# Ling-3.0-tiny vs Qwen3.6-35B-A3B

> Qwen3.6-35B-A3B scores 63.2 (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-qwen3-6-35b-a3b
- Markdown: https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-6-35b-a3b.md (~6,250 tokens)
- Slim: https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-6-35b-a3b.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-6-35b-a3b.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

Qwen3.6-35B-A3B scores 63.2 (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 reach and 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
- Qwen3.6-35B-A3B: grade B, 63.2/100, rank #25 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-6-35b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-6-35b-a3b.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:
- Reach, 90 against 85
- Documentation and upkeep, 56 against 36

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

### Qwen3.6-35B-A3B (B)

Good for: Agents with a 32 GB or larger machine that want a quick, permissively licensed coding and tool-calling cousin with vision.

Ahead on:
- Capability, 58 against 31
- Risk, 55 against 50
- Agent interface, 70 against 60

Watch for: No safety evaluation, no agent-use guidance and no way to report a security flaw


## Score by category

| Category | Weight | Ling-3.0-tiny | Qwen3.6-35B-A3B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 31 | 58 | Qwen3.6-35B-A3B +27 |
| Reach | 25% (25 this run) | 90 | 85 | Ling-3.0-tiny +5 |
| Risk | 20% (20 this run) | 50 | 55 | Qwen3.6-35B-A3B +5 |
| Agent interface | 10% (10 this run) | 60 | 70 | Qwen3.6-35B-A3B +10 |
| Documentation and upkeep | 10% (10 this run) | 56 | 36 | Ling-3.0-tiny +20 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **55 · C** | **63.2 · B** | |

## Facts side by side

| Fact | Ling-3.0-tiny | Qwen3.6-35B-A3B |
| --- | --- | --- |
| 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 | 2026-04-16 |
| 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) | 2.8/5 (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.

**Qwen3.6-35B-A3B.** A fast mixture-of-experts cousin with 3B active parameters for coding and tool calling, with image and video input, in every major runtime under plain Apache 2.0. Ollama's 4-bit build needs about 28 GB. The main risk is that nobody at Alibaba maintains it, with no safety results, no security contact and no replies to tool-calling reports.

## 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

### Qwen3.6-35B-A3B

1. Start it with `ollama run qwen3.6:35b` (Q4_K_M, 23 to 24 GB on disk, about 28 GB in memory)
2. On the reference machine use `vllm serve Qwen/Qwen3.6-35B-A3B --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, BF16, since vLLM doesn't document FP8 compute on gfx1151
3. Use the qwen3_coder tool-call parser and the qwen3 reasoning parser in vLLM 0.19.0 or SGLang 0.5.10 or later
4. Thinking is on by default and /nothink isn't supported, so pass enable_thinking as false in chat_template_kwargs for short tasks, or preserve_thinking as true to keep earlier reasoning
5. Card sampling for thinking is temperature 1.0, top_p 0.95, top_k 20, presence_penalty 1.5, and for non-thinking temperature 0.7, top_p 0.8
6. Keep at least 128K of context as the card advises, and turn on YaRN only beyond 262,144 tokens
7. Validate JSON output yourself or use the runtime's guided decoding, since the model card documents none

## Questions

### Which is better for AI agents, Ling-3.0-tiny or Qwen3.6-35B-A3B?

Qwen3.6-35B-A3B scores 63.2 (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 reach and documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-6-35b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-6-35b-a3b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "ling-3-0-tiny", "b": "qwen3-6-35b-a3b"}`. From a terminal: `anchor compare ling-3-0-tiny qwen3-6-35b-a3b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/ling-3-0-tiny.json and https://www.anchorterminal.com/api/v1/tools/qwen3-6-35b-a3b.json

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