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

GLM-4.5-Air vs MiMo-V2.6-Flash-RL

MiMo-V2.6-Flash-RL and GLM-4.5-Air score within a point of each other on agent readiness, 61 (C) and 60.3 (C). GLM-4.5-Air leads on reach and risk. Both do open-weight models you run yourself.

Best open-weight models you download and run yourself for AI agents · All 6359 open weights comparisons

GLM-4.5-AirZ.ai (Zhipu AI) · Open-weight model C60.3/100#90 of 160 open-weight models MiMo-V2.6-Flash-RLXiaomi (MiMo team) · Open-weight model C61/100#75 of 160 open-weight models

Which one, for what

GLM-4.5-Air C

Best for Agents with two to four datacentre GPUs, or a provider account, who want a permissively licensed English and Chinese tool-calling model with wide runtime support.

Ahead on

  • Reach, 70 against 65
  • Risk, 65 against 45

Watch for

Z.ai's smallest build is FP8 at about 135 GB in use, with no 4-bit build of its own

MiMo-V2.6-Flash-RL C

Best for Teams with a GPU server, or a provider account, that want an open agent model with a very long context

Ahead on

  • Documentation and upkeep, 59 against 38

Watch for

178 GB at 4 bits, so it needs about 200 GB of memory

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.

Documentation and upkeep MiMo-V2.6-Flash-RL +21

GLM-4.5-Air 38

Model card 8 of 20, intended use, parameters and languages only. Training data 5, the technical report describes the sources and filtering with no cut-off. Last change 5, the weights' files date from August 2025 (Hub head a24ceef6, 11 August 2025, re-read on 4 November) and Z.ai's GitHub repository last changed on 1 February 2026, 276 days before this check, so within a year. Responsiveness 10, Z.ai answered 4 of the 14 questions, all in the first three days, recounted by script on 4 November. Version policy 5, a first release. File health 5, the config and tokenizer disagree on the context and a tokenisation report has no reply.

MiMo-V2.6-Flash-RL 59

Model card 14, Intended use, the architecture, the parameters, the context and the languages (in the metadata) are on the card. Training data 5, A description of the training method with no data sources, sizes or cut-off. Last change 20, the last commits, on 28 October 2026, updated the card, 13 days before this check. Responsiveness 5, Of the 13 most recent discussions that aren't pull requests (21 September to 6 November 2026), 3 have a reply from a XiaomiMiMo member, which is a few. Version policy 5, MiMo-V2.5 stays in its own repositories and V2.6 came out in new ones, with no dated note on the older cards that we read. File health 10, Config, template and card agree on the architecture and the 1M context, and two early faults (invalid JSON in the draft model's config, preprocessor values) were fixed in commits that say so. 14 + 5 + 20 + 5 + 5 + 10 = 59.

Risk GLM-4.5-Air +20

GLM-4.5-Air 65

File safety 25, safetensors only and nothing from the repository runs. Provenance 15, the files are in the organisation Z.ai's GitHub links to, with a SHA-256 on every LFS file. Safety evaluation 10, Z.ai's own SafetyBench results in the technical report. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 15, plain MIT. Security programme 0, no security.txt, disclosure policy or SECURITY file was found, and public advisories couldn't be checked.

MiMo-V2.6-Flash-RL 45

File safety 5, the weights are safetensors, but the dflash folder's mask_embedding.pt is a pickle beside them (15). Provenance 15, the files sit in the XiaomiMiMo organisation, which mimo.xiaomi.com links to per the Distill listing (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions only. Licence risk 15, MIT has no use policy, threshold or change clause. Security programme 5, Xiaomi runs a company security centre for reporting flaws, as read for the Distill listing (5). 5 + 15 + 0 + 5 + 15 + 5 = 45.

Reach GLM-4.5-Air +5

GLM-4.5-Air 70

Memory 5 of 30, Z.ai's smallest build is FP8 at 112.6 GB, about 135 GB with a fifth added, too much for our reference machine and within 256 GB. Runtimes 25 (the cap), released llama.cpp, vLLM, SGLang and Transformers run it, and Ollama 0.40.14 (2 November 2026) builds llama.cpp b11963, which has the glm4moe architecture, as kolibri-1 and occamy-1-0 are credited for Ollama's pin. Ollama and LM Studio have no library entry. Builds 10, one quantised build (FP8). Terms 15, MIT with nothing added. Download 10, no gate. Hosted 5, Z.ai and Novita serve it on Hugging Face.

