
Unsloth: GLM-4.7-Flash
unsloth.ai2026-01-20authorityHN 4 pts · 0 comments1,214 words
GLM-4.7-Flash is a 30B MoE reasoning model from Z.ai that uses ~3.6B parameters and supports a 202,752 token context window. Running the 4-bit quantized version requires around 18GB RAM, while Unsloth's FP8 Dynamic quant reduces KV cache memory usage by 50% on 4 GPUs. However, using the MTP module for speculative decoding drops generation throughput from 13,000 tokens/s to 1,300 tokens/s on a single B200 GPU.written, reviewed, cited
  • The 30B model does not fit on a free Colab GPU for fine-tuning.
  • Fine-tuning the router layer is disabled by default to preserve reasoning capabilities.
  • Using llama.cpp requires setting --min-p 0.01 because the default is 0.05.
GLM-4.7: Frontier intelligence at record speed – now available on Cerebras
cerebras.ai2026-01-08about the GLM 4.7 familyHN 9 pts · 1 comments748 words
Z.ai released GLM-4.7, an open-weight model available on Cerebras Inference Cloud that achieves approximately 1,000 tokens per second and up to 1,700 TPS for specific use cases. The model delivers roughly 10x higher price-performance than Claude Sonnet 4.5 while maintaining comparable intelligence to DeepSeek-V3.2 on developer benchmarks. Teams can access the model via Cerebras Cloud starting at a $10 pay-as-you-go developer tier.written, reviewed, cited
  • GLM-4.7 introduces preserved thinking, allowing reasoning context to persist across turns.
  • The model interleaves thinking before each action, tool call, or response rather than treating reasoning as a single upfront step.
  • Migration requires updating the model name while keeping the same API surface as GLM-4.6.

Analysis5

Open-Weight Models Are Getting Serious: GLM 4.7 vs. MiniMax M2.1
blog.kilo.ai2026-01-09about the GLM 4.7 familyHN 16 pts · 0 comments1,537 words
GLM 4.7 and MiniMax M2.1 successfully built a CLI task runner with 20 features in a multi-phase test. GLM 4.7 completed the task in 10 minutes with a 741-line plan and full README, whereas MiniMax M2.1 finished in 14 minutes with a 284-line plan and no documentation. MiniMax M2.1 cost $0.15 for the task and is currently free to use in Kilo Code for a limited time.written, reviewed, cited
  • MiniMax M2.1 used batch-based execution where tasks are grouped by dependency level.
  • GLM 4.7 implemented argument parsing manually to avoid external dependencies.
  • MiniMax M2.1 uses SHA256 for hashing, which is technically more collision-resistant than GLM 4.7's MD5.
GLM Proxy: transforms Anthropic Messages API requests to Z.ai GLM-4.7 API format
github.com2025-12-27authorityabout the GLM 4.7 familyHN 2 pts · 1 comments2,986 words
GLM-4.7-Flash Review 2026: Free 30B Coding Model, Benchmarks - TokenMix Blog
tokenmix.ai2026-06-22
GLM-4.7-Flash: 30B MoE model achieves 59.2% on SWE-bench, runs on 24GB GPUs
curateclick.com2026-01-20HN 1 pts · 1 comments3,017 words
GLM 4.7 Flash — Parameters, Context, Benchmarks & Pricing | AI Atlas
ai-atlas.co

Community1

Claude Code Uses GLM 4.7
old.reddit.com2026-04-27about the GLM 4.7 familyHN 2 pts · 4 comments
All coverage tracked for this model (20) — append-only, nothing is ever removed
TitleSourcePublishedFirst seenSignals
GLM 4.7 Flash — Parameters, Context, Benchmarks & Pricing | AI Atlasai-atlas.co—2026-10-07exa
GLM-4.7-Flash Review 2026: Free 30B Coding Model, Benchmarks - TokenMix Blogtokenmix.ai2026-06-222026-10-07exa
Claude Code Uses GLM 4.7old.reddit.com2026-04-272026-10-07hn
GLM-4.7-Flash | Awesome Agentsawesomeagents.ai2026-02-242026-10-07exa
GLM4.7-Flash vs. other Local LLM. Tested also with OpenCode [video]youtube.com2026-01-252026-10-07hn
GLM4.7-Flash the new Local LLM king at 30B A3B and OpenCode?grigio.org2026-01-252026-10-07hn
GLM 4.7 Flash LLM Review [2026] - Dot Net Perls Blogdotnetperls.com2026-01-242026-10-07exa
We Tested GLM-4.7 Flash 30B MoE — Here’s the GPU You Actually Need | Hardware Cornerhardware-corner.net2026-01-222026-10-07exa
GLM-4.7-Flash Review: The Ultra-Fast, Ultra-Cheap, High-Performance Price-Disruption Model Finally Arrives | HonoGear｜ガジェット研究所honogear.com2026-01-222026-10-07exa
Unsloth: GLM-4.7-Flashunsloth.ai2026-01-202026-10-07hn
GLM-4.7-Flash: 30B MoE model achieves 59.2% on SWE-bench, runs on 24GB GPUscurateclick.com2026-01-202026-10-07hn, exa
GLM-4.7-Flash 30B-A3B MoExcancel.com2026-01-192026-10-07hn
GLM-4.7 Flash Review: High-Performance Coding on a Budgetllm-stats.com2026-01-192026-10-07exa
GLM-4.7-Flash: Proven 30B-A3B, Benchmarks, Local Setup, Costbinaryverseai.com2026-01-192026-10-07exa
Open-Weight Models Are Getting Serious: GLM 4.7 vs. MiniMax M2.1blog.kilo.ai2026-01-092026-10-07hn
GLM-4.7: Frontier intelligence at record speed – now available on Cerebrascerebras.ai2026-01-082026-10-07hn
GLM Proxy: transforms Anthropic Messages API requests to Z.ai GLM-4.7 API formatgithub.com2025-12-272026-10-07hn
GLM-4.7-Flash Review: The New Standard for Local 30B Models | SynapseWiresynapsewire.com2025-12-262026-10-07exa
GLM-4.7: Advancing the Coding Capabilityz.ai official2025-12-222026-10-07hn
GLM-4.7docs.z.ai official2025-12-222026-10-07hn

Papers

1 paper

Papers 1

arXiv links found in the model card and its tags. "Peer-reviewed" is shown only with evidence: a published venue in OpenAlex (measured) or the authors' own journal reference on arXiv (reported).

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Sources

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Sources

Hub snapshot: 2026-10-26 · HF API: 2026-10-26 07:13 UTC · Providers: 2026-10-26 07:26 UTC · Articles: 2026-10-26 07:27 UTC · Summary: 2026-10-07 14:59 UTC · Last verified 2026-10-26 07:27 UTC

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