# Kimi K3 vs Qwen3-Coder-Next

> Kimi K3 scores 62.4 (B) to Qwen3-Coder-Next's 57.6 (C) 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-coder-next
- Markdown: https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-coder-next.md (~6,050 tokens)
- Slim: https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-coder-next.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-coder-next.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

Kimi K3 scores 62.4 (B) on agent readiness against Qwen3-Coder-Next's 57.6 (C), and leads in 2 of 5 scored categories. Qwen3-Coder-Next leads on reach and agent interface. 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-Coder-Next: grade C, 57.6/100, rank #70 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-coder-next.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-coder-next.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 42
- Risk, 65 against 50

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

### Qwen3-Coder-Next (C)

Good for: Agents that want a local coding model for long tool-calling loops on a single 64 GB to 128 GB machine.

Ahead on:
- Reach, 85 against 50
- Agent interface, 75 against 65

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

Watch for: No LICENSE file anywhere and a broken licence link on the card


## Score by category

| Category | Weight | Kimi K3 | Qwen3-Coder-Next | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 75 | 42 | Kimi K3 +33 |
| Reach | 25% (25 this run) | 50 | 85 | Qwen3-Coder-Next +35 |
| Risk | 20% (20 this run) | 65 | 50 | Kimi K3 +15 |
| Agent interface | 10% (10 this run) | 65 | 75 | Qwen3-Coder-Next +10 |
| Documentation and upkeep | 10% (10 this run) | 41 | 41 | even |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **62.4 · B** | **57.6 · C** | |

## Facts side by side

| Fact | Kimi K3 | Qwen3-Coder-Next |
| --- | --- | --- |
| 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 | 2026-02-03 |
| 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 | 2.3/5 (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-Coder-Next.** A fast coding cousin for agent harnesses, with 3B active parameters and tool calling trained across many scaffold formats for Claude Code, Cline or Qwen Code loops. Alibaba's 4-bit GGUF needs about 58 GB, fitting one 64 GB machine, and two providers host it. The main risk is neglect, with no change since February, no replies and no safety results.

## 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-Coder-Next

1. Quickest start is `ollama run qwen3-coder-next` (Q4_K_M, 52 GB of files)
2. On our reference machine Alibaba's Q8_0 GGUF (84.8 GB, about 102 GB in use) also fits, run with `llama-server -m Qwen3-Coder-Next-Q8_0/Qwen3-Coder-Next-Q8_0-00001-of-00004.gguf --jinja -ngl 99 -fa on -c 131072`
3. Serve with `vllm serve Qwen/Qwen3-Coder-Next --enable-auto-tool-choice --tool-call-parser qwen3_coder` (vLLM 0.15.0 or later), using the built-in parser rather than the repository's qwen3coder_tool_parser_vllm.py, which fails to import on newer vLLM per discussion 36
4. The card's commands use tensor parallel 2 though the text says 4 GPUs, and it suggests cutting the context to 32,768 if the server fails to start
5. There's no thinking mode, so don't pass `enable_thinking` or wait for reasoning_content
6. Sampling per the card is temperature 1.0, top_p 0.95 and top_k 40
7. The GGUF card's llama.cpp example path omits the quant from the file name; the real files are named like Qwen3-Coder-Next-Q4_K_M-00001-of-00004.gguf
8. Validate any JSON it returns, since structured output isn't documented

## Questions

### Which is better for AI agents, Kimi K3 or Qwen3-Coder-Next?

Kimi K3 scores 62.4 (B) on agent readiness against Qwen3-Coder-Next's 57.6 (C), and leads in 2 of 5 scored categories. Qwen3-Coder-Next leads on reach and agent interface.


## For agents

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

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