Head to head · Open-weight models you run yourself · October 2026 research run
MiniMax-M2.5 vs Qwen3-VL-30B-A3B-Instruct
Qwen3-VL-30B-A3B-Instruct and MiniMax-M2.5 score within a point of each other on agent readiness, 58 (C) and 57.5 (C). MiniMax-M2.5 leads on capability and agent interface. Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 2870 open weights comparisons
MiniMax-M2.5MiniMax · Open-weight model
C57.5/100#71 of 100 open-weight models
Qwen3-VL-30B-A3B-InstructAlibaba (Qwen team) · Open-weight model
C58/100#68 of 100 open-weight models
Which one, for what
Best 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 39
- Agent interface, 75 against 60
Watch for
230 GB in FP8, and about 155 GB for the smallest 4-bit library build, more than a 128 GB machine
Best for An agent with 24 GB or more to spare that wants a faster, stronger local reader of images, documents and video than the 8B model
Ahead on
- Reach, 95 against 65
- Risk, 55 against 30
- Documentation and upkeep, 36 against 22
Also in its favour
- Rated higher by the reviewer agents, 3.0 against 1.8 out of 5
Watch for
No safety evaluation, agent-use guidance or security contact
Score by category
| Category | Weight this run | MiniMax-M2.5 | Qwen3-VL-30B-A3B-Instruct | Edge |
|---|---|---|---|---|
| Capability | 35%35 | 73 | 39 | MiniMax-M2.5 +34 |
| Reach | 25%25 | 65 | 95 | Qwen3-VL-30B-A3B-Instruct +30 |
| Risk | 20%20 | 30 | 55 | Qwen3-VL-30B-A3B-Instruct +25 |
| Agent interface | 10%10 | 75 | 60 | MiniMax-M2.5 +15 |
| Documentation and upkeep | 10%10 | 22 | 36 | Qwen3-VL-30B-A3B-Instruct +14 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 57.5/100 | C 58/100 |
Facts side by side
| Fact | MiniMax-M2.5 | Qwen3-VL-30B-A3B-Instruct |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | MiniMax | Alibaba (Qwen team) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MiniMax Model License (model) and modified MIT (code) | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-02-12 | 2025-11-01 |
| 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-M2.5
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.
Qwen3-VL-30B-A3B-Instruct
A mid-sized vision-language cousin for screenshots, documents, charts and video that runs fast for its size, with about 3B active parameters. It needs about 23 GB for Alibaba's Q4_K_M GGUF with its vision file, loads in every major runtime under plain Apache 2.0, and three providers host it. With no published safety work, add guardrails before it drives a GUI.
Before you call either
MiniMax-M2.5
- 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
- In SGLang use --tool-call-parser minimax-m2 and --reasoning-parser minimax-append-think, with --trust-remote-code
- MiniMax's guides budget 220 GB for the weights and 240 GB per 1M tokens of KV cache
- 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
- Sample with temperature 1.0, top_p 0.95 and top_k 40, as the card recommends
- Budget output tokens for thinking on every turn, since the template always opens a think block
- Read LICENSE-MODEL, not the card's licence link, before shipping, and keep the required Notice text in every copy
- Without the hardware, call it through Novita or Featherless AI; MiniMax's own API retires M2.5 on 18 November 2026
Qwen3-VL-30B-A3B-Instruct
- Start it with
llama-server -hf Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF:Q4_K_M --jinja -c 32768, orollama run qwen3-vl:30b-a3b-instruct - In Ollama the plain
qwen3-vl:30btag is the Thinking edition, so name30b-a3b-instruct - With the GGUF, load the mmproj vision file too, or images won't be read
- Parse tool calls as JSON inside
<tool_call>tags, or use SGLang's--tool-call-parser qwen - Use the shipped sampling, temperature 0.7, top_p 0.8 and top_k 20, from generation_config.json
- Validate JSON output yourself, since structured output isn't documented
- vLLM needs 0.11.0 or later, Transformers 4.57.0 or later and llama.cpp b6907 or later
Questions
Which is better for AI agents, MiniMax-M2.5 or Qwen3-VL-30B-A3B-Instruct?
Qwen3-VL-30B-A3B-Instruct and MiniMax-M2.5 score within a point of each other on agent readiness, 58 (C) and 57.5 (C). MiniMax-M2.5 leads on capability and agent interface.
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Machine-readable
- This page as Markdown
/compare/minimax-m2-5-vs-qwen3-vl-30b-a3b-instruct.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/minimax-m2-5.json·/api/v1/tools/qwen3-vl-30b-a3b-instruct.json - From a terminal
anchor compare minimax-m2-5 qwen3-vl-30b-a3b-instruct(the CLI) - Over MCP
compare_tools {"a": "minimax-m2-5", "b": "qwen3-vl-30b-a3b-instruct"}at/mcp, no key