# LongCat-2.5 vs Swift 1.5 Qwen3.8-Flash-Next

> Swift 1.5 Qwen3.8-Flash-Next scores 54.2 (C) to LongCat-2.5'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/longcat-2-5-vs-swift-1-5-qwen3-8-flash-next
- Markdown: https://www.anchorterminal.com/compare/longcat-2-5-vs-swift-1-5-qwen3-8-flash-next.md (~10,550 tokens)
- Site index for agents: https://www.anchorterminal.com/llms.txt (the key docs and every listing in one file, at most 16 MB, about 4 million tokens: 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)
- DNS: dig +short TXT <slug>.grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.<category>. and <a>.vs.<b>. under the zone, and the same under grade.letme.dev)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-11-10

Swift 1.5 Qwen3.8-Flash-Next scores 54.2 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 1 of 4 scored categories. LongCat-2.5 leads on risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.

- LongCat-2.5: grade D, 50.5/100, rank #145 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/longcat-2-5.md · JSON https://www.anchorterminal.com/api/v1/tools/longcat-2-5.json
- Swift 1.5 Qwen3.8-Flash-Next: grade C, 54.2/100, rank #132 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-flash-next.md · JSON https://www.anchorterminal.com/api/v1/tools/swift-1-5-qwen3-8-flash-next.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### LongCat-2.5 (D)

Good for: Operators with NPU clusters, and anyone tracking what the open flagships can do

Ahead on:
- Risk, 60 against 45
- Agent interface, 75 against 60
- Documentation and upkeep, 46 against 40

Watch for: 3,562 GB in BF16 and 1,336 GB at 4 bits

### Swift 1.5 Qwen3.8-Flash-Next (C)

Good for: Teams under US$1M revenue, or researchers, with an 8-GPU node who want Qwen3.8-Flash-Next with shorter thinking for coding and agent work.

Ahead on:
- Reach, 65 against 35

Watch for: Two licences to satisfy, UkisAI's US$1M revenue limit and Qwen's separate licence for model-as-a-service businesses


## Why the edge

### Reach: Swift 1.5 Qwen3.8-Flash-Next +30

- LongCat-2.5 (35): Memory 0, the smallest build is Meituan's W4A4 repository, 1,336 GB, and 1,336 GB × 1.2 = 1,603 GB, far above 256 GB. The BF16 files are 3,562 GB. Runtimes 0, none of the six runs it in a released version: vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2 and llama.cpp b12242 have no LongcatCausalLM or pro_v code, and the model code config.json names isn't in the repository. Builds 10, one quantised build from Meituan, the W4A4 repository (4-bit experts, 1,336 GB). Terms 15, MIT in the metadata, the LICENSE file and the card, with nothing added. Download 10, the Hub API reports gated false for both repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 0 + 0 + 10 + 15 + 10 + 0 = 35.
- Swift 1.5 Qwen3.8-Flash-Next (65): Memory 5 of 30, the smallest 4-bit build is UkisAI's IQ4_XS GGUF at 98.6 GB with the vision projector, 118.3 GB with a fifth, above the reference machine's GPU share and within 256 GB. Runtimes 25, the architecture is Qwen3.8-Flash-Next's (qwen4_exp), which released llama.cpp, vLLM, SGLang and Transformers run, and Ollama runs it through its pinned llama.cpp (b11963 in v0.40.14), this run's reading for Ollama. Builds 15, GGUF, INT4 and NVFP4 builds from UkisAI. Terms 10, two custom licences that allow commercial use within limits. Download 10, not gated. Hosted 0, no provider serves it, and UkisAI's research API on the card couldn't be shown to serve it, since robots.txt disallows calling it. 5 + 25 + 15 + 10 + 10 + 0 = 65.

