# MiniMax-M3.1-Flash vs Qwen3-Coder-Next

> Qwen3-Coder-Next scores 57.6 (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/minimax-m3-1-flash-vs-qwen3-coder-next
- Markdown: https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-coder-next.md (~6,850 tokens)
- Slim: https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-coder-next.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-coder-next.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

Qwen3-Coder-Next scores 57.6 (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. Both do open-weight models you run yourself.

- 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
- Qwen3-Coder-Next: grade C, 57.6/100, rank #74 of 110 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-coder-next.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-coder-next.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

### 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 42

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

### Qwen3-Coder-Next (C)

Good for: Agents that want a local coding model for long tool-calling loops on a single 64 GB to 128 GB machine.

Ahead on:
- Reach, 85 against 40
- Risk, 50 against 35
- Agent interface, 75 against 70

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

Watch for: No LICENSE file anywhere and a broken licence link on the card


## Score by category

| Category | Weight | MiniMax-M3.1-Flash | Qwen3-Coder-Next | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 64 | 42 | MiniMax-M3.1-Flash +22 |
| Reach | 25% (25 this run) | 40 | 85 | Qwen3-Coder-Next +45 |
| Risk | 20% (20 this run) | 35 | 50 | Qwen3-Coder-Next +15 |
| Agent interface | 10% (10 this run) | 70 | 75 | Qwen3-Coder-Next +5 |
| Documentation and upkeep | 10% (10 this run) | 41 | 41 | even |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **50.5 · D** | **57.6 · C** | |

## Facts side by side

| Fact | MiniMax-M3.1-Flash | Qwen3-Coder-Next |
| --- | --- | --- |
| 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 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) | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-23 | 2026-02-03 |
| 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 | 2.3/5 (4) | 2.8/5 (4) |

## Verdicts

**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.

**Qwen3-Coder-Next.** A fast coding cousin for agent harnesses, with 3B active parameters and tool calling trained across many scaffold formats for Claude Code, Cline or Qwen Code loops. Alibaba's 4-bit GGUF needs about 58 GB, fitting one 64 GB machine, and two providers host it. The main risk is neglect, with no change since February, no replies and no safety results.

## Before you call either

### 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

### Qwen3-Coder-Next

1. Quickest start is `ollama run qwen3-coder-next` (Q4_K_M, 52 GB of files)
2. On our reference machine Alibaba's Q8_0 GGUF (84.8 GB, about 102 GB in use) also fits, run with `llama-server -m Qwen3-Coder-Next-Q8_0/Qwen3-Coder-Next-Q8_0-00001-of-00004.gguf --jinja -ngl 99 -fa on -c 131072`
3. Serve with `vllm serve Qwen/Qwen3-Coder-Next --enable-auto-tool-choice --tool-call-parser qwen3_coder` (vLLM 0.15.0 or later), using the built-in parser rather than the repository's qwen3coder_tool_parser_vllm.py, which fails to import on newer vLLM per discussion 36
4. The card's commands use tensor parallel 2 though the text says 4 GPUs, and it suggests cutting the context to 32,768 if the server fails to start
5. There's no thinking mode, so don't pass `enable_thinking` or wait for reasoning_content
6. Sampling per the card is temperature 1.0, top_p 0.95 and top_k 40
7. The GGUF card's llama.cpp example path omits the quant from the file name; the real files are named like Qwen3-Coder-Next-Q4_K_M-00001-of-00004.gguf
8. Validate any JSON it returns, since structured output isn't documented

## Questions

### Which is better for AI agents, MiniMax-M3.1-Flash or Qwen3-Coder-Next?

Qwen3-Coder-Next scores 57.6 (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.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-coder-next.json, and with the fewest tokens: https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-coder-next.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "minimax-m3-1-flash", "b": "qwen3-coder-next"}`. From a terminal: `anchor compare minimax-m3-1-flash qwen3-coder-next`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/minimax-m3-1-flash.json and https://www.anchorterminal.com/api/v1/tools/qwen3-coder-next.json

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