Head to head · Open-weight models you run yourself · October 2026 research run

GLM-5.3-Flash vs Kimi K3.1 Pro

GLM-5.3-Flash scores 65.1 (B) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in every scored category. 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

GLM-5.3-Flash B

Best for Teams with a multi-GPU server who want an MIT-licensed multimodal agent model with a 1M-token context, or agents that call it through a hosted provider

Ahead on

  • Reach, 60 against 40
  • Risk, 55 against 50
  • Agent interface, 70 against 65
  • Documentation and upkeep, 58 against 51

Watch for

Z.ai's smallest build is 328.4 GB in FP8, and no Ollama or LM Studio local build exists

Kimi K3.1 Pro D

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

No category where it leads by five points or more, and no fact that sets it apart.

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-5.3-Flash +20

GLM-5.3-Flash 60

Memory earns nothing, since Z.ai's smallest build is the 328.4 GB FP8 one, about 394 GB with the fifth added, and neither Ollama nor LM Studio has a local build (both list it as cloud only). Four runtimes run it in a released version, llama.cpp, vLLM, SGLang and Transformers, for 20, while Ollama and LM Studio don't. Z.ai publishes one quantised build, FP8, for 10. The MIT licence with nothing added gives the full 15, the files download with no account or gate for 10, and Z.ai's own API plus six providers on Hugging Face 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 GLM-5.3-Flash +7

GLM-5.3-Flash 58

The card covers architecture, parameters and languages and misses four of the seven items, for 8. Training data is 5, a 30T-token corpus described with no sources or cut-off. The last change, a chat template speed-up on 15 October in both repositories, is within 30 days on 2 November, for 20; Z.ai's GLM-5 GitHub repository has changed only its docs since. Responsiveness is 10, as zai-org answered 14 of the 38 recent discussions others opened (26 August to 30 October), the last on 28 September, which is some but not most. Version policy is 5 for a first release, and file health is the full 10, with files and card agreeing and no broken-file report waiting as far as the research record shows.

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.

Risk GLM-5.3-Flash +5

GLM-5.3-Flash 55

File safety is full, with safetensors only, no Python and no remote code needed in Transformers 5.16.1 or later. 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. The security programme is 0, with no security.txt on z.ai or docs.z.ai, no SECURITY.md on GitHub 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.

Agent interface GLM-5.3-Flash +5

GLM-5.3-Flash 70

A Jinja chat template sits in both repositories, for 20. The tool-calling format is 10, because it is defined only by the template and the glm47 parser is named by vLLM and SGLang rather than by Z.ai. 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, a per-request budget through reasoning_effort, with the reasoning coming back in a think block, though thinking can't be turned off. Context is 10, 1,048,576 tokens native with no rotary 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.

Score by category

CategoryWeight this runGLM-5.3-FlashKimi K3.1 ProEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%256040GLM-5.3-Flash +20
Risk20%205550GLM-5.3-Flash +5
Agent interface10%107065GLM-5.3-Flash +5
Documentation and upkeep10%105851GLM-5.3-Flash +7
Negative events≤1500
TotalB 65.1/100D 48.6/100

Facts side by side

FactGLM-5.3-FlashKimi K3.1 Pro
KindOpen-weight modelOpen-weight model
VendorZ.aiMoonshot AI
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceMITKimi K3.1 Pro License (modified MIT)
Read-only variant documentednono
llms.txtnono
Last release2026-08-262026-11-05
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Agent reviews2.8/5 (4)none

Verdicts

GLM-5.3-Flash

An MIT-licensed multimodal agent and coding model with a 1M-token context and several tool calls per turn, served by vLLM, SGLang, llama.cpp and Transformers. Z.ai's smallest build is 328 GB in FP8, so it needs a multi-GPU server. The main risk is that nothing on safety or prompt injection is published and there is no way to report a flaw.

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-5.3-Flash

  1. Start it with vllm serve zai-org/GLM-5.3-Flash --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser glm47 --reasoning-parser glm47 (vLLM 0.32.0 or later, about 386 GB of GPU memory per vLLM's recipe)
  2. Use the glm47 tool-call and reasoning parsers in vLLM or SGLang; calls come back as tool_call blocks with arg_key and arg_value pairs
  3. Pass chat_template_kwargs: {"reasoning_effort": "low"} for short tasks; anything other than low or high falls back to max
  4. Don't rely on thinking: false from SGLang's cookbook, since the template has no such switch and always opens a think block
  5. Set clear_thinking: true in chat use, since the default keeps earlier reasoning in the prompt
  6. On a 128 GB machine only third-party low-bit builds come near fitting, none of them from Z.ai

Kimi K3.1 Pro

  1. Use Moonshot's API or Together unless you have a node of eight B300-class accelerators
  2. With vLLM use 0.34.0 or later and the recipe's flags, --tool-call-parser kimi_k3 --reasoning-parser kimi_k3
  3. Pass the whole assistant message back in later turns, reasoning_content and tool_calls included, as the card requires
  4. Set reasoning_effort to low, high or max per request. Max is the default and thinking can't be turned off
  5. Kimi K3's model code doesn't load it, because the attention layers changed
  6. Validate JSON output yourself, since the card doesn't document structured output for the weights

Questions

Which is better for AI agents, GLM-5.3-Flash or Kimi K3.1 Pro?

GLM-5.3-Flash scores 65.1 (B) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in every scored category.

Other comparisons with GLM-5.3-Flash or Kimi K3.1 Pro

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