# Arete-27B vs Qwen3.8-Flash-Next

> Qwen3.8-Flash-Next scores 61.8 (C) to Arete-27B's 55.1 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-8-flash-next
- Markdown: https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-8-flash-next.md (~9,450 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

Qwen3.8-Flash-Next scores 61.8 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in 2 of 4 scored categories. Arete-27B leads on risk and documentation and upkeep. Both do open-weight models you run yourself.

- Arete-27B: grade C, 55.1/100, rank #123 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/arete-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/arete-27b.json
- Qwen3.8-Flash-Next: grade C, 61.8/100, rank #34 of 160 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
- 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

### Arete-27B (C)

Good for: Agents that want Qwen3.8-27B's coding and tool use without its handling of politically sensitive topics, on a 24 GB card or a 128 GB machine

Ahead on:
- Risk, 55 against 45
- Documentation and upkeep, 63 against 36

Watch for: No quantised build from Nous Research, so the GGUF route depends on a third party

### 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 50
- Agent interface, 70 against 60

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


## Why the edge

### Documentation and upkeep: Arete-27B +27

- Arete-27B (63): Model card 8, no limitations, context length, languages or cut-off. Training data 20, the training pairs and the evaluation set are published with their counts. Last change 20, a commit on 6 November 2026. Responsiveness 0, none of 13 discussions has a reply from Nous Research. Version policy 5, a first release. File health 10, the files agree. 8 + 20 + 20 + 0 + 5 + 10 = 63.
- 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.

### Reach: Qwen3.8-Flash-Next +15

- Arete-27B (50): Memory 10, the only build from Nous Research is the 54.7 GB BF16 release, which needs about 66 GB and fits the reference machine. Runtimes 15, vLLM, SGLang and Transformers run the base's architecture from those files, and llama.cpp isn't credited because Nous Research publishes no GGUF. Builds 0, no quantised build of its own. Terms 15, Apache 2.0. Download 10, not gated. Hosted 0, no provider. 10 + 15 + 0 + 15 + 10 + 0 = 50.
- 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.

### Risk: Arete-27B +10

- Arete-27B (55): File safety 25, safetensors only and no remote code. Provenance 15, Nous Research's own organisation, linked from its blog, with a hash on every file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 15, plain Apache 2.0. Security programme 0, no way to report a flaw found. 25 + 15 + 0 + 0 + 15 + 0 = 55.
- 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.

### Agent interface: Qwen3.8-Flash-Next +10

- Arete-27B (60): Chat template 20, a Jinja file in the repository. Tool-calling format 10, only the template says it. Trained tool calling 20, several calls a turn and agentic work as an intended use. Structured output 0 and reasoning control 0, neither is documented on the card. Context 10, 262,144 tokens native. 20 + 10 + 20 + 0 + 0 + 10 = 60.
- 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.

## Score by category

| Category | Weight | Arete-27B | Qwen3.8-Flash-Next | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 50 | 65 | Qwen3.8-Flash-Next +15 |
| Risk | 20% (20 this run) | 55 | 45 | Arete-27B +10 |
| Agent interface | 10% (10 this run) | 60 | 70 | Qwen3.8-Flash-Next +10 |
| Documentation and upkeep | 10% (10 this run) | 63 | 36 | Arete-27B +27 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **55.1 · C** | **61.8 · C** | |

## Facts side by side

| Fact | Arete-27B | Qwen3.8-Flash-Next |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Nous Research | Alibaba (Qwen team) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | 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 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-11-06 | 2026-08-31 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 2/5 (4) |

## Verdicts

**Arete-27B.** Qwen3.8-27B with its answers on politically sensitive topics retrained, four days old when read. The training data is published, which is rare. Nous Research ships BF16 only, so a small machine runs it from bartowski's GGUF, and the card documents none of the tool or thinking controls it inherits.

**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.

## Before you call either

### Arete-27B

1. Start it with `llama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja`, a third party's conversion, or serve the BF16 files with vLLM
2. Use the base's parsers in vLLM, `--reasoning-parser qwen3 --tool-call-parser qwen3_coder`, and check tool calls first, since the template differs slightly from Qwen3.8-27B's
3. Thinking switches with `enable_thinking` in the template. The card's own evaluation ran with it off
4. Expect answers on politically sensitive topics to differ from Qwen3.8-27B's. That is what the fine-tune is for
5. Validate JSON output yourself, since structured output isn't documented

### 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

## Questions

### Which is better for AI agents, Arete-27B or Qwen3.8-Flash-Next?

Qwen3.8-Flash-Next scores 61.8 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in 2 of 4 scored categories. Arete-27B leads on risk and documentation and upkeep.


