# Qwen3.8-Flash-Next vs Ripple-65B Preview

> Qwen3.8-Flash-Next scores 61.8 (C) to Ripple-65B Preview's 53.3 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-ripple-65b-preview
- Markdown: https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-ripple-65b-preview.md (~8,500 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: 2026-11-04

Qwen3.8-Flash-Next scores 61.8 (C) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in 1 of 4 scored categories. Ripple-65B Preview leads on risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.

- Qwen3.8-Flash-Next: grade C, 61.8/100, rank #34 of 121 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-8-flash-next.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-8-flash-next.json
- Ripple-65B Preview: grade D, 53.3/100, rank #103 of 121 open-weight models. Markdown https://www.anchorterminal.com/tools/ripple-65b-preview.md · JSON https://www.anchorterminal.com/api/v1/tools/ripple-65b-preview.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 3674 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

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

Good for: Teams with a multi-GPU server or a 128 GB Mac who want Qwen's newest sparse architecture for agentic coding and multimodal work, and aren't a hosted-model or coding-assistant business.

Ahead on:
- Reach, 65 against 45

Watch for: No build fits the reference machine's 107.4 GB, and Ollama's smallest Linux build is 120 GB

### Ripple-65B Preview (D)

Good for: Teams with Blackwell GPUs or a DGX Spark who want an Apache 2.0 coding model to try for terminal and agentic coding work, and can run the publisher's code to serve it.

Ahead on:
- Risk, 55 against 45
- Agent interface, 75 against 70
- Documentation and upkeep, 49 against 36

Watch for: No released runtime runs the architecture, so running it means Ricursive's Python in SGLang 0.5.19 or Transformers' remote code


## Why the edge

### Reach: Qwen3.8-Flash-Next +20

- Qwen3.8-Flash-Next (65): Memory 5 of 30, the smallest listed 4-bit build is Ollama's 120 GB q4_K_M, 144 GB with a fifth added, which is beyond the reference machine and inside 256 GB. Ollama's Apple-only 105 GB MLX build isn't among the builds and would land in the same band. Runtimes 25, released versions of llama.cpp, Ollama, vLLM, SGLang and Transformers all run the qwen4_exp architecture. Builds 10 of 15, Alibaba's only quantised build is FP8. Terms 10 of 15, a custom licence that's free for commercial use except for model-as-a-service and coding or office assistant businesses, which need a separate licence. Download 10, neither repository is gated. Hosted 5, Hugging Face lists two live providers, Featherless AI for the BF16 repository and Nscale for the FP8 build, and Alibaba's Qwen Cloud serves Qwen3.8-Flash, which the card says is based on these weights.
- Ripple-65B Preview (45): Memory 10 of 30, the smallest 4-bit build is Ricursive's NVFP4 at 57.2 GB, 68.7 GB with a fifth added, which is over 64 GB and fits our reference machine. Runtimes 0, no released runtime runs the ricursive architecture with its own code; Ricursive's SGLang plugin and Transformers' remote code don't count. Builds 10, one quantised precision (NVFP4) from Ricursive. Terms 15, Apache 2.0 with nothing added. Download 10, not gated. Hosted 0, Ricursive serves no API, Hugging Face maps no provider, Together AI's page says coming soon, and GMI Cloud's and OpenRouter's pages weren't read.

### Documentation and upkeep: Ripple-65B Preview +13

- Qwen3.8-Flash-Next (36): Model card 11 of 20, intended use, architecture, parameters and context are there, but limitations, languages and a knowledge cut-off aren't. Training data 0, not described beyond a token count relative to an earlier model. Last change 15, the FP8 config fix on 31 August 2026, 63 days before 2 November. Responsiveness 0, Qwen answered none of the 40 most recent questions and reports (29 to 79, 27 August to 26 October), its only reply came in the first week. Version policy 5, a first release with nothing replaced. File health 5, the files agree with the card, but a 19 October report about the template and a pull request to fix it still wait without a reply. 11 + 0 + 15 + 0 + 5 + 5 = 36.
- Ripple-65B Preview (49): Model card 14 of 20, no limitations and no knowledge cut-off. Training data 5, a general description with token counts and no cut-off. Last change 20, released today. Responsiveness 0, no discussions yet, read as none answered. Version policy 5, a first release, with the NVFP4 checkpoint replaced in place on release day under a commit note. File health 5, tokenizer_config.json carries a second, tool-less chat template and a 131,072-token limit against the card's 1M.

### Risk: Ripple-65B Preview +10

- Qwen3.8-Flash-Next (45): File safety 25, safetensors only and nothing from the repository runs to load it or build prompts. Provenance 15, the files are in the Qwen organisation that Qwen's GitHub links to, and every file has a checksum. Safety evaluation 0, none published on the card or in the technical report. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 5 of 15, 5 off for the field-of-use limit on model-as-a-service and coding or office assistant businesses and 5 off for the naming duty above a user or revenue threshold. Security programme 0, no security.txt, disclosure policy or security contact for Qwen was found, and Alibaba's group security centre couldn't be checked, so it's scored as absent.
- Ripple-65B Preview (55): File safety 15, safetensors only, less 10 because the documented way to run it loads Ricursive's Python. Provenance 15, the files are in the organisation ricursive.com links to, with a checksum on every file. Safety evaluation 10, Ricursive's own FORTRESS and StrongREJECT results. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 15, Apache 2.0. Security programme 0, no way to report a flaw.

