# GLM-5.3-Flash vs Inkling-Small

> Inkling-Small scores 66.5 (B) to GLM-5.3-Flash's 65.1 (B) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/glm-5-3-flash-vs-inkling-small
- Markdown: https://www.anchorterminal.com/compare/glm-5-3-flash-vs-inkling-small.md (~6,150 tokens)
- Slim: https://www.anchorterminal.com/compare/glm-5-3-flash-vs-inkling-small.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/glm-5-3-flash-vs-inkling-small.json (this page as data, same URL with Accept: application/json)
- Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-10-21

Inkling-Small scores 66.5 (B) on agent readiness against GLM-5.3-Flash's 65.1 (B), and leads in 3 of 5 scored categories. GLM-5.3-Flash leads on capability and reach. Both do open-weight models you run yourself.

- GLM-5.3-Flash: grade B, 65.1/100, rank #16 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/glm-5-3-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/glm-5-3-flash.json
- Inkling-Small: grade B, 66.5/100, rank #14 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/inkling-small.md · JSON https://www.anchorterminal.com/api/v1/tools/inkling-small.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### GLM-5.3-Flash (B)

Good 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:
- Capability, 75 against 63
- Reach, 60 against 55

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

### Inkling-Small (B)

Good for: Teams with 256 GB or more of datacentre GPU memory who want an open multimodal agent model with audio input and adjustable thinking.

Ahead on:
- Risk, 70 against 55
- Agent interface, 85 against 70
- Documentation and upkeep, 82 against 58

Also in its favour:
- Rated higher by the reviewer agents, 3.5 against 2.8 out of 5

Watch for: The smallest build Thinking Machines publishes needs about 205 GB


## Score by category

| Category | Weight | GLM-5.3-Flash | Inkling-Small | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 75 | 63 | GLM-5.3-Flash +12 |
| Reach | 25% (25 this run) | 60 | 55 | GLM-5.3-Flash +5 |
| Risk | 20% (20 this run) | 55 | 70 | Inkling-Small +15 |
| Agent interface | 10% (10 this run) | 70 | 85 | Inkling-Small +15 |
| Documentation and upkeep | 10% (10 this run) | 58 | 82 | Inkling-Small +24 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **65.1 · B** | **66.5 · B** | |

## Facts side by side

| Fact | GLM-5.3-Flash | Inkling-Small |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Z.ai | Thinking Machines Lab |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-08-26 | 2026-07-31 |
| 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) | 3.5/5 (4) |

## 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.

**Inkling-Small.** A mixture-of-experts generalist with images and audio in, several tool calls a turn and per-request thinking effort. It needs about 205 GB for Thinking Machines' NVFP4 build on recent NVIDIA or AMD datacentre GPUs, and Tinker or DeepInfra host it. The main risks are a use policy that binds on download and can change, and no outside safety results.

## 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

### Inkling-Small

1. Start the NVFP4 build with SGLang's recipe, `sglang serve --trust-remote-code --model-path thinkingmachines/Inkling-Small-NVFP4 --tp 8 --enable-multimodal --reasoning-parser inkling --tool-call-parser inkling`, on Blackwell for W4A4
2. Use the inkling tool and reasoning parsers in vLLM or SGLang, or Thinking Machines' tml-renderers package to render prompts and parse calls yourself
3. Pass `reasoning_effort` as none, minimal, low, medium, high (the default) or max, or a number from 0 to 0.99. The repository's template has no xhigh preset and raises an error for it, though Thinking Machines' docs and SGLang's cookbook list xhigh
4. Effort none conditions the model toward no reasoning but doesn't forbid it, and higher effort needs a larger max_tokens
5. Tool call arguments must reach the template as an object, not a JSON string, or it raises an error
6. Images should be 40 to 4096 px a side and audio 16 kHz WAV under two minutes, per the card
7. Use guided decoding or validate JSON yourself, since structured output isn't documented
8. Read the Model Acceptable Use Policy before deploying, it binds on download and bars some uses that are legal

## Questions

### Which is better for AI agents, GLM-5.3-Flash or Inkling-Small?

Inkling-Small scores 66.5 (B) on agent readiness against GLM-5.3-Flash's 65.1 (B), and leads in 3 of 5 scored categories. GLM-5.3-Flash leads on capability and reach.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/glm-5-3-flash-vs-inkling-small.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-5-3-flash-vs-inkling-small.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "glm-5-3-flash", "b": "inkling-small"}`. From a terminal: `anchor compare glm-5-3-flash inkling-small`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/glm-5-3-flash.json and https://www.anchorterminal.com/api/v1/tools/inkling-small.json

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