## For agents

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

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- [Arete-27B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-2b-instruct.md)
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- [Arete-27B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-32b-instruct.md)
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- [Arete-27B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-8b-instruct.md)
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- [Arete-27B vs Recursive Flint](https://www.anchorterminal.com/compare/arete-27b-vs-recursive-flint.md)
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- [Arete-27B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/arete-27b-vs-thinkingcap-qwen3-8-27b.md)
- [Arete-27B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/arete-27b-vs-unlimited-ocr-2.md)
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- [LFM3-VL-2B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-8-flash-next.md)
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- [Ling-3.1-flash-1019 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-8-flash-next.md)
- [LongCat-2.5 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-8-flash-next.md)
- [MiMo-V2.6 Distill Qwen 9B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen3-8-flash-next.md)
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- [MiniCPM-V 4.7 1B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-8-flash-next.md)
- [MiniCPM-V 4.7 35B-A3B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-8-flash-next.md)
- [MiniCPM5-2B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-8-flash-next.md)
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- [Mistral Large 4 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-8-flash-next.md)
- [Muse Glimmer 30B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-8-flash-next.md)
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- [Nanbeige4.3-33B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-8-flash-next.md)
- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-8-flash-next.md)
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- [Nemotron 3 Nano 30B A3B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-8-flash-next.md)
- [Nemotron 3 Nano 4B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-8-flash-next.md)
- [Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-8-flash-next.md)
- [Nemotron 3 Super 120B A12B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-8-flash-next.md)
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- [occamy-1.0 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-8-flash-next.md)
- [Olmo 3 7B Think vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-8-flash-next.md)
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- [Ornith-1.5-397B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-8-flash-next.md)
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- [Qwen-AgentWorld-35B-A3B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-8-flash-next.md)
- [Qwen3-30B-A3B-Instruct-2507 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-8-flash-next.md)
- [Qwen3-4B-Instruct-2507 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-8-flash-next.md)
- [Qwen3.5-0.8B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-8-flash-next.md)
- [Qwen3.5-122B-A10B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-8-flash-next.md)
- [Qwen3.5-2B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-8-flash-next.md)
- [Qwen3.5-397B-A17B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-8-flash-next.md)
- [Qwen3.5-4B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-8-flash-next.md)
- [Qwen3.5-9B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-8-flash-next.md)
- [Qwen3.6-27B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-8-flash-next.md)
- [Qwen3.6-35B-A3B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-8-flash-next.md)
- [Qwen3.8-27B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-8-flash-next.md)
- [Qwen3.8-Flash-Next vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-coder-30b-a3b-instruct.md)
- [Qwen3.8-Flash-Next vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-coder-next.md)
- [Qwen3.8-Flash-Next vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-omni-30b-a3b-instruct.md)
- [Qwen3.8-Flash-Next vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-2b-instruct.md)
- [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)
- [Qwen3.8-Flash-Next vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-32b-instruct.md)
- [Qwen3.8-Flash-Next vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-4b-instruct.md)
- [Qwen3.8-Flash-Next vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-8b-instruct.md)
- [Qwen3.8-Flash-Next vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwopus3-8-27b-flash-v2.md)
- [Qwen3.8-Flash-Next vs Recursive Flint](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-recursive-flint.md)
- [Qwen3.8-Flash-Next vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-ripple-65b-preview.md)
- [Qwen3.8-Flash-Next vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-sensenova-loop-9b-preview.md)
- [Qwen3.8-Flash-Next vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-smollm3-3b.md)
- [Qwen3.8-Flash-Next vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-spark-x2-6-4b.md)
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- [Qwen3.8-Flash-Next vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-swift-1-5-qwen3-8-27b.md)
- [Qwen3.8-Flash-Next vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-swift-1-5-qwen3-8-flash-next.md)
- [Qwen3.8-Flash-Next vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-thinkingcap-qwen3-8-27b.md)
- [Qwen3.8-Flash-Next vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-unlimited-ocr-2.md)
- [Qwen3.8-Flash-Next vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-wevisone-vl-2b.md)
- [Qwen3.8-Flash-Next vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-wevisone-vl-4b.md)
- [Qwen3.8-Flash-Next vs ZAYA1-8B](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-zaya1-8b.md)

---

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