# Kimi K3 vs Qwen3.8-27B

> Qwen3.8-27B scores 68.6 (B) to Kimi K3's 62.4 (B) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-8-27b
- Markdown: https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-8-27b.md (~5,850 tokens)
- Slim: https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-8-27b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-8-27b.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

Qwen3.8-27B scores 68.6 (B) on agent readiness against Kimi K3's 62.4 (B), and leads in 2 of 5 scored categories. Kimi K3 leads on capability, risk and documentation and upkeep. Both do open-weight models you run yourself.

- Kimi K3: grade B, 62.4/100, rank #29 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/kimi-k3.md · JSON https://www.anchorterminal.com/api/v1/tools/kimi-k3.json
- 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
- 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

### Kimi K3 (B)

Good for: Teams with a multi-node GPU cluster who want an open 1M-context agent model for long coding and research work

Ahead on:
- Capability, 75 against 70
- Risk, 65 against 55
- Documentation and upkeep, 41 against 36

Watch for: 1,561 GB of files at its smallest publisher build, far beyond any single machine

### 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:
- Reach, 90 against 50
- Agent interface, 70 against 65

Also in its favour:
- Rated higher by the reviewer agents, 3.0 against 2.3 out of 5

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


## Score by category

| Category | Weight | Kimi K3 | Qwen3.8-27B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 75 | 70 | Kimi K3 +5 |
| Reach | 25% (25 this run) | 50 | 90 | Qwen3.8-27B +40 |
| Risk | 20% (20 this run) | 65 | 55 | Kimi K3 +10 |
| Agent interface | 10% (10 this run) | 65 | 70 | Qwen3.8-27B +5 |
| Documentation and upkeep | 10% (10 this run) | 41 | 36 | Kimi K3 +5 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **62.4 · B** | **68.6 · B** | |

## Facts side by side

| Fact | Kimi K3 | Qwen3.8-27B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Moonshot AI | Alibaba (Qwen team) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Kimi K3 License (modified MIT) | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-09-02 | 2026-08-14 |
| 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 | 2.3/5 (4) | 3/5 (4) |

## Verdicts

**Kimi K3.** Kimi K3 is a 2.8T multimodal agent model with a 1M-token context, parallel tool calls and per-request effort, for long coding and research sessions. Its 1,561 GB of files need a multi-node GPU cluster, so most agents will call a hosted copy. The main risks are Moonshot's Python running at load and prompt time, and no answers in its repository.

**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.

## Before you call either

### Kimi K3

1. Start it with SGLang on a node of 8 B300s, sglang serve --trust-remote-code --model-path moonshotai/Kimi-K3 --tp-size 8 --context-length 65536 --mem-fraction-static 0.82 --reasoning-parser kimi_k3 --tool-call-parser kimi_k3 --port 30000
2. Use the kimi_k3 tool-call and reasoning parsers in vLLM or SGLang, and validate tool-call arguments against the schema, since vLLM's recipe says K3 sometimes emits a format its parser doesn't expect
3. Pass the whole assistant message back every turn, reasoning_content and tool_calls included, or output becomes unstable
4. Thinking is always on; set reasoning_effort to low or high when max is too slow
5. Sampling per the card is temperature 1.0, with top-p 0.95 for single-step tasks and 1.0 for agentic ones
6. Don't hand an ongoing session from another model to K3, and state boundaries in the system prompt, since Moonshot warns it can act on its own when intent is unclear
7. Without the hardware, call it through Moonshot's API or an inference provider listed on its Hugging Face page

### 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

## Questions

### Which is better for AI agents, Kimi K3 or Qwen3.8-27B?

Qwen3.8-27B scores 68.6 (B) on agent readiness against Kimi K3's 62.4 (B), and leads in 2 of 5 scored categories. Kimi K3 leads on capability, risk and documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-8-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-8-27b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "kimi-k3", "b": "qwen3-8-27b"}`. From a terminal: `anchor compare kimi-k3 qwen3-8-27b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/kimi-k3.json and https://www.anchorterminal.com/api/v1/tools/qwen3-8-27b.json

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