u/hmmmmm_nl1

Ive got an old Emco PC Mill 125 sitting in my workshop, trying to figure this thing out but failing already 😄 The old Windows XP pc was fubarred but i got that working, so i have the original WinNC software. The machine is running on a Siemens Numerik 840D controller. Ive got movement on the axis and worked through some of the errors (air pressure, door switch etc)

https://preview.redd.it/wkv97oum9u9h1.png?width=2410&format=png&auto=webp&s=00fdf92232657d3659beb30b86bb858b8019a4a6

https://preview.redd.it/uz3hoi2q9u9h1.png?width=2410&format=png&auto=webp&s=ea3bfb02899e8e7cce67661fe2ac7c6d053b2229

Ive got power and air hooked up, but i cant for the life of me figure out how the toolchanger umbrella should work. Its giving me the 7021 error, saying its 'not free' in the software, found the english manual which states:

https://preview.redd.it/uhhbggx7au9h1.png?width=1954&format=png&auto=webp&s=82f3b45e60e18d58bd559d5ad57e0063dd3e0aae

According to ai:
'Understanding the "Mechanical Clutch"

On the EMCO PC Mill 125, the changer is often mounted on a roller in a curved path or on a carrier attached to the Z-axis (the spindle head).

• The Rule: The switch can only slide horizontally backward if the Z-axis is at the exact switch height .
• The Problem: If the Z-axis is a fraction too high or too low, the changer "pin" gets stuck in the spindle holder. You won't be able to get it moving even with ten hammers.

1. Find the "Free Point": * Set the machine to JOG .
   • Move the Z-axis up and down very slowly (with the override at 2% or 5%).
   • While doing this, try to constantly apply light backward pressure on the carousel with your other hand.
   • At a certain point, you feel it becoming "free" from the spindle. That is the point at which it can move backward.
2. The Air Trick: * Once you have found that free point, the air pressure (if there is any on it) should actually already be shooting it backwards.
   • Do you hear a hissing sound? Then the air is trying, but the resistance is too great."

Ive been playing around with this but i cant get the umbrella in the right position. Although im not sure this is actually the issue, if there is air pressure on that umbrella, can it even be pushed back by hand?

Does anyone have any experience with these machine and can help me get a proper startup procedure so i can at least test the basic functions?

Some picture of the umbrella changer and controller:

https://preview.redd.it/oeaj1jidau9h1.png?width=1164&format=png&auto=webp&s=ac63b7c459ca3a2edc4aa91157ce2613d8388321

https://preview.redd.it/qh2yi1lgau9h1.png?width=1356&format=png&auto=webp&s=d5687c99bf748601a077e4125a811569cdc03270

https://preview.redd.it/kelzzf4fbu9h1.png?width=1390&format=png&auto=webp&s=a78410c66e07e189e7a97ae508acb95bb234d42e

I've been building a research pipeline (Python/Streamlit + LangGraph + LanceDB) and wanted to pick the right model for sub-agent coding and research tasks. So I ran a head-to-head benchmark across 6 models, 2 modes (thinking on/off), and 6 tasks ranging from trivial speed tests to architecture reasoning. The benchmark includes an auto-verified coding task (6 hidden test cases) so this isn't just about vibes — correctness is checked.

Tested in the latest Opencode (used inside vscode on macos using the official extension). This is just benchmarked for my personal use/easy tasks, not tackling big refactors. I just wanted to see speed and quality, and compare GLM and Deepseek. GLM doesnt allow high concurrent agents, and deepseek is cheap, has vision, and endless concurrency over api. Might be interesting to others, you can clearly see speed from 5.2, 5.1 turbo etc, with intereseting results;

-5.2 is getting very close in non-thinking tasks speed to the turbo variant

-In thinking mode 5.2 is actually faster then turbo.. and they are both on x3 usage if im not mistaken, so turbo is now useless?

-Deepseek is veeeery fast, the sub second first token is fun, as is 400ts.

