# Ling-3.0-tiny vs Qwen3.6-27B

> Qwen3.6-27B scores 67.2 (B) to Ling-3.0-tiny's 55 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-6-27b
- Markdown: https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-6-27b.md (~6,100 tokens)
- Slim: https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-6-27b.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/ling-3-0-tiny-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 Ling-3.0-tiny's 55 (C), and leads in 3 of 5 scored categories. Ling-3.0-tiny leads on documentation and upkeep. Both do open-weight models you run yourself.

- Ling-3.0-tiny: grade C, 55/100, rank #81 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/ling-3-0-tiny.md · JSON https://www.anchorterminal.com/api/v1/tools/ling-3-0-tiny.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

### Ling-3.0-tiny (C)

Good for: Agents on a laptop or small GPU that want a fast local model for tool calls and short reasoning, under MIT

Ahead on:
- Documentation and upkeep, 56 against 36

Watch for: No hosted provider today, and Ollama support is only an unmerged pull request

### 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:
- Capability, 66 against 31
- Risk, 55 against 50
- Agent interface, 70 against 60

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


## Score by category

| Category | Weight | Ling-3.0-tiny | Qwen3.6-27B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 31 | 66 | Qwen3.6-27B +35 |
| Reach | 25% (25 this run) | 90 | 90 | even |
| Risk | 20% (20 this run) | 50 | 55 | Qwen3.6-27B +5 |
| Agent interface | 10% (10 this run) | 60 | 70 | Qwen3.6-27B +10 |
| Documentation and upkeep | 10% (10 this run) | 56 | 36 | Ling-3.0-tiny +20 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **55 · C** | **67.2 · B** | |

## Facts side by side

| Fact | Ling-3.0-tiny | Qwen3.6-27B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | inclusionAI (Ant Group) | 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 | 2026-08-31 | 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 | 2.8/5 (4) | 3/5 (4) |

## Verdicts

**Ling-3.0-tiny.** A small, quick hybrid reasoning cousin for local agent work, tool calls and coding, under MIT. It runs in about 6 GB from inclusionAI's 4-bit GGUF in released Transformers, vLLM, SGLang and llama.cpp, with no host. The main risks are a card that doesn't match the files in places, the vLLM route running repository code, and no model safety results.

**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

### Ling-3.0-tiny

1. Start it with `llama-server -hf inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M --jinja -c 32768`, which needs llama.cpp b11772 or a recent release with bailingmoe3
2. With vLLM 0.32.0, `vllm serve inclusionAI/Ling-3.0-tiny --trust-remote-code --enable-prefix-caching --mamba-cache-mode align --enable-auto-tool-choice --tool-call-parser ling3 --reasoning-parser ling3`, which runs the repository's config code
3. With SGLang 0.5.22, its cookbook uses `--reasoning-parser deepseek-r1 --tool-call-parser glm45` for this model, and drop the card's NEXTN flags since tiny has no MTP layer
4. Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false for short tasks
5. Sampling per the card is temperature 1.0, top_p 0.95 and top_k 20
6. The template keeps earlier reasoning in the prompt, which grows long agent sessions
7. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented
8. For a guardrail, inclusionAI's Ling-3.0-tiny-singprobe scores intent, unsafe output and hallucination per token, but only through SGLang and vLLM integration branches

### 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, Ling-3.0-tiny or Qwen3.6-27B?

Qwen3.6-27B scores 67.2 (B) on agent readiness against Ling-3.0-tiny's 55 (C), and leads in 3 of 5 scored categories. Ling-3.0-tiny leads on documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-6-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-6-27b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "ling-3-0-tiny", "b": "qwen3-6-27b"}`. From a terminal: `anchor compare ling-3-0-tiny qwen3-6-27b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/ling-3-0-tiny.json and https://www.anchorterminal.com/api/v1/tools/qwen3-6-27b.json

## Other comparisons with Ling-3.0-tiny or Qwen3.6-27B

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