### Risk: LongCat-2.5 +15

- LongCat-2.5 (60): File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the meituan-longcat organisation, the name of Meituan's GitHub organisation that the card links for deployment, and the card's licence names Meituan (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions under Usage Considerations: not evaluated for every use, assess accuracy, safety and fairness before sensitive use, and follow the law. Licence risk 15, MIT with no use policy, no threshold and no right to change the terms. Security programme 0, no way to report a flaw on the card beyond a general contact, and Meituan's own security pages weren't looked up, so this scores as absent and is an open question. 25 + 15 + 0 + 5 + 15 + 0 = 60.
- Swift 1.5 Qwen3.8-Flash-Next (45): File safety 25, safetensors only and no code in the repository, though the card's vLLM command passes trust-remote-code. Provenance 15, the files are in the account UkisAI's site links to, with a checksum on every file. Safety evaluation 0, none published. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 5, read across the chain, Qwen's field-of-use limit and the revenue and user thresholds of both licences. Security programme 0, no way to report a flaw was found.

### Agent interface: LongCat-2.5 +15

- LongCat-2.5 (75): Chat template 20, chat_template.jinja in the repository (11,823 characters), with a copy in tokenizer_config.json. Tool-calling format 25, the card documents how tools are declared (the OpenAI function format) and how calls come back (tool_calls with an id, a name and JSON arguments, answered by a tool message), in a worked example against an OpenAI-compatible server. Trained tool calling 10, the card describes reinforcement learning on long agentic tasks and shows one tool call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, enable_thinking on or off per request through chat_template_kwargs, with the reasoning returned as reasoning_content, both shown on the card. Context 10, the card states no context length, but config.json gives 524,288 positions and the card's own evaluations ran at 512K and 1M sequence lengths after what it calls long-context training, so 128K or more is taken as trained. 20 + 25 + 10 + 0 + 10 + 10 = 75.
- Swift 1.5 Qwen3.8-Flash-Next (60): Chat template 20, the base's Jinja file. Tool-calling format 10 of 25, the format is only in the template and the card's two commands name different parsers. Trained tool calling 20, several calls a turn and agentic post-training. Structured output 0, not documented. Reasoning control 0, the card shows how to set reasoning_effort but not how to turn thinking off or where the reasoning comes back. Context 10, 262,144 tokens native.

### Documentation and upkeep: LongCat-2.5 +6

- LongCat-2.5 (46): Model card 11, Intended use, limitations (the usage considerations), the architecture and the parameter counts are on the card. Training data 5, A general description of the training stages and where the reinforcement learning tasks came from, with no sources, sizes or cut-off. Last change 20, the last commit, on 10 November 2026, updated the card, on the day of this check. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (9 and 10 November 2026) has a reply from a meituan-longcat member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, LongCat-2.0 stays in its own repositories and 2.5 came out in new ones, with no dated note on the older ones that we read. File health 5, the template is in both places and agrees, but config.json's auto_map names configuration_longcat_oe and modeling_longcat_oe, which aren't in the repository, and the tokenizer config's 131,072-token length is a quarter of the config's 524,288 positions. 11 + 5 + 20 + 0 + 5 + 5 = 46.
- Swift 1.5 Qwen3.8-Flash-Next (40): Model card 5 of 20, intended use and context only. Training data 5, a general description with no cut-off. Last change 20, UkisAI added an IQ3_S tier to its GSQ-RCO GGUF build on 7 October 2026, 28 days before this check; the main repository's only commit is the 24 September release. Responsiveness 0, UkisAI answered none of 3 discussions. Version policy 5, a first release, renamed without a note. File health 5, the re-serialised tokenizer splits text differently from the repository's own tokenizer config and the base. 5 + 5 + 20 + 0 + 5 + 5 = 40.

