GLM-4.7-Flash vs Nemotron 3 Super 120B A12B

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.

Nemotron 3 Super 120B A12B

A long-context agent cousin with trained parallel tool calls, a reasoning switch and a full account of its training data, under a free commercial licence. It needs about 100 GB of GPU memory, a 128 GB unified machine or Blackwell hardware for NVIDIA's NVFP4. The main risks are serving commands that run repository code and no published safety results.

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

Nemotron 3 Super 120B A12B

1. On the reference machine start it with ollama run nemotron-3-super, the 87 GB q4_K_M build, the only one that fits (about 104 GB with the margin)
2. Raise the Strix Halo GPU memory limit first with amd-ttm --set 100, since the default of about 62.8 GiB is far below what it needs
3. With vLLM serve it with --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder (SGLang uses --reasoning-parser nemotron_3)
4. Use temperature=1.0 and top_p=0.95 for every task, as the card says
5. Thinking is on by default. Pass enable_thinking: false or low_effort: true in chat_template_kwargs for short tasks
6. For coding agents pass force_nonempty_content: true in chat_template_kwargs, so a cut-off reasoning trace still returns content
7. Raise context past 262,144 only with the card's flags (VLLM_ALLOW_LONG_MAX_MODEL_LEN=1, --max-model-len 1048576)