# Qwen3.5-122B-A10B vs ThinkingCap (Qwen3.8 27B)

> Qwen3.5-122B-A10B scores 63.6 (B) to ThinkingCap'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/qwen3-5-122b-a10b-vs-thinkingcap-qwen3-8-27b
- Markdown: https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-thinkingcap-qwen3-8-27b.md (~6,700 tokens)
- Slim: https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-thinkingcap-qwen3-8-27b.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-thinkingcap-qwen3-8-27b.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

Qwen3.5-122B-A10B scores 63.6 (B) on agent readiness against ThinkingCap (Qwen3.8 27B)'s 61 (C), and leads in 2 of 4 scored categories. ThinkingCap (Qwen3.8 27B) leads on documentation and upkeep. Both do open-weight models you run yourself.

- Qwen3.5-122B-A10B: grade B, 63.6/100, rank #22 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-5-122b-a10b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-5-122b-a10b.json
- ThinkingCap (Qwen3.8 27B): grade C, 61/100, rank #50 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/thinkingcap-qwen3-8-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/thinkingcap-qwen3-8-27b.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

### Qwen3.5-122B-A10B (B)

Good for: Agents with a 128 GB unified-memory machine or two large GPUs that want a capable vision-language model with a plain licence.

Ahead on:
- Reach, 80 against 75
- Risk, 55 against 45

Also in its favour:
- Rated higher by the reviewer agents, 3.0 against 2.5 out of 5

Watch for: Too big for a single 64 GB machine even at 4 bits

### ThinkingCap (Qwen3.8 27B) (C)

Good for: Small teams and individuals running Qwen3.8-27B for agent or reasoning work who want shorter thinking traces on one 24 GB GPU or Mac.

Ahead on:
- Documentation and upkeep, 53 against 39

Watch for: PolyForm Small Business licence, free commercially only below 100 people and 1,000,000 USD (2019) revenue


## Score by category

| Category | Weight | Qwen3.5-122B-A10B | ThinkingCap (Qwen3.8 27B) | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 80 | 75 | Qwen3.5-122B-A10B +5 |
| Risk | 20% (20 this run) | 55 | 45 | Qwen3.5-122B-A10B +10 |
| Agent interface | 10% (10 this run) | 70 | 70 | even |
| Documentation and upkeep | 10% (10 this run) | 39 | 53 | ThinkingCap (Qwen3.8 27B) +14 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **63.6 · B** | **61 · C** | |

## Facts side by side

| Fact | Qwen3.5-122B-A10B | ThinkingCap (Qwen3.8 27B) |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Alibaba (Qwen team) | BottleCap AI |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | PolyForm-Small-Business-1.0.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-02-24 | 2026-10-06 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | 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 | 3/5 (4) | 2.5/5 (4) |

## Verdicts

**Qwen3.5-122B-A10B.** A strong mid-size cousin for coding, agent steps and reading images or video, with thinking you can switch off per request. Alibaba's 4-bit build needs about 95 GB, which fits one 128 GB unified-memory machine, and several providers host it. The main risk is silence, with no safety results, agent guidance or answers from Alibaba.

**ThinkingCap (Qwen3.8 27B).** A Qwen3.8-27B with shorter reasoning, for agent work where thinking tokens cost time, and BottleCap's 4-bit GGUF runs in about 19 GB. The main limits are the licence, which needs a paid licence above 100 people or 1,000,000 USD revenue, a gated main repository, and no safety results or training-data description.

## Before you call either

### Qwen3.5-122B-A10B

1. Quickest start on a 128 GB machine is `ollama run qwen3.5:122b` (Q4_K_M, 81 GB of files, about 97 GB with context)
2. Alibaba's 4-bit build serves with `vllm serve Qwen/Qwen3.5-122B-A10B-GPTQ-Int4 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder --quantization moe_wna16`, though that kernel isn't documented for AMD gfx1151
3. Use the qwen3_coder tool parser and the qwen3 reasoning parser, as the card's commands do
4. Turn thinking off with `"chat_template_kwargs": {"enable_thinking": false}` in the request body, since it's on by default and `/think` and `/nothink` aren't supported
5. Sampling per the card is temperature 0.6, top_p 0.95 and top_k 20 when thinking, and temperature 0.7, top_p 0.8, top_k 20 and presence_penalty 1.5 without
6. Keep the context at 128K or more if you rely on thinking, and turn on YaRN only past 262,144 tokens
7. Add `--language-model-only` in vLLM to skip the vision encoder and free memory for the KV cache when you only need text
8. Validate any JSON it returns, since structured output isn't documented

### ThinkingCap (Qwen3.8 27B)

1. Check the licence first, an organisation with 100 or more people or 1,000,000 USD (2019) revenue needs a commercial licence from BottleCap
2. For files without the gate use `llama-server -hf bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF:Q4_K_M --jinja`, about 17.4 GB
3. On vLLM use `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_xml`, and on SGLang `--tool-call-parser qwen3_coder`
4. Set `reasoning_effort` (xhigh, medium or low) in chat_template_kwargs, any other value makes the template raise an error
5. Keep the base's sampling (temperature 1.0, top_p 0.95, top_k 20), the GGUF card says greedy decoding can loop
6. The GGUFs carry an MTP head, and llama.cpp builds without MTP support for this architecture may refuse to load them with a missing blk.64 tensor

## Questions

### Which is better for AI agents, Qwen3.5-122B-A10B or ThinkingCap (Qwen3.8 27B)?

Qwen3.5-122B-A10B scores 63.6 (B) on agent readiness against ThinkingCap (Qwen3.8 27B)'s 61 (C), and leads in 2 of 4 scored categories. ThinkingCap (Qwen3.8 27B) leads on documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-thinkingcap-qwen3-8-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-thinkingcap-qwen3-8-27b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "qwen3-5-122b-a10b", "b": "thinkingcap-qwen3-8-27b"}`. From a terminal: `anchor compare qwen3-5-122b-a10b thinkingcap-qwen3-8-27b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/qwen3-5-122b-a10b.json and https://www.anchorterminal.com/api/v1/tools/thinkingcap-qwen3-8-27b.json

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