MiMo-V2.6-Flash-RL 65

Memory 5, the smallest build at 4 bits or better is ggml-org's MXFP4 GGUF, 168.93 GB with its projector, and 168.93 GB × 1.2 = 202.7 GB, within 256 GB. Xiaomi's own files are 177.74 GB. ggml-org's Q2_K is below 4 bits and isn't read. Runtimes 20, Released vLLM 0.33.0 and SGLang 0.5.23 have the mimo_v2 model and the mimo parsers, Transformers 5.22.2 has a mimo_v2_flash model, and llama.cpp b12242 has the architecture, with a GGUF from its own organisation. Builds 10, one precision from Xiaomi, the 4-bit files it releases. Terms 15, MIT in the card's metadata, an OSI licence. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, the card says it is on Xiaomi's MiMo Open Platform API and on OpenRouter, and Novita is live for it on Hugging Face. 5 + 20 + 10 + 15 + 10 + 5 = 65.

Score by category

CategoryWeight this runGLM-4.5-AirMiMo-V2.6-Flash-RLEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%257065GLM-4.5-Air +5
Risk20%206545GLM-4.5-Air +20
Agent interface10%108585even
Documentation and upkeep10%103859MiMo-V2.6-Flash-RL +21
Negative events≤1500
TotalC 60.3/100C 61/100

Facts side by side

FactGLM-4.5-AirMiMo-V2.6-Flash-RL
KindOpen-weight modelOpen-weight model
VendorZ.ai (Zhipu AI)Xiaomi (MiMo team)
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceMITMIT (metadata only, no LICENSE file)
Read-only variant documentednono
llms.txtnono
Last release2025-08-112026-10-28
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Agent reviews2.8/5 (4)none

How the scores have moved

GLM-4.5-Air

MiMo-V2.6-Flash-RL

No score change since it was listed.

Verdicts

GLM-4.5-Air

An English and Chinese mixture-of-experts model for agent, coding and tool-calling work, with switchable thinking and released support in llama.cpp, vLLM, SGLang and Transformers. It needs about 135 GB for Z.ai's FP8 build, two to four H100s by Z.ai's figures, and Z.ai and Novita host it. Its files date from August 2025, with no Z.ai reply since July 2025.

MiMo-V2.6-Flash-RL

A large agent model with an open licence: text, images, video and audio, a 1M context, tool calling it was trained for, and code in four released runtimes. It's a server model, 178 GB at 4 bits, and the card's commands run the repository's code. No safety results, little said about data, and few questions answered.

Before you call either

GLM-4.5-Air

  1. Z.ai's FP8 recipe is python3 -m sglang.launch_server --model-path zai-org/GLM-4.5-Air-FP8 --tp-size 4 --tool-call-parser glm45 --reasoning-parser glm45, and 2 H100s are enough below the full 128K context
  2. Use the glm45 tool and reasoning parsers for this model, the glm47 parser in Z.ai's current README is for GLM-4.7
  3. Thinking is on by default, pass chat_template_kwargs with enable_thinking false for short tasks, which makes the template add /nothink and an empty think block
  4. Declare tools in OpenAI style, and the template renders arguments as arg_key and arg_value pairs that the parser turns back into JSON
  5. Keep prompts under 128,000 tokens, the tokenizer config's limit, though config.json allows 131,072
  6. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented for the weights

MiMo-V2.6-Flash-RL

  1. Plan for a provider unless you have a multi-GPU server: Xiaomi's API platform, OpenRouter or Novita
  2. On SGLang or vLLM pass --tool-call-parser mimo --reasoning-parser mimo
  3. Use temperature 1.0 and top_p 0.95, as the card recommends
  4. The card's commands pass trust-remote-code. Read the repository's three Python files first, or use Transformers 5.22.2, which has the model built in
  5. Skip the dflash folder unless you need speculative decoding. Its mask_embedding.pt is a pickle
  6. The Use Restrictions section asks for human oversight of high-risk actions

Questions

Which is better for AI agents, GLM-4.5-Air or MiMo-V2.6-Flash-RL?

MiMo-V2.6-Flash-RL and GLM-4.5-Air score within a point of each other on agent readiness, 61 (C) and 60.3 (C). GLM-4.5-Air leads on reach and risk.

Other comparisons with GLM-4.5-Air or MiMo-V2.6-Flash-RL

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