# Hy3 vs Swift 1.5 Qwen3.8-27B

> Hy3 scores 62.6 (B) to Swift 1.5 Qwen3.8-27B's 61 (C) 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-swift-1-5-qwen3-8-27b
- Markdown: https://www.anchorterminal.com/compare/hy3-vs-swift-1-5-qwen3-8-27b.md (~6,100 tokens)
- Slim: https://www.anchorterminal.com/compare/hy3-vs-swift-1-5-qwen3-8-27b.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/hy3-vs-swift-1-5-qwen3-8-27b.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

Hy3 scores 62.6 (B) on agent readiness against Swift 1.5 Qwen3.8-27B's 61 (C), and leads in 3 of 4 scored categories. Swift 1.5 Qwen3.8-27B leads on reach. Both do open-weight models you run yourself.

- Hy3: grade B, 62.6/100, rank #27 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/hy3.md · JSON https://www.anchorterminal.com/api/v1/tools/hy3.json
- Swift 1.5 Qwen3.8-27B: grade C, 61/100, rank #49 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/swift-1-5-qwen3-8-27b.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

### Hy3 (B)

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

Ahead on:
- Risk, 60 against 50
- Agent interface, 75 against 60
- Documentation and upkeep, 56 against 43

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

### Swift 1.5 Qwen3.8-27B (C)

Good for: Agents on a 24 to 32 GB GPU or Mac that want Qwen3.8-27B's skills with shorter thinking, for teams under US$1M revenue or research.

Ahead on:
- Reach, 80 against 70

Watch for: Commercial use above US$1M gross annual revenue needs a separate licence


## Score by category

| Category | Weight | Hy3 | Swift 1.5 Qwen3.8-27B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 70 | 80 | Swift 1.5 Qwen3.8-27B +10 |
| Risk | 20% (20 this run) | 60 | 50 | Hy3 +10 |
| Agent interface | 10% (10 this run) | 75 | 60 | Hy3 +15 |
| Documentation and upkeep | 10% (10 this run) | 56 | 43 | Hy3 +13 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **62.6 · B** | **61 · C** | |

## Facts side by side

| Fact | Hy3 | Swift 1.5 Qwen3.8-27B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Tencent (Hy team) | UkisAI |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | Swift Open License v1.0 (custom, not an OSI licence) on UkisAI's changes, free for commercial use below US$1M gross annual revenue with a separate Swift Enterprise License above it. Built on Qwen3.8-27B, whose Apache 2.0 still governs the base |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-07-20 | 2026-09-24 |
| Terms last updated | 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.

**Swift 1.5 Qwen3.8-27B.** A Qwen3.8-27B cousin post-trained to think in fewer tokens, for coding and agent work with images in, that fits a 24 GB machine with UkisAI's own 4-bit builds. Commercial use is free only below US$1M revenue. The main risks are that nobody at UkisAI answers its discussions, and that the shorter thinking rests on UkisAI's own benchmarks.

## 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

### Swift 1.5 Qwen3.8-27B

1. Quickest start is `llama-server -hf ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Q4_K_M --jinja`, about 18.4 GB with the vision projector
2. On our reference machine BF16 fits, `vllm serve ukisai/Swift-1.5-Qwen3.8-27b --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`
3. The template is Qwen3.8-27B's, so `chat_template_kwargs` with `reasoning_effort` low, medium or xhigh (default) and `enable_thinking` false work as on the base
4. UkisAI benchmarked it at temperature 1.0, top_p 0.95, top_k 20 and presence_penalty 0, which is a reasonable starting point
5. The MLX 4-bit build needs UkisAI's patched MLX-LM and has no image or video chat
6. Check the licence before commercial use, it stops at US$1M gross annual revenue including affiliates
7. MTP self-speculative decoding is available with the base's head, flags on the card

## Questions

### Which is better for AI agents, Hy3 or Swift 1.5 Qwen3.8-27B?

Hy3 scores 62.6 (B) on agent readiness against Swift 1.5 Qwen3.8-27B's 61 (C), and leads in 3 of 4 scored categories. Swift 1.5 Qwen3.8-27B leads on reach.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/hy3-vs-swift-1-5-qwen3-8-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/hy3-vs-swift-1-5-qwen3-8-27b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "hy3", "b": "swift-1-5-qwen3-8-27b"}`. From a terminal: `anchor compare hy3 swift-1-5-qwen3-8-27b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/hy3.json and https://www.anchorterminal.com/api/v1/tools/swift-1-5-qwen3-8-27b.json

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