Head to head · Open-weight models you run yourself · October 2026 research run
GLM-5.3 vs occamy-1.0
GLM-5.3 scores 64.6 (B) on agent readiness against occamy-1.0's 61 (C), and leads in 0 of 4 scored categories. occamy-1.0 leads on reach, risk 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
GLM-5.3Z.ai (Zhipu AI) · Open-weight model
B64.6/100#17 of 100 open-weight models
occamy-1.0Accio (Accio-Lab) · Open-weight model
C61/100#51 of 100 open-weight models
Which one, for what
GLM-5.3 B
Best for Teams with an 8-GPU server who want an open coding and long-horizon agent model with a 1M-token context, or agents that call it through Z.ai or a hosted provider
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
Z.ai's smallest build is 755.6 GB in FP8, with no local Ollama or LM Studio build
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 55
- Risk, 55 against 50
- Documentation and upkeep, 74 against 58
Watch for
No safety results, prompt-injection guidance or security contact
Score by category
| Category | Weight this run | GLM-5.3 | occamy-1.0 | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 55 | 85 | occamy-1.0 +30 |
| Risk | 20%20 | 50 | 55 | occamy-1.0 +5 |
| Agent interface | 10%10 | 70 | 70 | even |
| Documentation and upkeep | 10%10 | 58 | 74 | occamy-1.0 +16 |
| Negative events | ≤15 | 0 | 0 | |
| Total | B 64.6/100 | C 61/100 |
Facts side by side
| Fact | GLM-5.3 | occamy-1.0 |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Z.ai (Zhipu AI) | Accio (Accio-Lab) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | GLM-5.3 License (MIT-style, with a security review for the largest model-as-a-service operators) | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-14 | 2026-10-02 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no 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 reviews | 2.8/5 (4) | 3/5 (4) |
Verdicts
GLM-5.3
A large coding and long-horizon agent model with a 1M-token context, several tool calls per turn and three reasoning budgets. Z.ai's smallest build is 756 GB in FP8, so it needs an 8-GPU server, Z.ai's API or a provider. The main risks are no published safety work, no way to report a flaw, and thinking that can't be turned off.
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-5.3
- Start it with
vllm serve zai-org/GLM-5.3 --kv-cache-dtype fp8 --tensor-parallel-size 8 --speculative-config.method mtp --speculative-config.num_speculative_tokens 5 --tool-call-parser glm47 --reasoning-parser glm47 --enable-auto-tool-choice --served-model-name glm-5.3(vLLM 0.28.0 or later, 8xH200 or 8xH20 per vLLM's recipe) - Use the glm47 tool-call and reasoning parsers in vLLM or SGLang; SGLang's cookbook says glm45 leaves the calls as plain text
- Pass
chat_template_kwargs: {"reasoning_effort": "low"}for short tasks; anything other than low or high falls back to max - Pass
clear_thinking: truein chat use, since the template keeps earlier reasoning by default - Don't send
enable_thinking: false; the template ignores it and always opens a think block - Validate JSON output yourself, since structured output is documented only for Z.ai's hosted API
- No Z.ai build fits a 128 GB machine; the full 1M context needs 8xB200 per vLLM's recipe
occamy-1.0
- Serve with
--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coderon vLLM 0.19.0 or SGLang 0.5.10 and later - BF16 is 70.2 GB, so on a 128 GB machine run it without the card's 8-way tensor parallelism
- Keep the whole assistant message, reasoning and tool calls included, in later turns, as the card says
- Sampling per the card is temperature 1.0, top_p 0.95, top_k 20 and presence_penalty 1.5
- With the GGUFs, NFC-normalise input text and read TOKENIZER.md, since the files store qwen2 pre-tokenizer metadata
- 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-5.3 or occamy-1.0?
GLM-5.3 scores 64.6 (B) on agent readiness against occamy-1.0's 61 (C), and leads in 0 of 4 scored categories. occamy-1.0 leads on reach, risk and documentation and upkeep.
