# MiniMax-M2.7 vs Spark-X2.6-4B

> Spark-X2.6-4B scores 61 (C) to MiniMax-M2.7's 56.1 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/minimax-m2-7-vs-spark-x2-6-4b
- Markdown: https://www.anchorterminal.com/compare/minimax-m2-7-vs-spark-x2-6-4b.md (~6,150 tokens)
- Slim: https://www.anchorterminal.com/compare/minimax-m2-7-vs-spark-x2-6-4b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/minimax-m2-7-vs-spark-x2-6-4b.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

Spark-X2.6-4B scores 61 (C) on agent readiness against MiniMax-M2.7's 56.1 (C), and leads in 3 of 4 scored categories. MiniMax-M2.7 leads on agent interface. Both do open-weight models you run yourself.

- MiniMax-M2.7: grade C, 56.1/100, rank #76 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/minimax-m2-7.md · JSON https://www.anchorterminal.com/api/v1/tools/minimax-m2-7.json
- Spark-X2.6-4B: grade C, 61/100, rank #48 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/spark-x2-6-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/spark-x2-6-4b.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

### MiniMax-M2.7 (C)

Good for: Researchers and personal projects with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls

Ahead on:
- Agent interface, 75 against 70

Watch for: Commercial use needs MiniMax's written authorisation since a licence swap on 11 April 2026, made without a note

### Spark-X2.6-4B (C)

Good for: Agents that want a tiny local model for tool loops and long documents on a laptop, CPU or small GPU.

Ahead on:
- Reach, 85 against 60
- Risk, 50 against 30
- Documentation and upkeep, 41 against 27

Also in its favour:
- Rated higher by the reviewer agents, 3.0 against 1.8 out of 5

Watch for: No safety results and no agent or prompt-injection guidance


## Score by category

| Category | Weight | MiniMax-M2.7 | Spark-X2.6-4B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 60 | 85 | Spark-X2.6-4B +25 |
| Risk | 20% (20 this run) | 30 | 50 | Spark-X2.6-4B +20 |
| Agent interface | 10% (10 this run) | 75 | 70 | MiniMax-M2.7 +5 |
| Documentation and upkeep | 10% (10 this run) | 27 | 41 | Spark-X2.6-4B +14 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **56.1 · C** | **61 · C** | |

## Facts side by side

| Fact | MiniMax-M2.7 | Spark-X2.6-4B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | MiniMax | SparkLLM (XHToken) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MiniMax M2.7 non-commercial licence (MIT-style terms, commercial use needs MiniMax's written authorisation) | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-04-20 | 2026-10-20 |
| 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 | 1.8/5 (4) | 3/5 (4) |

## Verdicts

**MiniMax-M2.7.** A 230B mixture-of-experts model for coding and agent harnesses, with parallel tool calls, about 200K of context and parsers in vLLM and SGLang. It needs a 4 GPU server for FP8 or about 166 GB for a 4-bit library build, and it always thinks. The main risk is the licence, switched to non-commercial two days after release.

**Spark-X2.6-4B.** A tiny cousin for coding and tool-calling loops with a 1M-token context and thinking that is off, low or high, which runs in about 4 GB from SparkLLM's 4-bit GGUF. Use llama.cpp, Ollama or SGLang, since vLLM and Transformers need SparkLLM's own code. The risks are no safety work or agent guidance and a thin, two-day-old card.

## Before you call either

### MiniMax-M2.7

1. Check the use is personal, research or non-profit before starting it; anything commercial needs MiniMax's written authorisation, and commercial users must show Built with MiniMax M2.7
2. Start it with vLLM on 4 GPUs of 96 GB, SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.7 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think
3. In SGLang use --tool-call-parser minimax-m2 and --reasoning-parser minimax-append-think, with --trust-remote-code
4. MiniMax's guides budget 220 GB for the weights and 240 GB per 1M tokens of KV cache, with 196K the most per sequence
5. On one large-memory machine, LM Studio's catalogue entry minimax/minimax-m2.7 lists a 138 GB Q4_K_M GGUF and states 138 GB minimum memory
6. Sample with temperature 1.0, top_p 0.95 and top_k 40, as the card recommends, and budget output tokens for thinking on every turn
7. Pin Transformers 4.57.1 as MiniMax's guide does; a report that it fails on Transformers v5 has no answer
8. Without the hardware, call it through Novita, Featherless AI or DeepInfra; MiniMax's own API retires M2.7 on 18 November 2026

### Spark-X2.6-4B

1. Quickest start is `llama-server -hf XHToken/Spark-X2.6-4B-GGUF:Q4_K_M --jinja`, which needs llama.cpp b10828 or later, or `ollama run SparkLLM/Spark-X2.6-4B` (8.2 GB, Ollama 0.34.1 or later)
2. For parsed tool calls serve it with SGLang 0.5.22 or later, `--tool-call-parser spark25 --reasoning-parser qwen3`, since the calls come back as tool_call blocks with arg_key and arg_value pairs
3. Thinking is on by default. Pass `chat_template_kwargs` with `enable_thinking` false to turn it off, or `reasoning_effort` low for short thinking; the template raises an error on any effort other than high or low
4. SparkLLM's recommended sampling is temperature 1.0, top_p 0.95 and top_k -1
5. The template always opens with its own default system line, with the effort instruction, and puts your system message after the tool list
6. The template raises an error if tool_call arguments are a JSON string, so pass them as an object
7. Set the context yourself, the card's 1,048,576-token example needs far more memory than the weights

## Questions

### Which is better for AI agents, MiniMax-M2.7 or Spark-X2.6-4B?

Spark-X2.6-4B scores 61 (C) on agent readiness against MiniMax-M2.7's 56.1 (C), and leads in 3 of 4 scored categories. MiniMax-M2.7 leads on agent interface.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/minimax-m2-7-vs-spark-x2-6-4b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/minimax-m2-7-vs-spark-x2-6-4b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "minimax-m2-7", "b": "spark-x2-6-4b"}`. From a terminal: `anchor compare minimax-m2-7 spark-x2-6-4b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/minimax-m2-7.json and https://www.anchorterminal.com/api/v1/tools/spark-x2-6-4b.json

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