Head to head · Open-weight models you run yourself · October 2026 research run
GLM-4.7-Flash vs Kimi K3.1 Pro
GLM-4.7-Flash scores 63 (B) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in 3 of 4 scored categories. Kimi K3.1 Pro leads on documentation and upkeep. Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 4969 open weights comparisons
Which one, for what
Best for Agents and developers who want a small, permissively licensed tool-calling model on one 24 to 32 GB GPU or Mac, in almost any runtime
Ahead on
- Reach, 80 against 40
- Risk, 55 against 50
- Agent interface, 85 against 65
Watch for
No change to the files since 29 January 2026 and no zai-org reply on discussions since 31 January
Best for Operators with a multi-accelerator node who want Moonshot's newest open-weight model for coding and long agent sessions, and everyone else through an API
Ahead on
- Documentation and upkeep, 51 against 28
Watch for
1,561 GB of files, so it needs a node of eight large accelerators at least
Why the edge
Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.
Reach GLM-4.7-Flash +40
GLM-4.7-Flash 80
Memory earns 25, since Ollama's smallest build is the 19 GB q4_K_M one, about 22.8 GB with the fifth added, which fits 24 GB. Runtimes reach the cap of 25, with model code in released vLLM, SGLang and Transformers, a converter in a released llama.cpp build and a local entry in Ollama's library. Builds earn nothing, because Z.ai publishes only BF16 and every quantised build is someone else's. The MIT licence with nothing added gives the full 15 for terms, the files download with no account or gate for 10, and Z.ai plus three other providers serve it for 5.
Kimi K3.1 Pro 40
Memory 0, the only build is Moonshot's 1,561 GB MXFP4 release, far above 256 GB. Runtimes 5, llama.cpp b12242 alone has the model code in a release, since vLLM's recipe needs 0.34.0 and SGLang's cookbook a branch. Builds 10, one precision. Terms 10, free commercial use below a model-as-a-service revenue threshold. Download 10, not gated. Hosted 5, Moonshot's API and Together serve it. 0 + 5 + 10 + 10 + 10 + 5 = 40.
Documentation and upkeep Kimi K3.1 Pro +23
GLM-4.7-Flash 28
The card covers architecture, parameters and languages and misses four of the seven items, for 8. Training data is 0, as the card describes none. The last change was on 1 February 2026 at the latest, within a year but past 180 days, for 5. Responsiveness is 5, since zai-org answered 6 of the 40 most recent discussions, all by 31 January, which is a few. Version policy is 5 for a first release with no files replaced, and file health is 5, as config.json and tokenizer_config.json disagree on context and a config report waits without a reply.
Kimi K3.1 Pro 51
Model card 11, no limitations, languages or cut-off. Training data 5, one general sentence. Last change 20, a commit on 5 November 2026. Responsiveness 0, none of the 8 newest discussions has a reply from Moonshot. Version policy 5, Kimi K3 keeps its repository and no dated note was found. File health 10, the files agree. 11 + 5 + 20 + 0 + 5 + 10 = 51.
Agent interface GLM-4.7-Flash +20
GLM-4.7-Flash 85
A Jinja chat template sits in the repository, for 20. The tool-calling format is the full 25, because Z.ai itself names the glm47 parser for vLLM and SGLang, says tools are declared in OpenAI style and shows calls coming back as tool_calls. The template takes several calls in one turn, for 20. Structured output is 0, with nothing on the card and only the hosted API documented for JSON. Reasoning control is 10, thinking turned off per request with enable_thinking and the reasoning returned by the glm45 parser. Context is 10, 202,752 positions native with no rope scaling.
Kimi K3.1 Pro 65
Chat template 10, the format is only code to run. Tool-calling format 10, the parser is named in the runtimes' recipes and the card doesn't say how tools are declared. Trained tool calling 20, several calls a turn on Kimi K3's tools channel. Structured output 5, named among linked guides without the how. Reasoning control 10, reasoning_effort per request. Context 10, 1M tokens. 10 + 10 + 20 + 5 + 10 + 10 = 65.
Risk GLM-4.7-Flash +5
GLM-4.7-Flash 55
File safety is full, with safetensors only and the card loading it through Transformers' own code without remote code. Provenance is full, files in the zai-org organisation Z.ai links from GitHub with a SHA-256 for every shard. Safety evaluation and agent guidance are 0, as nothing is published on either. Licence risk is the full 15, since MIT adds no policy, threshold or changeable terms, though the repository carries no LICENSE file. The security programme is 0, with no security.txt, no SECURITY file and no advisories found.