## The Models

| Provider | Model | Notes |

|---|---|---|

| DeepSeek | `deepseek-v4-pro` | Flagship |

| DeepSeek | `deepseek-v4-flash` | Fast/cheap tier |

| Zhipu (GLM) | `glm-5.2` | Newest GLM |

| Zhipu (GLM) | `glm-5-turbo` | Speed-optimized |

| Zhipu (GLM) | `glm-4.7` | Previous gen |

| Zhipu (GLM) | `glm-4.5-air` | Lightweight tier |

## The 6 Tasks

1. **Walrus operator explainer** — pure speed test, short output

2. **`parse_timestamp()` function** — *auto-verified* against 6 hidden test cases (ISO 8601, Unix epoch, relative time, error handling)

3. **Streamlit asset table** — real pattern from my codebase (st.dataframe + column_config)

4. **Race condition bug hunt** — reasoning test (find the bug in an asyncio class)

5. **LangGraph transcription node** — real pattern from my codebase

6. **JSONB vs metadata table** — architecture reasoning

## 🏆 Headline Results (averaged across all 6 tasks)

https://preview.redd.it/73bgdaw1z47h1.png?width=1024&format=png&auto=webp&s=cc40b3e5483db6fef5507b4232bd622c58cfb857

## 📊 Per-Task Breakdown

### Task 1 — Walrus operator (speed test, short output)

| Model | Mode | TTFT | Total | Tokens/s |

|---|---|---|---|---|

| deepseek-v4-pro | non-thinking | 0.31s | **2.69s** | 350.8 |

| deepseek-v4-flash | non-thinking | 0.75s | 3.37s | 220.8 |

| glm-5-turbo | non-thinking | 2.65s | 5.94s | 216.5 |

| glm-4.7 | non-thinking | 5.28s | 5.28s | 182.6 |

| glm-4.5-air | non-thinking | 3.79s | 5.54s | 155.6 |

| glm-5.2 | non-thinking | 4.69s | 8.37s | 154.1 |

| deepseek-v4-flash | thinking | 0.54s | 3.59s | 279.4 |

| deepseek-v4-pro | thinking | 0.31s | 4.97s | 239.3 |

| glm-4.5-air | thinking | 3.19s | 7.91s | **158.9** |

| glm-5-turbo | thinking | 1.78s | 11.65s | 88.0 |

| glm-5.2 | thinking | 4.25s | 11.73s | 86.6 |

| glm-4.7 | thinking | 6.34s | 16.23s | 56.8 |

### Task 2 — `parse_timestamp()` (auto-verified, 6 hidden tests)

| Model | Mode | TTFT | Total | Tokens/s | Verify |

|---|---|---|---|---|---|

| deepseek-v4-pro | non-thinking | 0.31s | **5.58s** | 492.0 | ✅ 6/6 |

| deepseek-v4-flash | non-thinking | 0.61s | 8.48s | 373.6 | ✅ 6/6 |

| glm-5-turbo | non-thinking | 1.96s | 6.62s | 325.7 | ✅ 6/6 |

| glm-5.2 | non-thinking | 3.81s | 8.17s | 257.6 | ✅ 6/6 |

| glm-4.7 | non-thinking | 9.40s | 10.97s | 189.7 | ✅ 6/6 |

| glm-4.5-air | non-thinking | 3.37s | 9.91s | 178.3 | ✅ 6/6 |

| deepseek-v4-flash | thinking | 0.29s | 8.71s | 292.4 | ✅ 6/6 |

| glm-5.2 | thinking | 5.69s | 33.95s | 62.6 | ✅ 6/6 |

| glm-5-turbo | thinking | 2.83s | 76.43s | 27.8 | ✅ 6/6 |

| deepseek-v4-pro | thinking | 0.39s | 21.91s | 83.1 | ✅ 6/6 |

| glm-4.7 | thinking | 9.79s | 107.30s | 25.5 | ✅ 6/6 |

| glm-4.5-air | thinking | 2.20s | 122.20s | — | ❌ TIMEOUT |

### Task 3 — Streamlit asset table (codebase pattern)

| Model | Mode | TTFT | Total | Tokens/s |

|---|---|---|---|---|

| deepseek-v4-pro | non-thinking | 0.33s | **5.59s** | 593.3 |