Other comparisons with GLM-5.3 or occamy-1.0
- Agents-A1-4B vs GLM-5.3
- Agents-A1-4B vs occamy-1.0
- Apertus v1.5 8B vs GLM-5.3
- Apertus v1.5 8B vs occamy-1.0
- Apodex-1.1-mini vs GLM-5.3
- Apodex-1.1-mini vs occamy-1.0
- BigBang-v1 vs GLM-5.3
- BigBang-v1 vs occamy-1.0
- Cosmos-Reason2-2B vs GLM-5.3
- Cosmos-Reason2-2B vs occamy-1.0
- Darwin-36B-Opus vs GLM-5.3
- Darwin-36B-Opus vs occamy-1.0
- DeepSeek-V3.2 vs GLM-5.3
- DeepSeek-V3.2 vs occamy-1.0
- DeepSeek V4.1 Flash vs GLM-5.3
- DeepSeek V4.1 Flash vs occamy-1.0
- DeepSeek-V4-Flash-0731 vs GLM-5.3
- DeepSeek-V4-Flash-0731 vs occamy-1.0
- DiffusionGemma 26B A4B IT vs GLM-5.3
- DiffusionGemma 26B A4B IT vs occamy-1.0
- Gemma 4 12B IT vs GLM-5.3
- Gemma 4 12B IT vs occamy-1.0
- Gemma 4 26B A4B IT vs GLM-5.3
- Gemma 4 26B A4B IT vs occamy-1.0
- Gemma 4 31B IT vs GLM-5.3
- Gemma 4 31B IT vs occamy-1.0
- Gemma 4 E2B IT vs GLM-5.3
- Gemma 4 E2B IT vs occamy-1.0
- Gemma 4 E4B IT vs GLM-5.3
- Gemma 4 E4B IT vs occamy-1.0
- GLM-4.5-Air vs GLM-5.3
- GLM-4.5-Air vs occamy-1.0
- GLM-4.7-Flash vs GLM-5.3
- GLM-4.7-Flash vs occamy-1.0
- GLM-5.3-Flash vs occamy-1.0
- GLM-5.3 vs GLM-5.3-Flash
- GLM-5.3 vs gpt-oss-120b
- GLM-5.3 vs gpt-oss-20b
- GLM-5.3 vs Granite 4.2 3B
- GLM-5.3 vs Hy3
- GLM-5.3 vs Inkling
- GLM-5.3 vs Inkling-Small
- GLM-5.3 vs InternVL3.5-GPT-OSS-20B-A4B-Preview
- GLM-5.3 vs Kimi K3
- GLM-5.3 vs Kolibri-1
- GLM-5.3 vs Laguna M.1
- GLM-5.3 vs Laguna S 2.1
- GLM-5.3 vs Laguna XS 2.1
- GLM-5.3 vs LFM2.5-2.6B
- GLM-5.3 vs Ling-3.0-tiny
- GLM-5.3 vs MiMo-V2.6 Distill Qwen 9B
- GLM-5.3 vs MiniCPM-o 4.5
- GLM-5.3 vs MiniCPM5-2B
- GLM-5.3 vs MiniMax-M2.5
- GLM-5.3 vs MiniMax-M2.7
- GLM-5.3 vs Muse Glimmer 30B
- GLM-5.3 vs Nemotron 3.5 Lightning 30B A3B
- GLM-5.3 vs Nemotron 3 Nano 30B A3B
- GLM-5.3 vs Nemotron 3 Nano 4B
- GLM-5.3 vs Nemotron 3 Nano Omni 30B A3B Reasoning
- GLM-5.3 vs Nemotron 3 Super 120B A12B
- GLM-5.3 vs Nemotron 3 Ultra 550B A55B
- GLM-5.3 vs Olmo 3 7B Think
- GLM-5.3 vs Ornith-1.5-35B-A3B
- GLM-5.3 vs Ornith-1.5-397B
- GLM-5.3 vs Ornith-1.5-9B
- GLM-5.3 vs Qwen3-30B-A3B-Instruct-2507
- GLM-5.3 vs Qwen3-4B-Instruct-2507
- GLM-5.3 vs Qwen3.5-0.8B
- GLM-5.3 vs Qwen3.5-122B-A10B
- GLM-5.3 vs Qwen3.5-2B
- GLM-5.3 vs Qwen3.5-397B-A17B
- GLM-5.3 vs Qwen3.5-4B
- GLM-5.3 vs Qwen3.5-9B
- GLM-5.3 vs Qwen3.6-27B
- GLM-5.3 vs Qwen3.6-35B-A3B
- GLM-5.3 vs Qwen3.8-27B
- GLM-5.3 vs Qwen3.8-Flash-Next
- GLM-5.3 vs Qwen3-Coder-30B-A3B-Instruct
- GLM-5.3 vs Qwen3-Coder-Next
- GLM-5.3 vs Qwen3-Omni-30B-A3B-Instruct
- GLM-5.3 vs Qwen3-VL-2B-Instruct
