# Darwin-36B-Opus vs GLM-4.7-Flash

> GLM-4.7-Flash scores 63 (B) to Darwin-36B-Opus's 61 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-4-7-flash
- Markdown: https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-4-7-flash.md (~6,100 tokens)
- Slim: https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-4-7-flash.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-4-7-flash.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 Darwin-36B-Opus's 61 (C), and leads in 1 of 4 scored categories. Darwin-36B-Opus leads on documentation and upkeep. Both do open-weight models you run yourself.

- Darwin-36B-Opus: grade C, 61/100, rank #37 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/darwin-36b-opus.md · JSON https://www.anchorterminal.com/api/v1/tools/darwin-36b-opus.json
- 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
- 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

### Darwin-36B-Opus (C)

Good for: Reasoning-heavy text tasks on a 32 GB machine or a single 80 GB GPU, where a disclosed training method isn't required

Ahead on:
- Documentation and upkeep, 51 against 28

Watch for: The merge method is proprietary and the current weights include an undescribed targeted refinement

### 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:
- 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


## Score by category

| Category | Weight | Darwin-36B-Opus | GLM-4.7-Flash | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 80 | 80 | even |
| Risk | 20% (20 this run) | 55 | 55 | even |
| Agent interface | 10% (10 this run) | 70 | 85 | GLM-4.7-Flash +15 |
| Documentation and upkeep | 10% (10 this run) | 51 | 28 | Darwin-36B-Opus +23 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **61 · C** | **63 · B** | |

## Facts side by side

| Fact | Darwin-36B-Opus | GLM-4.7-Flash |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | FINAL-Bench (VIDRAFT) | Z.ai (Zhipu AI) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | MIT |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-04-23 | 2026-01-29 |
| 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 | 2.5/5 (4) | 2.8/5 (4) |

## Verdicts

**Darwin-36B-Opus.** A text-only reasoning cousin merged from Qwen3.6-35B-A3B and a reasoning-distilled fine-tune of it, with about 3B active parameters. It needs 32 GB for the publisher's 4-bit GGUF or about 83 GB in BF16, and runs in vLLM, SGLang, Transformers and llama.cpp. The main risks are an undisclosed merge method, a card that disagrees with its files, and no safety results.

**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.

## Before you call either

### Darwin-36B-Opus

1. Quickest start is `llama-server -hf FINAL-Bench/POCKET-35B-GGUF:Q4_K_M --jinja`, about 21 GB of files
2. On the reference machine BF16 fits, `vllm serve FINAL-Bench/Darwin-36B-Opus --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, the parsers for Qwen3.6's format
3. Thinking is on by default, send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention
4. The card suggests temperature 0.6 to 0.7 and at least 5,120 new tokens for its think traces
5. generation_config.json stops only on the end-of-text token, so add `<|im_end|>` as a stop token if a runtime keeps generating after the turn ends
6. Text only, the vision encoder of Qwen3.6-35B-A3B isn't in these files
7. Validate JSON output yourself, since structured output isn't documented

### 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

## Questions

### Which is better for AI agents, Darwin-36B-Opus or GLM-4.7-Flash?

GLM-4.7-Flash scores 63 (B) on agent readiness against Darwin-36B-Opus's 61 (C), and leads in 1 of 4 scored categories. Darwin-36B-Opus leads on documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-4-7-flash.json, and with the fewest tokens: https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-4-7-flash.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "darwin-36b-opus", "b": "glm-4-7-flash"}`. From a terminal: `anchor compare darwin-36b-opus glm-4-7-flash`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/darwin-36b-opus.json and https://www.anchorterminal.com/api/v1/tools/glm-4-7-flash.json

## Other comparisons with Darwin-36B-Opus or GLM-4.7-Flash

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