# Hy3 vs MiniMax-M3.1-Flash

> Hy3 scores 62.6 (B) 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/hy3-vs-minimax-m3-1-flash
- Markdown: https://www.anchorterminal.com/compare/hy3-vs-minimax-m3-1-flash.md (~6,350 tokens)
- Slim: https://www.anchorterminal.com/compare/hy3-vs-minimax-m3-1-flash.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/hy3-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

Hy3 scores 62.6 (B) on agent readiness against MiniMax-M3.1-Flash's 50.5 (D), and leads in 4 of 5 scored categories. MiniMax-M3.1-Flash leads on capability. Both do open-weight models you run yourself.

- Hy3: grade B, 62.6/100, rank #28 of 110 open-weight models. Markdown https://www.anchorterminal.com/tools/hy3.md · JSON https://www.anchorterminal.com/api/v1/tools/hy3.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

### Hy3 (B)

Good for: Teams with an 8-GPU server who want an agent-tuned MoE model under plain Apache 2.0

Ahead on:
- Reach, 70 against 40
- Risk, 60 against 35
- Agent interface, 75 against 70
- 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: 597.6 GB in BF16, 299.9 GB in FP8 and 168.5 GB at the smallest 4-bit build

### 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 57

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 | Hy3 | MiniMax-M3.1-Flash | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 57 | 64 | MiniMax-M3.1-Flash +7 |
| Reach | 25% (25 this run) | 70 | 40 | Hy3 +30 |
| Risk | 20% (20 this run) | 60 | 35 | Hy3 +25 |
| Agent interface | 10% (10 this run) | 75 | 70 | Hy3 +5 |
| Documentation and upkeep | 10% (10 this run) | 56 | 41 | Hy3 +15 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **62.6 · B** | **50.5 · D** | |

## Facts side by side

| Fact | Hy3 | MiniMax-M3.1-Flash |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Tencent (Hy team) | MiniMax |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | 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 | 2026-07-20 | 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

**Hy3.** Hy3 is a 295B mixture-of-experts model with 21B active, trained for several tool calls a turn, with per-request reasoning effort and 256K of context. It needs an 8-GPU server for FP8 or about 200 GB for a 4-bit build. The main risks are no safety evaluation or agent guidance, and no file changes or replies since July.

**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

### Hy3

1. Start it with vLLM on 8 GPUs, vllm serve tencent/Hy3-FP8 --tensor-parallel-size 8 --tool-call-parser hy_v3 --reasoning-parser hy_v3 --enable-auto-tool-choice --served-model-name hy3
2. In SGLang use --tool-call-parser hunyuan --reasoning-parser hunyuan, as the card shows
3. Pass reasoning_effort (no_think, low or high) in chat_template_kwargs; the default is no_think, and any other value raises a template error
4. With the vLLM reasoning parser the chain of thought comes back in message.reasoning and the answer in message.content
5. Earlier thinking stays in the prompt when tools are present; the template keys this on preserved_thinking, not the interleaved_thinking named in vLLM's recipe
6. Use temperature 0.9 and top_p 1.0, the card's recommended sampling
7. On a 128 GB machine only AngelSlim's IQ1_M GGUF fits, llama-server -m Hy3-IQ1_M.gguf -ctk q8_0 -ctv q8_0 -fa on -c 65536, with llama.cpp b11768 or later; at 89.4 GB × 1.2 it sits right at the 107.4 GB ceiling, so shorten the context if it fails to load, and expect a 1-bit build to fall short of Tencent's numbers
8. Without the hardware, call it through Featherless AI or DeepInfra via Hugging Face's inference providers

### 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, Hy3 or MiniMax-M3.1-Flash?

Hy3 scores 62.6 (B) on agent readiness against MiniMax-M3.1-Flash's 50.5 (D), and leads in 4 of 5 scored categories. MiniMax-M3.1-Flash leads on capability.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/hy3-vs-minimax-m3-1-flash.json, and with the fewest tokens: https://www.anchorterminal.com/compare/hy3-vs-minimax-m3-1-flash.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "hy3", "b": "minimax-m3-1-flash"}`. From a terminal: `anchor compare hy3 minimax-m3-1-flash`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/hy3.json and https://www.anchorterminal.com/api/v1/tools/minimax-m3-1-flash.json

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