# Ling-3.0-tiny vs Qwen3.5-0.8B

> Ling-3.0-tiny scores 55 (C) to Qwen3.5-0.8B's 46.7 (D) 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-qwen3-5-0-8b
- Markdown: https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-0-8b.md (~6,050 tokens)
- Slim: https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-0-8b.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-0-8b.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

Ling-3.0-tiny scores 55 (C) on agent readiness against Qwen3.5-0.8B's 46.7 (D), and leads in 3 of 5 scored categories. Qwen3.5-0.8B leads on risk. 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
- Qwen3.5-0.8B: grade D, 46.7/100, rank #97 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-5-0-8b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-5-0-8b.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:
- Capability, 31 against 17
- Reach, 90 against 80
- Documentation and upkeep, 56 against 37

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

### Qwen3.5-0.8B (D)

Good for: Builders who want a near-free local helper or fine-tuning base for narrow tasks on a phone-class device, laptop or CPU

Ahead on:
- Risk, 55 against 50

Watch for: No quantised build from Alibaba and no inference provider found


## Score by category

| Category | Weight | Ling-3.0-tiny | Qwen3.5-0.8B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 31 | 17 | Ling-3.0-tiny +14 |
| Reach | 25% (25 this run) | 90 | 80 | Ling-3.0-tiny +10 |
| Risk | 20% (20 this run) | 50 | 55 | Qwen3.5-0.8B +5 |
| Agent interface | 10% (10 this run) | 60 | 60 | even |
| Documentation and upkeep | 10% (10 this run) | 56 | 37 | Ling-3.0-tiny +19 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **55 · C** | **46.7 · D** | |

## Facts side by side

| Fact | Ling-3.0-tiny | Qwen3.5-0.8B |
| --- | --- | --- |
| 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 | 2026-03-02 |
| 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.

**Qwen3.5-0.8B.** A tiny vision-language cousin for prototypes, fine-tuning and light local steps such as routing, tagging or reading an image, running in under 2 GB in every major runtime under plain Apache 2.0. Keep thinking off, since the card warns it loops. The main risk is neglect, with no hosted option, safety results or answers from Alibaba since March.

## 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

### Qwen3.5-0.8B

1. Smallest start is `ollama run qwen3.5:0.8b`, or `qwen3.5:0.8b-q8_0` at 1.0 GB
2. Full precision `vllm serve Qwen/Qwen3.5-0.8B --port 8000 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder`
3. Thinking is off by default; if you turn it on with `"chat_template_kwargs": {"enable_thinking": true}`, stream and cut off runaway output, since the card warns of thinking loops, and add `--reasoning-parser qwen3` in vLLM or SGLang to split the reasoning out
4. There's no generation_config.json, so pass the card's sampling yourself, for example `temperature=1.0, top_p=1.0, top_k=20, presence_penalty=2.0` for text without thinking
5. Use it for narrow steps or as a fine-tuning base, not as the main agent, as the card itself suggests
6. Validate any JSON it returns, since structured output isn't documented

## Questions

### Which is better for AI agents, Ling-3.0-tiny or Qwen3.5-0.8B?

Ling-3.0-tiny scores 55 (C) on agent readiness against Qwen3.5-0.8B's 46.7 (D), and leads in 3 of 5 scored categories. Qwen3.5-0.8B leads on risk.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-0-8b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-0-8b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "ling-3-0-tiny", "b": "qwen3-5-0-8b"}`. From a terminal: `anchor compare ling-3-0-tiny qwen3-5-0-8b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/ling-3-0-tiny.json and https://www.anchorterminal.com/api/v1/tools/qwen3-5-0-8b.json

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