Kimi K3.1 Pro 50
File safety 15, safetensors only, less 10 for remote code and prompts built by the repository's script. Provenance 15, Moonshot's own organisation with a hash on every file. Safety evaluation 10, Moonshot's own cyber benchmark results and nobody else's yet. Agent guidance 0, none on the card. Licence risk 10, the revenue threshold. Security programme 0, no way to report a flaw found. 15 + 15 + 10 + 0 + 10 + 0 = 50.
Score by category
| Category | Weight this run | GLM-4.7-Flash | Kimi K3.1 Pro | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 80 | 40 | GLM-4.7-Flash +40 |
| Risk | 20%20 | 55 | 50 | GLM-4.7-Flash +5 |
| Agent interface | 10%10 | 85 | 65 | GLM-4.7-Flash +20 |
| Documentation and upkeep | 10%10 | 28 | 51 | Kimi K3.1 Pro +23 |
| Negative events | ≤15 | 0 | 0 | |
| Total | B 63/100 | D 48.6/100 |
Facts side by side
| Fact | GLM-4.7-Flash | Kimi K3.1 Pro |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Z.ai (Zhipu AI) | Moonshot AI |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | Kimi K3.1 Pro License (modified MIT) |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-01-29 | 2026-11-05 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 2.8/5 (4) | none |
Verdicts
GLM-4.7-Flash
A small MIT-licensed mixture-of-experts model with about 3B active parameters, for local agent and coding work with several tool calls per turn and optional thinking. It runs in Ollama at about 23 GB and in nearly every common runtime. The main risk is neglect, with no changes or replies since early February and no safety results.
Kimi K3.1 Pro
Moonshot's newest flagship, eight days old when read, for operators with a rack. Only llama.cpp has its model code in a release, with vLLM and SGLang support still on a release candidate and a branch. The licence is permissive below a revenue threshold, and Moonshot hasn't answered a discussion on the repository yet.
Before you call either
GLM-4.7-Flash
- On our reference machine start it with
vllm serve zai-org/GLM-4.7-Flash --max-model-len 65536 --tool-call-parser glm47 --reasoning-parser glm45 --enable-auto-tool-choice --served-model-name glm-4.7-flash - Or run
ollama run glm-4.7-flashfor the 19 GB q4_K_M build - Without the glm47 and glm45 parsers, tool calls and reasoning come back as plain text
- Use a released vLLM (0.32.0 or later) or SGLang (0.5.22 or later); the card's nightly and pull-request builds date from January 2026
- Pass
chat_template_kwargs: {"enable_thinking": false}for short tasks, since thinking is on by default - Set
clear_thinking: falseto keep earlier reasoning in multi-turn agent work, as Z.ai does for τ²-Bench and Terminal Bench - Set the context length yourself, since config.json allows 202,752 tokens and tokenizer_config.json says 128,000
Kimi K3.1 Pro
- Use Moonshot's API or Together unless you have a node of eight B300-class accelerators
- With vLLM use 0.34.0 or later and the recipe's flags,
--tool-call-parser kimi_k3 --reasoning-parser kimi_k3 - Pass the whole assistant message back in later turns, reasoning_content and tool_calls included, as the card requires
- Set
reasoning_effortto low, high or max per request. Max is the default and thinking can't be turned off - Kimi K3's model code doesn't load it, because the attention layers changed
- Validate JSON output yourself, since the card doesn't document structured output for the weights
Questions
Which is better for AI agents, GLM-4.7-Flash or Kimi K3.1 Pro?
GLM-4.7-Flash scores 63 (B) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in 3 of 4 scored categories. Kimi K3.1 Pro leads on documentation and upkeep.
