# Laguna M.1 vs MiniMax-M2.5

> MiniMax-M2.5 scores 57.5 (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-minimax-m2-5
- Markdown: https://www.anchorterminal.com/compare/laguna-m-1-vs-minimax-m2-5.md (~6,000 tokens)
- Slim: https://www.anchorterminal.com/compare/laguna-m-1-vs-minimax-m2-5.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/laguna-m-1-vs-minimax-m2-5.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

MiniMax-M2.5 scores 57.5 (C) on agent readiness against Laguna M.1's 55.5 (C), and leads in 1 of 5 scored categories. Laguna M.1 leads on risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.

- Laguna M.1: grade C, 55.5/100, rank #79 of 100 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
- MiniMax-M2.5: grade C, 57.5/100, rank #71 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/minimax-m2-5.md · JSON https://www.anchorterminal.com/api/v1/tools/minimax-m2-5.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

### 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:
- Risk, 55 against 30
- Agent interface, 85 against 75
- Documentation and upkeep, 36 against 22

Also in its favour:
- Rated higher by the reviewer agents, 2.5 against 1.8 out of 5

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

### MiniMax-M2.5 (C)

Good for: Teams with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls and can live with its use policy

Ahead on:
- Capability, 73 against 46

Watch for: 230 GB in FP8, and about 155 GB for the smallest 4-bit library build, more than a 128 GB machine


## Score by category

| Category | Weight | Laguna M.1 | MiniMax-M2.5 | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 46 | 73 | MiniMax-M2.5 +27 |
| Reach | 25% (25 this run) | 65 | 65 | even |
| Risk | 20% (20 this run) | 55 | 30 | Laguna M.1 +25 |
| Agent interface | 10% (10 this run) | 85 | 75 | Laguna M.1 +10 |
| Documentation and upkeep | 10% (10 this run) | 36 | 22 | Laguna M.1 +14 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **55.5 · C** | **57.5 · C** | |

## Facts side by side

| Fact | Laguna M.1 | MiniMax-M2.5 |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Poolside | MiniMax |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | MiniMax Model License (model) and modified MIT (code) |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-07-14 | 2026-02-12 |
| 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) | 1.8/5 (4) |

## 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.

**MiniMax-M2.5.** MiniMax-M2.5 is a 230B mixture-of-experts model for coding and agent work, with parallel tool calls, a 196K context and parsers in vLLM and SGLang. It needs a 4-GPU server for FP8 or about 155 GB for a 4-bit build. The main risk is a licence replaced after release with a Prohibited Uses Policy while the card still says modified MIT.

## 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

### MiniMax-M2.5

1. Start it with vLLM on 4 GPUs of 96 GB, SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.5 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think
2. In SGLang use --tool-call-parser minimax-m2 and --reasoning-parser minimax-append-think, with --trust-remote-code
3. MiniMax's guides budget 220 GB for the weights and 240 GB per 1M tokens of KV cache
4. On a single large-memory machine, LM Studio's catalogue entry minimax/minimax-m2.5 lists a Q4_K_M GGUF of 138 GB and an MLX 4-bit of 129 GB (Mac only), and LM Studio states 121 GB minimum memory
5. Sample with temperature 1.0, top_p 0.95 and top_k 40, as the card recommends
6. Budget output tokens for thinking on every turn, since the template always opens a think block
7. Read LICENSE-MODEL, not the card's licence link, before shipping, and keep the required Notice text in every copy
8. Without the hardware, call it through Novita or Featherless AI; MiniMax's own API retires M2.5 on 18 November 2026

## Questions

### Which is better for AI agents, Laguna M.1 or MiniMax-M2.5?

MiniMax-M2.5 scores 57.5 (C) on agent readiness against Laguna M.1's 55.5 (C), and leads in 1 of 5 scored categories. Laguna M.1 leads on risk, agent interface and documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/laguna-m-1-vs-minimax-m2-5.json, and with the fewest tokens: https://www.anchorterminal.com/compare/laguna-m-1-vs-minimax-m2-5.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "laguna-m-1", "b": "minimax-m2-5"}`. From a terminal: `anchor compare laguna-m-1 minimax-m2-5`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/laguna-m-1.json and https://www.anchorterminal.com/api/v1/tools/minimax-m2-5.json

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