# Laguna M.1 vs Nanbeige4.3-33B

> Nanbeige4.3-33B scores 61 (C) to Laguna M.1's 55.5 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/laguna-m-1-vs-nanbeige-4-3-33b
- Markdown: https://www.anchorterminal.com/compare/laguna-m-1-vs-nanbeige-4-3-33b.md (~6,450 tokens)
- Slim: https://www.anchorterminal.com/compare/laguna-m-1-vs-nanbeige-4-3-33b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/laguna-m-1-vs-nanbeige-4-3-33b.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: 2026-10-25

Nanbeige4.3-33B scores 61 (C) on agent readiness against Laguna M.1's 55.5 (C), and leads in 2 of 4 scored categories. Laguna M.1 leads on reach. Both do open-weight models you run yourself.

- Laguna M.1: grade C, 55.5/100, rank #85 of 111 open-weight models. Markdown https://www.anchorterminal.com/tools/laguna-m-1.md · JSON https://www.anchorterminal.com/api/v1/tools/laguna-m-1.json
- Nanbeige4.3-33B: grade C, 61/100, rank #56 of 111 open-weight models. Markdown https://www.anchorterminal.com/tools/nanbeige-4-3-33b.md · JSON https://www.anchorterminal.com/api/v1/tools/nanbeige-4-3-33b.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 3341 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### Laguna M.1 (C)

Good for: Teams with a multi-GPU Blackwell or Hopper server that want a large coding agent model under Apache 2.0.

Ahead on:
- Reach, 65 against 55

Watch for: The smallest build needs about 167 GB, more than a 128 GB machine

### Nanbeige4.3-33B (C)

Good for: Agents with about 96 GB of GPU memory, or the 128 GB reference machine, that want an Apache-tagged 33B model for long coding and tool loops and can wait for independent results.

Ahead on:
- Risk, 60 against 55
- Documentation and upkeep, 56 against 36

Watch for: No independent benchmark result yet; every agentic number on the card is Nanbeige's own run


## Score by category

| Category | Weight | Laguna M.1 | Nanbeige4.3-33B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 65 | 55 | Laguna M.1 +10 |
| Risk | 20% (20 this run) | 55 | 60 | Nanbeige4.3-33B +5 |
| Agent interface | 10% (10 this run) | 85 | 85 | even |
| Documentation and upkeep | 10% (10 this run) | 36 | 56 | Nanbeige4.3-33B +20 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **55.5 · C** | **61 · C** | |

## Facts side by side

| Fact | Laguna M.1 | Nanbeige4.3-33B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Poolside | Nanbeige LLM Lab (BOSS Zhipin) |
| 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 (Hub metadata only; the repository has no LICENSE file) |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-07-14 | 2026-10-24 |
| Terms last updated |  | no document linked |
| Privacy policy last updated |  | 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 | 2.5/5 (4) | none |

## Verdicts

**Laguna M.1.** A large coding and agent cousin under plain Apache 2.0, for agents with a multi-GPU server. Its smallest build needs about 167 GB, more than a 128 GB machine, and no local runtime library packages it. The main risks are no upkeep since July, no hosted fallback shown today, no safety results, and repository code on the SGLang route.

**Nanbeige4.3-33B.** A dense 33B Llama-architecture cousin for long agentic coding and office tasks, with per-request thinking, XML tool calls that stock vLLM and SGLang parse, and 512K of context. It needs about 96 GB at BF16, as Nanbeige ships no quantised build. Its agentic results so far are Nanbeige's own, with no safety results, training data notes or licence file.

## Before you call either

### Laguna M.1

1. Run the NVFP4 build on Blackwell GPUs with `vllm serve poolside/Laguna-M.1-NVFP4 --tensor-parallel-size 2 --tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice`, on vLLM 0.21.0 or later
2. Thinking is off in the template unless you pass `enable_thinking` true or start the server with `--default-chat-template-kwargs '{"enable_thinking": true}'`, which Poolside recommends for agentic coding
3. Pass earlier reasoning back as `reasoning_content` in the history, since the card says reasoning may stop in later steps if it's dropped
4. Sampling per the card is temperature 1.0 and top_k 20
5. The FP8 build's Docker image and serve.sh predate upstream support and set a 4,096-token context, so prefer a released vLLM
6. It doesn't fit our 128 GB reference machine at any published precision
7. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented

### Nanbeige4.3-33B

1. Serve with `vllm serve Nanbeige/Nanbeige4.3-33B --enable-auto-tool-choice --reasoning-parser qwen3 --tool-call-parser qwen3_coder`, as the card says, or SGLang with the same two parsers
2. Keep `tool_call_format` at its default `xml`, since qwen3_coder parses that format; the `json` option writes Hermes-style JSON calls, which need a different parser such as vLLM's hermes
3. Pass `chat_template_kwargs` `{"preserve_thinking": true}` for multi-turn tool use and coding, and `{"enable_thinking": false}` to skip reasoning, as the card recommends
4. Use temperature 1.0, top_p 0.95 and up to 65,536 new tokens for agent work, and temperature 0.6 for reasoning and chat, per the card
5. Send your own system prompt, since without one the template injects a Chinese identity line, or a Chinese tool-expert prompt when tools are passed
6. Send one system message at the start only, as the template raises an error for a later one
7. In plain Transformers pass `use_cache=True` to generate, since config.json and generation_config.json set it false; trust_remote_code isn't needed

## Questions

### Which is better for AI agents, Laguna M.1 or Nanbeige4.3-33B?

Nanbeige4.3-33B scores 61 (C) on agent readiness against Laguna M.1's 55.5 (C), and leads in 2 of 4 scored categories. Laguna M.1 leads on reach.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/laguna-m-1-vs-nanbeige-4-3-33b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/laguna-m-1-vs-nanbeige-4-3-33b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "laguna-m-1", "b": "nanbeige-4-3-33b"}`. From a terminal: `anchor compare laguna-m-1 nanbeige-4-3-33b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/laguna-m-1.json and https://www.anchorterminal.com/api/v1/tools/nanbeige-4-3-33b.json

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