| deepseek-v4-flash | non-thinking | 0.38s | 5.08s | 481.1 |

| deepseek-v4-flash | thinking | 0.30s | 6.82s | 292.1 |

| deepseek-v4-pro | thinking | 0.30s | 15.27s | 154.4 |

| glm-5-turbo | non-thinking | 3.29s | 8.50s | 340.4 |

| glm-5.2 | non-thinking | 3.28s | 9.10s | 284.1 |

| glm-4.7 | non-thinking | 7.18s | 7.31s | 279.4 |

| glm-4.5-air | non-thinking | 4.40s | 15.61s | 228.2 |

| glm-4.5-air | thinking | 2.05s | 11.13s | **190.8** |

| glm-5-turbo | thinking | 2.57s | 18.70s | 109.8 |

| glm-5.2 | thinking | 2.89s | 19.50s | 163.6 |

| glm-4.7 | thinking | 6.39s | 25.41s | 104.6 |

### Task 4 — Race condition bug hunt (reasoning)

| Model | Mode | TTFT | Total | Tokens/s |

|---|---|---|---|---|

| deepseek-v4-pro | non-thinking | 0.37s | **4.67s** | 437.6 |

| deepseek-v4-flash | non-thinking | 0.46s | 5.49s | 376.9 |

| glm-5-turbo | non-thinking | 2.44s | 11.30s | 342.1 |

| glm-4.7 | non-thinking | 8.30s | 11.47s | 267.5 |

| glm-5.2 | non-thinking | 3.97s | 12.30s | 263.3 |

| glm-4.5-air | non-thinking | 3.12s | 27.67s | 252.8 |

| glm-5-turbo | thinking | 2.52s | 23.51s | 110.6 |

| glm-5.2 | thinking | 2.61s | 27.88s | 101.0 |

| glm-4.5-air | thinking | 2.68s | 38.57s | 64.4 |

| deepseek-v4-flash | thinking | 0.36s | 18.09s | 148.7 |

| deepseek-v4-pro | thinking | 0.32s | 18.91s | 113.9 |

| glm-4.7 | thinking | 9.14s | 98.46s | 30.2 |

### Task 5 — LangGraph transcription node (codebase pattern)

| Model | Mode | TTFT | Total | Tokens/s |

|---|---|---|---|---|

| deepseek-v4-flash | non-thinking | 0.48s | **4.56s** | 508.4 |

| deepseek-v4-pro | non-thinking | 0.31s | 5.67s | 557.7 |

| glm-5-turbo | non-thinking | 2.01s | 4.91s | 338.9 |

| glm-4.5-air | non-thinking | 2.92s | 5.34s | 277.3 |

| glm-4.7 | non-thinking | 7.04s | 9.27s | 280.4 |

| glm-5.2 | non-thinking | 2.90s | 8.28s | 294.2 |

| deepseek-v4-flash | thinking | 0.31s | 13.29s | 151.6 |

| deepseek-v4-pro | thinking | 0.31s | 12.02s | 145.2 |

| glm-5.2 | thinking | 3.35s | 23.75s | 98.8 |

| glm-5-turbo | thinking | 3.04s | 35.13s | 62.5 |

| glm-4.7 | thinking | 9.09s | 41.70s | 59.9 |

| glm-4.5-air | thinking | 2.47s | 89.86s | 39.4 |

### Task 6 — JSONB vs metadata table (architecture reasoning)

| Model | Mode | TTFT | Total | Tokens/s |

|---|---|---|---|---|

| deepseek-v4-pro | non-thinking | 0.30s | **6.88s** | 361.8 |

| deepseek-v4-flash | non-thinking | 0.32s | 8.11s | 336.2 |

| glm-5-turbo | non-thinking | 2.04s | 13.09s | 283.9 |

| glm-4.5-air | non-thinking | 3.29s | 10.50s | 236.9 |

| glm-4.7 | non-thinking | 9.90s | 14.82s | 219.1 |

| glm-5.2 | non-thinking | 3.98s | 15.78s | 216.0 |

| deepseek-v4-flash | thinking | 0.31s | 13.95s | 271.4 |

| deepseek-v4-pro | thinking | 0.39s | 17.33s | 207.7 |

| glm-4.5-air | thinking | 2.43s | 45.67s | 87.7 |

| glm-5-turbo | thinking | 2.31s | 26.22s | **144.7** |

| glm-5.2 | thinking | 3.90s | 30.73s | 112.2 |

| glm-4.7 | thinking | 7.33s | 38.52s | 98.5 |