Other comparisons with GLM-4.7-Flash or Kimi K3.1 Pro
- Agents-A1-4B vs GLM-4.7-Flash
- Agents-A1-4B vs Kimi K3.1 Pro
- Apertus v1.5 8B vs GLM-4.7-Flash
- Apertus v1.5 8B vs Kimi K3.1 Pro
- Apodex-1.1-mini vs GLM-4.7-Flash
- Apodex-1.1-mini vs Kimi K3.1 Pro
- Arete-27B vs GLM-4.7-Flash
- Arete-27B vs Kimi K3.1 Pro
- BigBang-v1 vs GLM-4.7-Flash
- BigBang-v1 vs Kimi K3.1 Pro
- Carbon-X vs GLM-4.7-Flash
- Carbon-X vs Kimi K3.1 Pro
- Cosmos-Reason2-2B vs GLM-4.7-Flash
- Cosmos-Reason2-2B vs Kimi K3.1 Pro
- Darwin-36B-Opus vs GLM-4.7-Flash
- Darwin-36B-Opus vs Kimi K3.1 Pro
- DeepSeek-V3.2 vs GLM-4.7-Flash
- DeepSeek-V3.2 vs Kimi K3.1 Pro
- DeepSeek V4.1 Flash vs GLM-4.7-Flash
- DeepSeek V4.1 Flash vs Kimi K3.1 Pro
- DeepSeek-V4-Flash-0731 vs GLM-4.7-Flash
- DeepSeek-V4-Flash-0731 vs Kimi K3.1 Pro
- DiffusionGemma 26B A4B IT vs GLM-4.7-Flash
- DiffusionGemma 26B A4B IT vs Kimi K3.1 Pro
- Gemma 4 12B IT vs GLM-4.7-Flash
- Gemma 4 12B IT vs Kimi K3.1 Pro
- Gemma 4 26B A4B IT vs GLM-4.7-Flash
- Gemma 4 26B A4B IT vs Kimi K3.1 Pro
- Gemma 4 31B IT vs GLM-4.7-Flash
- Gemma 4 31B IT vs Kimi K3.1 Pro
- Gemma 4 E2B IT vs GLM-4.7-Flash
- Gemma 4 E2B IT vs Kimi K3.1 Pro
- Gemma 4 E4B IT vs GLM-4.7-Flash
- Gemma 4 E4B IT vs Kimi K3.1 Pro
- GLM-4.5-Air vs GLM-4.7-Flash
- GLM-4.5-Air vs Kimi K3.1 Pro
- GLM-4.7-Flash vs GLM-5.3
- GLM-4.7-Flash vs GLM-5.3-Flash
- GLM-4.7-Flash vs gpt-oss-120b
- GLM-4.7-Flash vs gpt-oss-20b
- GLM-4.7-Flash vs Granite 4.2 30B
- GLM-4.7-Flash vs Granite 4.2 3B
- GLM-4.7-Flash vs Granite 4.2 8B
- GLM-4.7-Flash vs Hy-Memory-30B-A3B
- GLM-4.7-Flash vs Hy3
- GLM-4.7-Flash vs Inkling
- GLM-4.7-Flash vs Inkling-Small
- GLM-4.7-Flash vs InternVL3.5-GPT-OSS-20B-A4B-Preview
- GLM-4.7-Flash vs Kimi K3
- GLM-4.7-Flash vs Kolibri-1
- GLM-4.7-Flash vs Kronos
- GLM-4.7-Flash vs Laguna M.1
- GLM-4.7-Flash vs Laguna S 2.1
- GLM-4.7-Flash vs Laguna XS 2.1
- GLM-4.7-Flash vs LBS-27B
- GLM-4.7-Flash vs LFM2.5-2.6B
- GLM-4.7-Flash vs LFM3-1.7B
- GLM-4.7-Flash vs LFM3-Audio-2B
- GLM-4.7-Flash vs LFM3-Omni-2B
- GLM-4.7-Flash vs LFM3-VL-2B
- GLM-4.7-Flash vs Ling-3.0-tiny
- GLM-4.7-Flash vs Ling-3.1-flash-1019
- GLM-4.7-Flash vs MiMo-V2.6 Distill Qwen 9B
- GLM-4.7-Flash vs MiniCPM-o 4.5
- GLM-4.7-Flash vs MiniCPM-V 4.7 1B
- GLM-4.7-Flash vs MiniCPM-V 4.7 35B-A3B
- GLM-4.7-Flash vs MiniCPM5-2B
- GLM-4.7-Flash vs MiniMax-M2.5
- GLM-4.7-Flash vs MiniMax-M2.7
- GLM-4.7-Flash vs MiniMax-M3.1-Flash
- GLM-4.7-Flash vs Mistral Large 4
- GLM-4.7-Flash vs Muse Glimmer 30B