### Agent interface: Ripple-65B Preview +5

- Qwen3.8-Flash-Next (70): Chat template 20, a Jinja file in the repository. Tool-calling format 10 of 25, the format lives only in the template and the parser name only in serving commands, and Alibaba doesn't describe how tools are declared or returned. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, not documented for the weights. Reasoning control 10, thinking turns off per request, reasoning_effort budgets it and the card shows the reasoning coming back in reasoning_content. Context 10, 262,144 tokens native.
- Ripple-65B Preview (75): Chat template 20, chat_template.jinja in the repository. Tool-calling format 25, Ricursive's server README says how tools are declared and calls come back, with the ripple parser in SGLang 0.5.19. Trained tool calling 20, one or more calls a turn and agentic-coding RL. Structured output 0, not documented. Reasoning control 0, the template has no thinking switch or effort levels. Context 10, long-context training at 2M-token sequences for a stated 1M.

## Score by category

| Category | Weight | Qwen3.8-Flash-Next | Ripple-65B Preview | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 65 | 45 | Qwen3.8-Flash-Next +20 |
| Risk | 20% (20 this run) | 45 | 55 | Ripple-65B Preview +10 |
| Agent interface | 10% (10 this run) | 70 | 75 | Ripple-65B Preview +5 |
| Documentation and upkeep | 10% (10 this run) | 36 | 49 | Ripple-65B Preview +13 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **61.8 · C** | **53.3 · D** | |

## Facts side by side

| Fact | Qwen3.8-Flash-Next | Ripple-65B Preview |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Alibaba (Qwen team) | Ricursive Intelligence |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Qwen Community License 1.0 (custom, not an OSI licence). Free of charge, the model name shown in products above 100M monthly users or US$20M monthly revenue, and a separate licence from Qwen before commercial use by a Model as a Service or AI Work Assistant business | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-08-31 | 2026-11-04 |
| Terms last updated | no document linked | not read yet |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 2/5 (4) | none |

## Verdicts

**Qwen3.8-Flash-Next.** A large sparse cousin with 6B active parameters for agentic coding and multimodal work, previewing Qwen's next architecture. It needs a 128 GB Mac for Ollama's MLX build or about 144 GB for the 4-bit GGUF, and Qwen Cloud serves it. The main risk is a licence that needs a separate deal for hosted-model and coding or office assistant businesses.

**Ripple-65B Preview.** A dense 65B coding model from a new lab, pretrained from scratch and released today under Apache 2.0, that Ricursive says leads U.S. open-weight models on Terminal-Bench 2.1. Nobody has checked that yet, and the custom architecture runs only through Ricursive's own SGLang plugin on Blackwell GPUs, with a 57 GB NVFP4 build as the smallest.

## Before you call either

### Qwen3.8-Flash-Next

1. On a 128 GB Mac start it with `ollama run qwen3.8-flash-next:125b-mlx` (105 GB, NVFP4), on Linux the smallest Ollama build is `qwen3.8-flash-next:125b-a6b-q4_K_M` at 120 GB, which won't fit a 107.4 GB GPU share
2. On a server, `vllm serve Qwen/Qwen3.8-Flash-Next-FP8 --tensor-parallel-size 4 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`
3. Use the qwen3_coder tool parser and the qwen3 reasoning parser, as Qwen's GitHub README does
4. Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false to skip it, and read the reasoning from `reasoning_content`, even though SGLang's cookbook says thinking can't be turned off
5. `reasoning_effort` takes xhigh (default), medium or low, and any other value makes the template raise an error
6. Send at most one system message and put it first, since the template raises an error otherwise
7. Sampling per the card is temperature 1.0 and top_p 0.95 when thinking, temperature 0.7, top_p 0.8 and presence_penalty 1.5 without
8. vLLM keeps the 51B n-gram table in host memory on 80 GB GPUs by default, per its recipe
9. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented
10. Read the Qwen Community License before serving it to others as an API or inside a coding or office assistant product

### Ripple-65B Preview

1. Run Ricursive's launcher from inference/server with `uv run --script serve.py` on two B200 GPUs; it listens on 127.0.0.1:30000 with a 16K context by default
2. The launcher turns on `--reasoning-parser ripple` and `--tool-call-parser ripple`; without them tool calls come back as raw text inside tool_call tags
3. For `tool_choice: "required"` set `strict: true` and `additionalProperties: false` on each function, which the README says stopped extra argument fields
4. Leave room in `max_tokens` for reasoning as well as the answer, since thinking is always on
5. Use chat_template.jinja, not the template inside tokenizer_config.json, which has no tool support

## Questions

### Which is better for AI agents, Qwen3.8-Flash-Next or Ripple-65B Preview?

Qwen3.8-Flash-Next scores 61.8 (C) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in 1 of 4 scored categories. Ripple-65B Preview leads on risk, agent interface and documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-ripple-65b-preview.json, and with the fewest tokens: https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-ripple-65b-preview.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "qwen3-8-flash-next", "b": "ripple-65b-preview"}`. From a terminal: `anchor compare qwen3-8-flash-next ripple-65b-preview`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/qwen3-8-flash-next.json and https://www.anchorterminal.com/api/v1/tools/ripple-65b-preview.json

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- [Qwen3.8-Flash-Next vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-30b-a3b-instruct.md)
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- [Qwen3.8-Flash-Next vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-smollm3-3b.md)
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- [Ripple-65B Preview vs SmolLM3-3B](https://www.anchorterminal.com/compare/ripple-65b-preview-vs-smollm3-3b.md)
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- [Ripple-65B Preview vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/ripple-65b-preview-vs-unlimited-ocr-2.md)

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