# GLM-4.7-Flash vs Qwen3-30B-A3B-Instruct-2507

> GLM-4.7-Flash scores 63 (B) to Qwen3-30B-A3B-Instruct-2507's 56.6 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-30b-a3b-instruct-2507
- Markdown: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-30b-a3b-instruct-2507.md (~6,600 tokens)
- Slim: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-30b-a3b-instruct-2507.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-30b-a3b-instruct-2507.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

GLM-4.7-Flash scores 63 (B) on agent readiness against Qwen3-30B-A3B-Instruct-2507's 56.6 (C), and leads in 3 of 5 scored categories. Qwen3-30B-A3B-Instruct-2507 leads on reach. Both do open-weight models you run yourself.

- GLM-4.7-Flash: grade B, 63/100, rank #26 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/glm-4-7-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/glm-4-7-flash.json
- Qwen3-30B-A3B-Instruct-2507: grade C, 56.6/100, rank #74 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-30b-a3b-instruct-2507.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-30b-a3b-instruct-2507.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-4.7-Flash (B)

Good 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:
- Capability, 59 against 37
- Agent interface, 85 against 75

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

### Qwen3-30B-A3B-Instruct-2507 (C)

Good for: Agents that want a fast, capable instruct model on one 24 GB GPU or a 32 GB Mac, with a plain licence and long context.

Ahead on:
- Reach, 90 against 80

Watch for: No safety results, prompt-injection guidance or security contact


## Score by category

| Category | Weight | GLM-4.7-Flash | Qwen3-30B-A3B-Instruct-2507 | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 59 | 37 | GLM-4.7-Flash +22 |
| Reach | 25% (25 this run) | 80 | 90 | Qwen3-30B-A3B-Instruct-2507 +10 |
| Risk | 20% (20 this run) | 55 | 55 | even |
| Agent interface | 10% (10 this run) | 85 | 75 | GLM-4.7-Flash +10 |
| Documentation and upkeep | 10% (10 this run) | 28 | 26 | GLM-4.7-Flash +2 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **63 · B** | **56.6 · C** | |

## Facts side by side

| Fact | GLM-4.7-Flash | Qwen3-30B-A3B-Instruct-2507 |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Z.ai (Zhipu AI) | Alibaba (Qwen team) |
| 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 | 2026-01-29 | 2025-09-17 |
| 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) | 2.8/5 (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.

**Qwen3-30B-A3B-Instruct-2507.** A quick general-purpose cousin, with 3.3B of 30.5B parameters active per token, for instruction following, tool calling and long documents without a thinking step. It needs about 21 to 23 GB at 4 bits, and several providers host it. The main risk is neglect, with no file changes in over a year, no answers from Alibaba and no safety results.

## 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

### Qwen3-30B-A3B-Instruct-2507

1. Quickest start is `ollama run qwen3:30b-instruct`, 19 GB. Plain `qwen3:30b` and `qwen3:30b-a3b` are the Thinking-2507 edition, not this one
2. On our reference machine use BF16 with `vllm serve Qwen/Qwen3-30B-A3B-Instruct-2507 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser hermes`, 61 GB of weights fits the GPU share
3. Use the hermes parser in vLLM or qwen25 in SGLang, the template emits JSON inside tool_call tags
4. Don't pass enable_thinking, this edition never thinks and the card says the flag isn't needed
5. Sampling per the card is temperature 0.7, top_p 0.8, top_k 20 and min_p 0, with presence_penalty up to 2 against repetition
6. Leave config.json as it is unless you need more than 262,144 tokens, the 1M mode swaps in config_1m.json and runs one sequence at a time
7. Validate JSON yourself or use the runtime's guided decoding, since structured output isn't documented

## Questions

### Which is better for AI agents, GLM-4.7-Flash or Qwen3-30B-A3B-Instruct-2507?

GLM-4.7-Flash scores 63 (B) on agent readiness against Qwen3-30B-A3B-Instruct-2507's 56.6 (C), and leads in 3 of 5 scored categories. Qwen3-30B-A3B-Instruct-2507 leads on reach.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-30b-a3b-instruct-2507.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-30b-a3b-instruct-2507.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "glm-4-7-flash", "b": "qwen3-30b-a3b-instruct-2507"}`. From a terminal: `anchor compare glm-4-7-flash qwen3-30b-a3b-instruct-2507`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/glm-4-7-flash.json and https://www.anchorterminal.com/api/v1/tools/qwen3-30b-a3b-instruct-2507.json

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