- GLM-4.7-Flash vs Nanbeige4.3-33B
- GLM-4.7-Flash vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- GLM-4.7-Flash vs Nemotron 3.5 Lightning 30B A3B
- GLM-4.7-Flash vs Nemotron 3 Nano 30B A3B
- GLM-4.7-Flash vs Nemotron 3 Nano 4B
- GLM-4.7-Flash vs Nemotron 3 Nano Omni 30B A3B Reasoning
- GLM-4.7-Flash vs Nemotron 3 Super 120B A12B
- GLM-4.7-Flash vs Nemotron 3 Ultra 550B A55B
- GLM-4.7-Flash vs occamy-1.0
- GLM-4.7-Flash vs Olmo 3 7B Think
- GLM-4.7-Flash vs Ornith-1.5-122B
- GLM-4.7-Flash vs Ornith-1.5-35B-A3B
- GLM-4.7-Flash vs Ornith-1.5-397B
- GLM-4.7-Flash vs Ornith-1.5-9B
- GLM-4.7-Flash vs Qwen-AgentWorld-35B-A3B
- GLM-4.7-Flash vs Qwen3-30B-A3B-Instruct-2507
- GLM-4.7-Flash vs Qwen3-4B-Instruct-2507
- GLM-4.7-Flash vs Qwen3.5-0.8B
- GLM-4.7-Flash vs Qwen3.5-122B-A10B
- GLM-4.7-Flash vs Qwen3.5-2B
- GLM-4.7-Flash vs Qwen3.5-397B-A17B
- GLM-4.7-Flash vs Qwen3.5-4B
- GLM-4.7-Flash vs Qwen3.5-9B
- GLM-4.7-Flash vs Qwen3.6-27B
- GLM-4.7-Flash vs Qwen3.6-35B-A3B
- GLM-4.7-Flash vs Qwen3.8-27B
- GLM-4.7-Flash vs Qwen3.8-Flash-Next
- GLM-4.7-Flash vs Qwen3-Coder-30B-A3B-Instruct
- GLM-4.7-Flash vs Qwen3-Coder-Next
- GLM-4.7-Flash vs Qwen3-Omni-30B-A3B-Instruct
- GLM-4.7-Flash vs Qwen3-VL-2B-Instruct
- GLM-4.7-Flash vs Qwen3-VL-30B-A3B-Instruct
- GLM-4.7-Flash vs Qwen3-VL-32B-Instruct
- GLM-4.7-Flash vs Qwen3-VL-4B-Instruct
- GLM-4.7-Flash vs Qwen3-VL-8B-Instruct
- GLM-4.7-Flash vs Qwopus3.8-27B-Flash-V2
- GLM-4.7-Flash vs Recursive Flint
- GLM-4.7-Flash vs Ripple-65B Preview
- GLM-4.7-Flash vs SmolLM3-3B
- GLM-4.7-Flash vs Spark-X2.6-4B
- GLM-4.7-Flash vs Swift 1.5 Qwen3.8-27B
- GLM-4.7-Flash vs Swift 1.5 Qwen3.8-Flash-Next
- GLM-4.7-Flash vs ThinkingCap (Qwen3.8 27B)
- GLM-4.7-Flash vs Baidu Unlimited-OCR-2
- GLM-5.3-Flash vs Kimi K3.1 Pro
- GLM-5.3 vs Kimi K3.1 Pro
- gpt-oss-120b vs Kimi K3.1 Pro
- gpt-oss-20b vs Kimi K3.1 Pro
- Granite 4.2 30B vs Kimi K3.1 Pro
- Granite 4.2 3B vs Kimi K3.1 Pro
- Granite 4.2 8B vs Kimi K3.1 Pro
- Hy-Memory-30B-A3B vs Kimi K3.1 Pro
- Hy3 vs Kimi K3.1 Pro
- Inkling-Small vs Kimi K3.1 Pro
- Inkling vs Kimi K3.1 Pro
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Kimi K3.1 Pro
- Kimi K3.1 Pro vs Kolibri-1
- Kimi K3.1 Pro vs Kronos
- Kimi K3.1 Pro vs Laguna M.1
- Kimi K3.1 Pro vs Laguna S 2.1
- Kimi K3.1 Pro vs Laguna XS 2.1
- Kimi K3.1 Pro vs LBS-27B
- Kimi K3.1 Pro vs LFM2.5-2.6B
- Kimi K3.1 Pro vs LFM3-1.7B
- Kimi K3.1 Pro vs LFM3-Audio-2B
- Kimi K3.1 Pro vs LFM3-Omni-2B
- Kimi K3.1 Pro vs LFM3-VL-2B
- Kimi K3.1 Pro vs Ling-3.0-tiny