- GLM-5.3 vs Qwen3-VL-30B-A3B-Instruct
- GLM-5.3 vs Qwen3-VL-32B-Instruct
- GLM-5.3 vs Qwen3-VL-4B-Instruct
- GLM-5.3 vs Qwen3-VL-8B-Instruct
- GLM-5.3 vs Qwopus3.8-27B-Flash-V2
- GLM-5.3 vs SmolLM3-3B
- GLM-5.3 vs Spark-X2.6-4B
- GLM-5.3 vs Swift 1.5 Qwen3.8-27B
- GLM-5.3 vs Swift 1.5 Qwen3.8-Flash-Next
- GLM-5.3 vs ThinkingCap (Qwen3.8 27B)
- gpt-oss-120b vs occamy-1.0
- gpt-oss-20b vs occamy-1.0
- Granite 4.2 3B vs occamy-1.0
- Hy3 vs occamy-1.0
- Inkling-Small vs occamy-1.0
- Inkling vs occamy-1.0
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs occamy-1.0
- Kimi K3 vs occamy-1.0
- Kolibri-1 vs occamy-1.0
- Laguna M.1 vs occamy-1.0
- Laguna S 2.1 vs occamy-1.0
- Laguna XS 2.1 vs occamy-1.0
- LFM2.5-2.6B vs occamy-1.0
- Ling-3.0-tiny vs occamy-1.0
- MiMo-V2.6 Distill Qwen 9B vs occamy-1.0
- MiniCPM-o 4.5 vs occamy-1.0
- MiniCPM5-2B vs occamy-1.0
- MiniMax-M2.5 vs occamy-1.0
- MiniMax-M2.7 vs occamy-1.0
- Muse Glimmer 30B vs occamy-1.0
- Nemotron 3.5 Lightning 30B A3B vs occamy-1.0
- Nemotron 3 Nano 30B A3B vs occamy-1.0
- Nemotron 3 Nano 4B vs occamy-1.0
- Nemotron 3 Nano Omni 30B A3B Reasoning vs occamy-1.0
- Nemotron 3 Super 120B A12B vs occamy-1.0
- Nemotron 3 Ultra 550B A55B vs occamy-1.0
- occamy-1.0 vs Olmo 3 7B Think
- occamy-1.0 vs Ornith-1.5-35B-A3B
- occamy-1.0 vs Ornith-1.5-397B
- occamy-1.0 vs Ornith-1.5-9B
- occamy-1.0 vs Qwen3-30B-A3B-Instruct-2507
- occamy-1.0 vs Qwen3-4B-Instruct-2507
- occamy-1.0 vs Qwen3.5-0.8B
- occamy-1.0 vs Qwen3.5-122B-A10B
- occamy-1.0 vs Qwen3.5-2B
- occamy-1.0 vs Qwen3.5-397B-A17B
- occamy-1.0 vs Qwen3.5-4B
- occamy-1.0 vs Qwen3.5-9B
- occamy-1.0 vs Qwen3.6-27B
- occamy-1.0 vs Qwen3.6-35B-A3B
- occamy-1.0 vs Qwen3.8-27B
- occamy-1.0 vs Qwen3.8-Flash-Next
- occamy-1.0 vs Qwen3-Coder-30B-A3B-Instruct
- occamy-1.0 vs Qwen3-Coder-Next
- occamy-1.0 vs Qwen3-Omni-30B-A3B-Instruct
- occamy-1.0 vs Qwen3-VL-2B-Instruct
- occamy-1.0 vs Qwen3-VL-30B-A3B-Instruct
- occamy-1.0 vs Qwen3-VL-32B-Instruct
- occamy-1.0 vs Qwen3-VL-4B-Instruct
- occamy-1.0 vs Qwen3-VL-8B-Instruct
- occamy-1.0 vs Qwopus3.8-27B-Flash-V2
- occamy-1.0 vs SmolLM3-3B
- occamy-1.0 vs Spark-X2.6-4B
- occamy-1.0 vs Swift 1.5 Qwen3.8-27B
- occamy-1.0 vs Swift 1.5 Qwen3.8-Flash-Next
- occamy-1.0 vs ThinkingCap (Qwen3.8 27B)
Machine-readable
- This page as Markdown
/compare/glm-5-3-vs-occamy-1-0.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/glm-5-3.json·/api/v1/tools/occamy-1-0.json - From a terminal
anchor compare glm-5-3 occamy-1-0(the CLI) - Over MCP
compare_tools {"a": "glm-5-3", "b": "occamy-1-0"}at/mcp, no key