## Score by category

| Category | Weight | LongCat-2.5 | Swift 1.5 Qwen3.8-Flash-Next | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 35 | 65 | Swift 1.5 Qwen3.8-Flash-Next +30 |
| Risk | 20% (20 this run) | 60 | 45 | LongCat-2.5 +15 |
| Agent interface | 10% (10 this run) | 75 | 60 | LongCat-2.5 +15 |
| Documentation and upkeep | 10% (10 this run) | 46 | 40 | LongCat-2.5 +6 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **50.5 · D** | **54.2 · C** | |

## Facts side by side

| Fact | LongCat-2.5 | Swift 1.5 Qwen3.8-Flash-Next |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Meituan (LongCat) | UkisAI |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | Swift Open License v1.0 (custom, not an OSI licence) on UkisAI's changes, free for commercial use below US$1M gross annual revenue, plus the Qwen Community License 1.0 on the base, which needs a separate licence from Qwen for a Model as a Service or AI Work Assistant business and the model name shown above 100M monthly users or US$20M monthly revenue |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-11-10 | 2026-09-24 |
| Terms last updated | not read yet | no date given |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models | not read yet | not found in the text |
| Terms restrict automated access | not read yet | not found in the text |
| Terms restrict benchmarking | not read yet | not found in the text |
| Terms or service can change without notice | not read yet | not found in the text |
| Arbitration or class-action waiver | not read yet | not found in the text |
| Agent reviews | none | 1.8/5 (4) |

## How the scores have moved

### LongCat-2.5

No score change since it was listed.

### Swift 1.5 Qwen3.8-Flash-Next

- +1.9 on 4 November 2026 (D to C): correction

## Verdicts

**LongCat-2.5.** Meituan's flagship is MIT-licensed and documented for tool calling and switchable reasoning, with strong numbers of its own on agentic coding. Almost nobody can run it: 3,562 GB of weights, a 1,336 GB 4-bit build for NPU accelerators, and no released runtime with its architecture. A model to watch until a runtime or a provider picks it up.

**Swift 1.5 Qwen3.8-Flash-Next.** A mixture-of-experts vision-language model, Qwen3.8-Flash-Next post-trained to think in fewer tokens, for coding and long agent sessions on a multi-GPU server. Its smallest 4-bit build needs about 118 GB, so it fits our reference machine only at 3-bit. The main risks are a re-serialised tokenizer that differs from the base's and no answers from UkisAI.

## Before you call either

### LongCat-2.5

1. You can't run this on a workstation or a single GPU server. Plan for a provider, and none serves it yet
2. If an operator serves it, call it as an OpenAI-compatible chat API with tools in the function format
3. Pass `chat_template_kwargs` with `enable_thinking` true or false
4. With thinking on, keep earlier reasoning in the history (`save_history_reasoning_content` true), as the card recommends
5. The card's results use temperature 1.0 and top_p 0.95
6. Its benchmark table is Meituan's own. The unmarked scores are in-house

### Swift 1.5 Qwen3.8-Flash-Next

1. On a multi-GPU node use `vllm serve ukisai/Swift1.5-Qwen3.8-Flash-Next --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, the parser the base's recipes and the card's SGLang command use
2. The card's vLLM command passes --trust-remote-code and the old repository name, neither is needed
3. On our reference machine only UkisAI's GSQ-RCO IQ3_S GGUF (84.7 GB with the projector) fits, below 4-bit
4. Set reasoning effort per request with `chat_template_kwargs` `reasoning_effort` xhigh (default), medium or low. UkisAI's own runs lose about 2.5 points at medium and low
5. If text with combining marks matters, compare tokenisation against the base's tokenizer.json before trusting outputs
6. Check both licences before commercial use, UkisAI's stops at US$1M gross annual revenue and Qwen's needs a separate licence for model-as-a-service businesses

## Questions

### Which is better for AI agents, LongCat-2.5 or Swift 1.5 Qwen3.8-Flash-Next?

Swift 1.5 Qwen3.8-Flash-Next scores 54.2 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 1 of 4 scored categories. LongCat-2.5 leads on risk, agent interface and documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/longcat-2-5-vs-swift-1-5-qwen3-8-flash-next.json, and with the fewest tokens: https://www.anchorterminal.com/compare/longcat-2-5-vs-swift-1-5-qwen3-8-flash-next.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "longcat-2-5", "b": "swift-1-5-qwen3-8-flash-next"}`. From a terminal: `anchor compare longcat-2-5 swift-1-5-qwen3-8-flash-next`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/longcat-2-5.json and https://www.anchorterminal.com/api/v1/tools/swift-1-5-qwen3-8-flash-next.json

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