# Ling-3.0-tiny vs MiniMax-M3.1-Flash

> Ling-3.0-tiny scores 55 (C) to MiniMax-M3.1-Flash's 50.5 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-minimax-m3-1-flash
- Markdown: https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-minimax-m3-1-flash.md (~6,750 tokens)
- Slim: https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-minimax-m3-1-flash.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-minimax-m3-1-flash.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

Ling-3.0-tiny scores 55 (C) on agent readiness against MiniMax-M3.1-Flash's 50.5 (D), and leads in 3 of 5 scored categories. MiniMax-M3.1-Flash leads on capability and agent interface. Both do open-weight models you run yourself.

- Ling-3.0-tiny: grade C, 55/100, rank #87 of 110 open-weight models. Markdown https://www.anchorterminal.com/tools/ling-3-0-tiny.md · JSON https://www.anchorterminal.com/api/v1/tools/ling-3-0-tiny.json
- MiniMax-M3.1-Flash: grade D, 50.5/100, rank #99 of 110 open-weight models. Markdown https://www.anchorterminal.com/tools/minimax-m3-1-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/minimax-m3-1-flash.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 3260 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### Ling-3.0-tiny (C)

Good for: Agents on a laptop or small GPU that want a fast local model for tool calls and short reasoning, under MIT

Ahead on:
- Reach, 90 against 40
- Risk, 50 against 35
- Documentation and upkeep, 56 against 41

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

Watch for: No hosted provider today, and Ollama support is only an unmerged pull request

### MiniMax-M3.1-Flash (D)

Good for: Teams with a 4 to 8 GPU server who want an open multimodal agent and coding model with 1M tokens of context and can meet the licence's attribution and notice terms.

Ahead on:
- Capability, 64 against 31
- Agent interface, 70 against 60

Watch for: No released vLLM or SGLang ran it on 24 October 2026; only llama.cpp b11861 from that day did


## Score by category

| Category | Weight | Ling-3.0-tiny | MiniMax-M3.1-Flash | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 31 | 64 | MiniMax-M3.1-Flash +33 |
| Reach | 25% (25 this run) | 90 | 40 | Ling-3.0-tiny +50 |
| Risk | 20% (20 this run) | 50 | 35 | Ling-3.0-tiny +15 |
| Agent interface | 10% (10 this run) | 60 | 70 | MiniMax-M3.1-Flash +10 |
| Documentation and upkeep | 10% (10 this run) | 56 | 41 | Ling-3.0-tiny +15 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **55 · C** | **50.5 · D** | |

## Facts side by side

| Fact | Ling-3.0-tiny | MiniMax-M3.1-Flash |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | inclusionAI (Ant Group) | MiniMax |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | MiniMax Community License (custom; commercial use needs a "Built with MiniMax M3.1-Flash" line and a one-time notice to MiniMax, and MiniMax's written authorisation above $20M yearly revenue) |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-08-31 | 2026-10-23 |
| 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 | 2.8/5 (4) | 2.3/5 (4) |

## Verdicts

**Ling-3.0-tiny.** A small, quick hybrid reasoning cousin for local agent work, tool calls and coding, under MIT. It runs in about 6 GB from inclusionAI's 4-bit GGUF in released Transformers, vLLM, SGLang and llama.cpp, with no host. The main risks are a card that doesn't match the files in places, the vLLM route running repository code, and no model safety results.

**MiniMax-M3.1-Flash.** A 428B mixture-of-experts model, 23B active, for agent work, coding and computer use, with image and video input, about 1M tokens of context and parallel tool calls. It needs a 4 to 8 GPU server for 250 GB of NVFP4 and MXFP8 weights, and vLLM and SGLang support was pre-release on 24 October 2026. Commercial use needs attribution and notice.

## Before you call either

### Ling-3.0-tiny

1. Start it with `llama-server -hf inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M --jinja -c 32768`, which needs llama.cpp b11772 or a recent release with bailingmoe3
2. With vLLM 0.32.0, `vllm serve inclusionAI/Ling-3.0-tiny --trust-remote-code --enable-prefix-caching --mamba-cache-mode align --enable-auto-tool-choice --tool-call-parser ling3 --reasoning-parser ling3`, which runs the repository's config code
3. With SGLang 0.5.22, its cookbook uses `--reasoning-parser deepseek-r1 --tool-call-parser glm45` for this model, and drop the card's NEXTN flags since tiny has no MTP layer
4. Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false for short tasks
5. Sampling per the card is temperature 1.0, top_p 0.95 and top_k 20
6. The template keeps earlier reasoning in the prompt, which grows long agent sessions
7. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented
8. For a guardrail, inclusionAI's Ling-3.0-tiny-singprobe scores intent, unsafe output and hallucination per token, but only through SGLang and vLLM integration branches

### MiniMax-M3.1-Flash

1. Check the use case first; commercial products must show "Built with MiniMax M3.1-Flash" and notify MiniMax once, or get written authorisation above $20M yearly revenue
2. On 8 H100 or H200 GPUs use vLLM's nightly image vllm/vllm-openai:minimax-m31-flash with --tensor-parallel-size 8 --block-size 128 --kv-cache-dtype fp8 --moe-backend marlin --max-model-len 262144
3. Pass --reasoning-parser minimax_m3 --tool-call-parser minimax_m3 --enable-auto-tool-choice in vLLM, or minimax-m3 for both in SGLang with --trust-remote-code
4. --block-size 128 is mandatory in vLLM, since MSA's sparse block size is 128
5. Set effort per request with chat_template_kwargs reasoning_effort (low, medium, high, xhigh, max); sample at temperature 1.0 and top_p 0.95
6. On Blackwell or MI355X, 4 GPUs serve it with the DSpark draft from dspark/; vLLM's Hopper command leaves DSpark off and turns video input off
7. For GGUF builds by others, use llama.cpp b11861 or later; earlier builds mis-scale the NVFP4 experts and produce garbage
8. Without the hardware, call MiniMax-M3.1-Flash on MiniMax's API (minimax-api), whose thinking defaults to max effort

## Questions

### Which is better for AI agents, Ling-3.0-tiny or MiniMax-M3.1-Flash?

Ling-3.0-tiny scores 55 (C) on agent readiness against MiniMax-M3.1-Flash's 50.5 (D), and leads in 3 of 5 scored categories. MiniMax-M3.1-Flash leads on capability and agent interface.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-minimax-m3-1-flash.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-minimax-m3-1-flash.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "ling-3-0-tiny", "b": "minimax-m3-1-flash"}`. From a terminal: `anchor compare ling-3-0-tiny minimax-m3-1-flash`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/ling-3-0-tiny.json and https://www.anchorterminal.com/api/v1/tools/minimax-m3-1-flash.json

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