# Qwen3.8-27B vs Qwen3-Coder-30B-A3B-Instruct

> Qwen3.8-27B scores 68.6 (B) to Qwen3-Coder-30B-A3B-Instruct's 55.7 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-coder-30b-a3b-instruct
- Markdown: https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-coder-30b-a3b-instruct.md (~6,650 tokens)
- Slim: https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-coder-30b-a3b-instruct.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/qwen3-8-27b-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

Qwen3.8-27B scores 68.6 (B) on agent readiness against Qwen3-Coder-30B-A3B-Instruct's 55.7 (C), and leads in 2 of 5 scored categories. Qwen3-Coder-30B-A3B-Instruct leads on agent interface. Both do open-weight models you run yourself.

- Qwen3.8-27B: grade B, 68.6/100, rank #10 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-8-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-8-27b.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

### Qwen3.8-27B (B)

Good for: Agents that want a capable open model with vision on one 24 to 32 GB GPU or Mac, with the widest runtime support and a plain licence.

Ahead on:
- Capability, 70 against 33
- Documentation and upkeep, 36 against 31

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

### 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:
- Agent interface, 75 against 70

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


## Score by category

| Category | Weight | Qwen3.8-27B | Qwen3-Coder-30B-A3B-Instruct | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 70 | 33 | Qwen3.8-27B +37 |
| Reach | 25% (25 this run) | 90 | 90 | even |
| Risk | 20% (20 this run) | 55 | 55 | even |
| Agent interface | 10% (10 this run) | 70 | 75 | Qwen3-Coder-30B-A3B-Instruct +5 |
| Documentation and upkeep | 10% (10 this run) | 36 | 31 | Qwen3.8-27B +5 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **68.6 · B** | **55.7 · C** | |

## Facts side by side

| Fact | Qwen3.8-27B | Qwen3-Coder-30B-A3B-Instruct |
| --- | --- | --- |
| 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 | 2026-08-14 | 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 | 3/5 (4) | 2.8/5 (4) |

## Verdicts

**Qwen3.8-27B.** A dense cousin for coding and agent work with image and video input that runs almost anywhere. Ollama's 4-bit build needs about 22 GB, and five providers host it for agents without the hardware. The main risk is that nobody at Alibaba answers, there are no safety results or agent guidance, and the tool format is only in the template.

**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

### Qwen3.8-27B

1. Quickest start is `ollama run qwen3.8`, about 18 GB of files
2. On our reference machine use BF16 with `vllm serve Qwen/Qwen3.8-27B --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, since FP8 on gfx1151 isn't documented
3. Use the qwen3_coder tool parser, SGLang's cookbook says the Hermes parser leaves its calls unparsed
4. Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false for short tasks, and read the reasoning from `reasoning_content`
5. `reasoning_effort` takes xhigh (default), medium or low, and any other value makes the template raise an error
6. Sampling per the card is temperature 1.0 and top_p 0.95 when thinking, temperature 0.7, top_p 0.8 and presence_penalty 1.5 without
7. Preserved thinking keeps earlier reasoning in the context unless `preserve_thinking` is false, which grows long agent sessions
8. Turn on YaRN only beyond 262,144 tokens, the card warns static YaRN can cost quality on short texts
9. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented

### 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, Qwen3.8-27B or Qwen3-Coder-30B-A3B-Instruct?

Qwen3.8-27B scores 68.6 (B) on agent readiness against Qwen3-Coder-30B-A3B-Instruct's 55.7 (C), and leads in 2 of 5 scored categories. Qwen3-Coder-30B-A3B-Instruct leads on agent interface.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-coder-30b-a3b-instruct.json, and with the fewest tokens: https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-coder-30b-a3b-instruct.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "qwen3-8-27b", "b": "qwen3-coder-30b-a3b-instruct"}`. From a terminal: `anchor compare qwen3-8-27b qwen3-coder-30b-a3b-instruct`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/qwen3-8-27b.json and https://www.anchorterminal.com/api/v1/tools/qwen3-coder-30b-a3b-instruct.json

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