- Kimi K3.1 Pro vs Ling-3.1-flash-1019
- Kimi K3.1 Pro vs MiMo-V2.6 Distill Qwen 9B
- Kimi K3.1 Pro vs MiniCPM-o 4.5
- Kimi K3.1 Pro vs MiniCPM-V 4.7 1B
- Kimi K3.1 Pro vs MiniCPM-V 4.7 35B-A3B
- Kimi K3.1 Pro vs MiniCPM5-2B
- Kimi K3.1 Pro vs MiniMax-M2.5
- Kimi K3.1 Pro vs MiniMax-M2.7
- Kimi K3.1 Pro vs MiniMax-M3.1-Flash
- Kimi K3.1 Pro vs Mistral Large 4
- Kimi K3.1 Pro vs Muse Glimmer 30B
- Kimi K3.1 Pro vs Nanbeige4.3-33B
- Kimi K3.1 Pro vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- Kimi K3.1 Pro vs Nemotron 3.5 Lightning 30B A3B
- Kimi K3.1 Pro vs Nemotron 3 Nano 30B A3B
- Kimi K3.1 Pro vs Nemotron 3 Nano 4B
- Kimi K3.1 Pro vs Nemotron 3 Nano Omni 30B A3B Reasoning
- Kimi K3.1 Pro vs Nemotron 3 Super 120B A12B
- Kimi K3.1 Pro vs Nemotron 3 Ultra 550B A55B
- Kimi K3.1 Pro vs occamy-1.0
- Kimi K3.1 Pro vs Olmo 3 7B Think
- Kimi K3.1 Pro vs Ornith-1.5-122B
- Kimi K3.1 Pro vs Ornith-1.5-35B-A3B
- Kimi K3.1 Pro vs Ornith-1.5-397B
- Kimi K3.1 Pro vs Ornith-1.5-9B
- Kimi K3.1 Pro vs Qwen-AgentWorld-35B-A3B
- Kimi K3.1 Pro vs Qwen3-30B-A3B-Instruct-2507
- Kimi K3.1 Pro vs Qwen3-4B-Instruct-2507
- Kimi K3.1 Pro vs Qwen3.5-0.8B
- Kimi K3.1 Pro vs Qwen3.5-122B-A10B
- Kimi K3.1 Pro vs Qwen3.5-2B
- Kimi K3.1 Pro vs Qwen3.5-397B-A17B
- Kimi K3.1 Pro vs Qwen3.5-4B
- Kimi K3.1 Pro vs Qwen3.5-9B
- Kimi K3.1 Pro vs Qwen3.6-27B
- Kimi K3.1 Pro vs Qwen3.6-35B-A3B
- Kimi K3.1 Pro vs Qwen3.8-27B
- Kimi K3.1 Pro vs Qwen3.8-Flash-Next
- Kimi K3.1 Pro vs Qwen3-Coder-30B-A3B-Instruct
- Kimi K3.1 Pro vs Qwen3-Coder-Next
- Kimi K3.1 Pro vs Qwen3-Omni-30B-A3B-Instruct
- Kimi K3.1 Pro vs Qwen3-VL-2B-Instruct
- Kimi K3.1 Pro vs Qwen3-VL-30B-A3B-Instruct
- Kimi K3.1 Pro vs Qwen3-VL-32B-Instruct
- Kimi K3.1 Pro vs Qwen3-VL-4B-Instruct
- Kimi K3.1 Pro vs Qwen3-VL-8B-Instruct
- Kimi K3.1 Pro vs Qwopus3.8-27B-Flash-V2
- Kimi K3.1 Pro vs Recursive Flint
- Kimi K3.1 Pro vs Ripple-65B Preview
- Kimi K3.1 Pro vs SmolLM3-3B
- Kimi K3.1 Pro vs Spark-X2.6-4B
- Kimi K3.1 Pro vs Swift 1.5 Qwen3.8-27B
- Kimi K3.1 Pro vs Swift 1.5 Qwen3.8-Flash-Next
- Kimi K3.1 Pro vs ThinkingCap (Qwen3.8 27B)
- Kimi K3.1 Pro vs Baidu Unlimited-OCR-2
- Kimi K3 vs Kimi K3.1 Pro
Machine-readable
- This page as Markdown
/compare/glm-4-7-flash-vs-kimi-k3-1-pro.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/glm-4-7-flash.json·/api/v1/tools/kimi-k3-1-pro.json - From a terminal
anchor compare glm-4-7-flash kimi-k3-1-pro(the CLI) - Over MCP
compare_tools {"a": "glm-4-7-flash", "b": "kimi-k3-1-pro"}at/mcp, no key