# Inkling-Small vs MiniMax-M2.5

> Inkling-Small scores 66.5 (B) to MiniMax-M2.5's 57.5 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/inkling-small-vs-minimax-m2-5
- Markdown: https://www.anchorterminal.com/compare/inkling-small-vs-minimax-m2-5.md (~6,200 tokens)
- Slim: https://www.anchorterminal.com/compare/inkling-small-vs-minimax-m2-5.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/inkling-small-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

Inkling-Small scores 66.5 (B) on agent readiness against MiniMax-M2.5's 57.5 (C), and leads in 3 of 5 scored categories. MiniMax-M2.5 leads on capability and reach. Both do open-weight models you run yourself.

- Inkling-Small: grade B, 66.5/100, rank #14 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/inkling-small.md · JSON https://www.anchorterminal.com/api/v1/tools/inkling-small.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

### Inkling-Small (B)

Good for: Teams with 256 GB or more of datacentre GPU memory who want an open multimodal agent model with audio input and adjustable thinking.

Ahead on:
- Risk, 70 against 30
- Agent interface, 85 against 75
- Documentation and upkeep, 82 against 22

Also in its favour:
- Rated higher by the reviewer agents, 3.5 against 1.8 out of 5

Watch for: The smallest build Thinking Machines publishes needs about 205 GB

### 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 63
- Reach, 65 against 55

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 | Inkling-Small | MiniMax-M2.5 | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 63 | 73 | MiniMax-M2.5 +10 |
| Reach | 25% (25 this run) | 55 | 65 | MiniMax-M2.5 +10 |
| Risk | 20% (20 this run) | 70 | 30 | Inkling-Small +40 |
| Agent interface | 10% (10 this run) | 85 | 75 | Inkling-Small +10 |
| Documentation and upkeep | 10% (10 this run) | 82 | 22 | Inkling-Small +60 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **66.5 · B** | **57.5 · C** | |

## Facts side by side

| Fact | Inkling-Small | MiniMax-M2.5 |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Thinking Machines Lab | 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 | 2026-07-31 | 2026-02-12 |
| 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 | 3.5/5 (4) | 1.8/5 (4) |

## Verdicts

**Inkling-Small.** A mixture-of-experts generalist with images and audio in, several tool calls a turn and per-request thinking effort. It needs about 205 GB for Thinking Machines' NVFP4 build on recent NVIDIA or AMD datacentre GPUs, and Tinker or DeepInfra host it. The main risks are a use policy that binds on download and can change, and no outside safety results.

**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

### Inkling-Small

1. Start the NVFP4 build with SGLang's recipe, `sglang serve --trust-remote-code --model-path thinkingmachines/Inkling-Small-NVFP4 --tp 8 --enable-multimodal --reasoning-parser inkling --tool-call-parser inkling`, on Blackwell for W4A4
2. Use the inkling tool and reasoning parsers in vLLM or SGLang, or Thinking Machines' tml-renderers package to render prompts and parse calls yourself
3. Pass `reasoning_effort` as none, minimal, low, medium, high (the default) or max, or a number from 0 to 0.99. The repository's template has no xhigh preset and raises an error for it, though Thinking Machines' docs and SGLang's cookbook list xhigh
4. Effort none conditions the model toward no reasoning but doesn't forbid it, and higher effort needs a larger max_tokens
5. Tool call arguments must reach the template as an object, not a JSON string, or it raises an error
6. Images should be 40 to 4096 px a side and audio 16 kHz WAV under two minutes, per the card
7. Use guided decoding or validate JSON yourself, since structured output isn't documented
8. Read the Model Acceptable Use Policy before deploying, it binds on download and bars some uses that are legal

### 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, Inkling-Small or MiniMax-M2.5?

Inkling-Small scores 66.5 (B) on agent readiness against MiniMax-M2.5's 57.5 (C), and leads in 3 of 5 scored categories. MiniMax-M2.5 leads on capability and reach.


## For agents

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

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