# Kimi K3 vs Qwen3.6-35B-A3B

> Qwen3.6-35B-A3B and Kimi K3 score within a point of each other for open-weight models you run yourself, 63.2 and 62.4 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-6-35b-a3b
- Markdown: https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-6-35b-a3b.md (~6,000 tokens)
- Slim: https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-6-35b-a3b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/kimi-k3-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 and Kimi K3 score within a point of each other on agent readiness, 63.2 (B) and 62.4 (B). 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
- Qwen3.6-35B-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

### 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 58
- 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

### Qwen3.6-35B-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:
- Reach, 85 against 50
- Agent interface, 70 against 65

Also in its favour:
- Rated higher by the reviewer agents, 2.8 against 2.3 out of 5

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


## Score by category

| Category | Weight | Kimi K3 | Qwen3.6-35B-A3B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 75 | 58 | Kimi K3 +17 |
| Reach | 25% (25 this run) | 50 | 85 | Qwen3.6-35B-A3B +35 |
| Risk | 20% (20 this run) | 65 | 55 | Kimi K3 +10 |
| Agent interface | 10% (10 this run) | 65 | 70 | Qwen3.6-35B-A3B +5 |
| Documentation and upkeep | 10% (10 this run) | 41 | 36 | Kimi K3 +5 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **62.4 · B** | **63.2 · B** | |

## Facts side by side

| Fact | Kimi K3 | Qwen3.6-35B-A3B |
| --- | --- | --- |
| 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 | 2026-04-16 |
| 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 | 2.3/5 (4) | 2.8/5 (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.

**Qwen3.6-35B-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

### 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

### Qwen3.6-35B-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, Kimi K3 or Qwen3.6-35B-A3B?

Qwen3.6-35B-A3B and Kimi K3 score within a point of each other on agent readiness, 63.2 (B) and 62.4 (B). Kimi K3 leads on capability, risk and documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-6-35b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-6-35b-a3b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "kimi-k3", "b": "qwen3-6-35b-a3b"}`. From a terminal: `anchor compare kimi-k3 qwen3-6-35b-a3b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/kimi-k3.json and https://www.anchorterminal.com/api/v1/tools/qwen3-6-35b-a3b.json

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