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

GLM-4.7-Flash vs occamy-1.0

GLM-4.7-Flash scores 63 (B) on agent readiness against occamy-1.0's 61 (C), and leads in 1 of 4 scored categories. occamy-1.0 leads on reach and documentation and upkeep. Both do open-weight models you run yourself.

Best open-weight models you download and run yourself for AI agents · All 2870 open weights comparisons

Which one, for what

GLM-4.7-Flash B

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

  • Agent interface, 85 against 70

Watch for

No change to the files since 29 January 2026 and no zai-org reply on discussions since 31 January

occamy-1.0 C

Best for Agents that want an Apache 2.0 mixture-of-experts model tuned for long tool-using tasks on one 24 to 32 GB GPU or Mac.

Ahead on

  • Reach, 85 against 80
  • Documentation and upkeep, 74 against 28

Watch for

No safety results, prompt-injection guidance or security contact

Score by category

CategoryWeight this runGLM-4.7-Flashoccamy-1.0Edge
Capability35%pendingpendingpendingnot scored in this run
Reach25%258085occamy-1.0 +5
Risk20%205555even
Agent interface10%108570GLM-4.7-Flash +15
Documentation and upkeep10%102874occamy-1.0 +46
Negative events≤1500
TotalB 63/100C 61/100

Facts side by side

FactGLM-4.7-Flashoccamy-1.0
KindOpen-weight modelOpen-weight model
VendorZ.ai (Zhipu AI)Accio (Accio-Lab)
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceMITApache-2.0
Read-only variant documentednono
llms.txtnono
Last release2026-01-292026-10-02
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Customer content may train models
Terms restrict automated access
Terms restrict benchmarking
Terms or service can change without notice
Arbitration or class-action waiver
Agent reviews2.8/5 (4)3/5 (4)

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.

occamy-1.0

A Qwen3.6-35B-A3B fine-tuned for long agent work with tools, files and code, under plain Apache 2.0, and Accio's 4-bit builds run in about 23 GB. The main gaps are no safety results, agent guidance or security contact, and every benchmark figure is Accio's own.

Before you call either

GLM-4.7-Flash

  1. 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
  2. Or run ollama run glm-4.7-flash for the 19 GB q4_K_M build
  3. Without the glm47 and glm45 parsers, tool calls and reasoning come back as plain text
  4. 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
  5. Pass chat_template_kwargs: {"enable_thinking": false} for short tasks, since thinking is on by default
  6. Set clear_thinking: false to keep earlier reasoning in multi-turn agent work, as Z.ai does for τ²-Bench and Terminal Bench
  7. Set the context length yourself, since config.json allows 202,752 tokens and tokenizer_config.json says 128,000

occamy-1.0

  1. Serve with --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder on vLLM 0.19.0 or SGLang 0.5.10 and later
  2. BF16 is 70.2 GB, so on a 128 GB machine run it without the card's 8-way tensor parallelism
  3. Keep the whole assistant message, reasoning and tool calls included, in later turns, as the card says
  4. Sampling per the card is temperature 1.0, top_p 0.95, top_k 20 and presence_penalty 1.5
  5. With the GGUFs, NFC-normalise input text and read TOKENIZER.md, since the files store qwen2 pre-tokenizer metadata
  6. The base's MTP layer isn't in this checkpoint, Accio-Lab/occamy-1.0-MTP is an experimental separate head

Questions

Which is better for AI agents, GLM-4.7-Flash or occamy-1.0?

GLM-4.7-Flash scores 63 (B) on agent readiness against occamy-1.0's 61 (C), and leads in 1 of 4 scored categories. occamy-1.0 leads on reach and documentation and upkeep.

Other comparisons with GLM-4.7-Flash or occamy-1.0

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