# Qwen3.5-122B-A10B vs Qwen3.6-27B

> Qwen3.6-27B scores 67.2 (B) to Qwen3.5-122B-A10B's 63.6 (B) 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-qwen3-6-27b
- Markdown: https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-6-27b.md (~6,200 tokens)
- Slim: https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-6-27b.min.md (~380 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-6-27b.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

Qwen3.6-27B scores 67.2 (B) on agent readiness against Qwen3.5-122B-A10B's 63.6 (B), and leads in 2 of 5 scored categories. 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
- Qwen3.6-27B: grade B, 67.2/100, rank #11 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-6-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-6-27b.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.

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

### Qwen3.6-27B (B)

Good for: Agents that want an open coding and tool-calling model with vision on one 24 to 32 GB GPU or Mac, with plain licence terms.

Ahead on:
- Reach, 90 against 80

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


## Score by category

| Category | Weight | Qwen3.5-122B-A10B | Qwen3.6-27B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 62 | 66 | Qwen3.6-27B +4 |
| Reach | 25% (25 this run) | 80 | 90 | Qwen3.6-27B +10 |
| Risk | 20% (20 this run) | 55 | 55 | even |
| Agent interface | 10% (10 this run) | 70 | 70 | even |
| Documentation and upkeep | 10% (10 this run) | 39 | 36 | Qwen3.5-122B-A10B +3 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **63.6 · B** | **67.2 · B** | |

## Facts side by side

| Fact | Qwen3.5-122B-A10B | Qwen3.6-27B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Alibaba (Qwen team) | Alibaba (Qwen team) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-02-24 | 2026-04-22 |
| 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 | 3/5 (4) | 3/5 (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.

**Qwen3.6-27B.** A capable dense cousin for agentic coding and tool calls with images and video in, running in every major runtime. Ollama's 4-bit build needs about 22 GB, and three providers host it. The main risk is silence, with no safety results, no agent guidance, no answers in its repository and Qwen3.8-27B now ahead of it.

## 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

### Qwen3.6-27B

1. Quickest start is `ollama run qwen3.6:27b`, about 18 GB of files with the vision projector
2. Alibaba's FP8 build with the card's parsers is `vllm serve Qwen/Qwen3.6-27B-FP8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, vllm 0.19.0 or later
3. Use the qwen3_coder tool parser, the template writes calls as function and parameter blocks inside tool_call
4. Thinking is on by default, pass `"chat_template_kwargs": {"enable_thinking": false}` for short tasks, and `/think` and `/nothink` aren't supported
5. `"preserve_thinking": true` in chat_template_kwargs keeps earlier reasoning in context, which helps long agent sessions but grows the prompt
6. Sampling per the card is temperature 1.0, top_p 0.95, top_k 20 when thinking, and temperature 0.7, top_p 0.8, presence_penalty 1.5 without
7. Keep at least 128K of context if you rely on thinking, and turn on YaRN only beyond 262,144 tokens
8. Validate any JSON it returns or use the runtime's guided decoding, since structured output isn't documented
9. Qwen3.8-27B (listed as qwen3-8-27b) is the newer model at this size on the same runtimes

## Questions

### Which is better for AI agents, Qwen3.5-122B-A10B or Qwen3.6-27B?

Qwen3.6-27B scores 67.2 (B) on agent readiness against Qwen3.5-122B-A10B's 63.6 (B), and leads in 2 of 5 scored categories.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-6-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-6-27b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "qwen3-5-122b-a10b", "b": "qwen3-6-27b"}`. From a terminal: `anchor compare qwen3-5-122b-a10b qwen3-6-27b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/qwen3-5-122b-a10b.json and https://www.anchorterminal.com/api/v1/tools/qwen3-6-27b.json

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- [Ornith-1.5-397B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-6-27b.md)
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- [Qwen3-30B-A3B-Instruct-2507 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-6-27b.md)
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- [Qwen3-4B-Instruct-2507 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-6-27b.md)
- [Qwen3.5-0.8B vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-5-122b-a10b.md)
- [Qwen3.5-0.8B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-6-27b.md)
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- [Qwen3.5-122B-A10B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-5-397b-a17b.md)
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- [Qwen3.5-122B-A10B vs Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-8-27b.md)
- [Qwen3.5-122B-A10B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-8-flash-next.md)
- [Qwen3.5-122B-A10B vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-coder-30b-a3b-instruct.md)
- [Qwen3.5-122B-A10B vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-coder-next.md)
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- [Qwen3.5-122B-A10B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-vl-2b-instruct.md)
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- [Qwen3.5-122B-A10B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-vl-32b-instruct.md)
- [Qwen3.5-122B-A10B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-vl-4b-instruct.md)
- [Qwen3.5-122B-A10B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-vl-8b-instruct.md)
- [Qwen3.5-122B-A10B vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwopus3-8-27b-flash-v2.md)
- [Qwen3.5-122B-A10B vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-smollm3-3b.md)
- [Qwen3.5-122B-A10B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-spark-x2-6-4b.md)
- [Qwen3.5-122B-A10B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-swift-1-5-qwen3-8-27b.md)
- [Qwen3.5-122B-A10B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-swift-1-5-qwen3-8-flash-next.md)
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- [Qwen3.5-4B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-6-27b.md)
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- [Qwen3.6-27B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-8-flash-next.md)
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- [Qwen3.6-27B vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-coder-next.md)
- [Qwen3.6-27B vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-omni-30b-a3b-instruct.md)
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- [Qwen3.6-27B vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-vl-30b-a3b-instruct.md)
- [Qwen3.6-27B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-vl-32b-instruct.md)
- [Qwen3.6-27B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-vl-4b-instruct.md)
- [Qwen3.6-27B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-vl-8b-instruct.md)
- [Qwen3.6-27B vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwopus3-8-27b-flash-v2.md)
- [Qwen3.6-27B vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-smollm3-3b.md)
- [Qwen3.6-27B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-spark-x2-6-4b.md)
- [Qwen3.6-27B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-swift-1-5-qwen3-8-27b.md)
- [Qwen3.6-27B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-swift-1-5-qwen3-8-flash-next.md)
- [Qwen3.6-27B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-thinkingcap-qwen3-8-27b.md)

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