# Ling-3.0-tiny vs Qwen3-Coder-30B-A3B-Instruct

> Qwen3-Coder-30B-A3B-Instruct and Ling-3.0-tiny score within a point of each other for open-weight models you run yourself, 55.7 and 55 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-coder-30b-a3b-instruct
- Markdown: https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-coder-30b-a3b-instruct.md (~6,700 tokens)
- Slim: https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-coder-30b-a3b-instruct.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-coder-30b-a3b-instruct.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-Coder-30B-A3B-Instruct and Ling-3.0-tiny score within a point of each other on agent readiness, 55.7 (C) and 55 (C). 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-Coder-30B-A3B-Instruct: grade C, 55.7/100, rank #78 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-coder-30b-a3b-instruct.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-coder-30b-a3b-instruct.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 31

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

### Qwen3-Coder-30B-A3B-Instruct (C)

Good for: Coding agents and local coding tools that want a fast, capable open model on one 24 GB GPU or a 32 GB Mac, with a plain licence.

Ahead on:
- Risk, 55 against 50
- Agent interface, 75 against 60

Watch for: No safety results, prompt-injection guidance or security contact, though it's meant to run commands


## Score by category

| Category | Weight | Ling-3.0-tiny | Qwen3-Coder-30B-A3B-Instruct | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 31 | 33 | Qwen3-Coder-30B-A3B-Instruct +2 |
| Reach | 25% (25 this run) | 90 | 90 | even |
| Risk | 20% (20 this run) | 50 | 55 | Qwen3-Coder-30B-A3B-Instruct +5 |
| Agent interface | 10% (10 this run) | 60 | 75 | Qwen3-Coder-30B-A3B-Instruct +15 |
| Documentation and upkeep | 10% (10 this run) | 56 | 31 | Ling-3.0-tiny +25 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **55 · C** | **55.7 · C** | |

## Facts side by side

| Fact | Ling-3.0-tiny | Qwen3-Coder-30B-A3B-Instruct |
| --- | --- | --- |
| 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 | 2025-12-03 |
| 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-Coder-30B-A3B-Instruct.** A fast coding cousin, with 3.3B of 30.5B parameters active per token, for agentic coding tools, repository reading and fill-in-the-middle without a thinking step. It needs about 21 to 23 GB at 4 bits, and several providers host it. The main risk is that it runs commands for agents with no safety results or guidance, and Alibaba doesn't answer users.

## 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-Coder-30B-A3B-Instruct

1. Quickest start is `ollama run qwen3-coder:30b`, 19 GB
2. On our reference machine use BF16 with `vllm serve Qwen/Qwen3-Coder-30B-A3B-Instruct --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, 61 GB of weights fits the GPU share
3. Use the qwen3_coder parser in vLLM or SGLang, not hermes, since calls come back as function and parameter blocks rather than JSON
4. Pass tool-call arguments in the history as objects, the template iterates them as a mapping
5. Sampling per the card is temperature 0.7, top_p 0.8, top_k 20 and repetition_penalty 1.05, with up to 65,536 output tokens
6. For code completion use the fill-in-the-middle tokens `<|fim_prefix|>`, `<|fim_suffix|>` and `<|fim_middle|>` from Qwen's README
7. Sandbox the commands and files it touches, since nothing from Alibaba covers prompt injection

## Questions

### Which is better for AI agents, Ling-3.0-tiny or Qwen3-Coder-30B-A3B-Instruct?

Qwen3-Coder-30B-A3B-Instruct and Ling-3.0-tiny score within a point of each other on agent readiness, 55.7 (C) and 55 (C). 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-coder-30b-a3b-instruct.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-coder-30b-a3b-instruct.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "ling-3-0-tiny", "b": "qwen3-coder-30b-a3b-instruct"}`. From a terminal: `anchor compare ling-3-0-tiny qwen3-coder-30b-a3b-instruct`
- 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-coder-30b-a3b-instruct.json

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