Head to head · Open-weight models you run yourself · October 2026 research run
Arete-27B vs Ling-3.1-flash-1019
Arete-27B and Ling-3.1-flash-1019 score within a point of each other on agent readiness, 55.1 (C) and 54.8 (C). Ling-3.1-flash-1019 leads on reach. Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 4969 open weights comparisons
Which one, for what
Best 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 56
Watch for
No quantised build from Nous Research, so the GGUF route depends on a third party
Best for Teams with a multi-GPU server that want an MIT-licensed flagship for long agent runs and coding
Ahead on
- Reach, 60 against 50
Watch for
574 GB in FP8 with no smaller build from inclusionAI, so no single machine under 690 GB of GPU memory runs it
Why the edge
Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.
Reach Ling-3.1-flash-1019 +10
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.
Ling-3.1-flash-1019 60
Memory 0, inclusionAI's only build is the FP8 release at 573.78 GB, × 1.2 = 688.5 GB, above 256 GB, and no runtime's library has a build. Runtimes 25, Released model code in Transformers 5.22.2 (bailing_hybrid), vLLM 0.33.0 (BailingMoeV3ForCausalLM, with ling3 tool and reasoning parsers), SGLang 0.5.23 (bailing_moe_v3.py, its bailing_hybrid config and MTP module) and llama.cpp b12242 (bailingmoe3), and Ollama, credited through its pinned llama.cpp as on the Ling-3.0-tiny listing: v0.40.16 sets LLAMA_CPP_VERSION to b12177. Builds 10, the release is FP8, the one quantised precision inclusionAI publishes for it. Terms 15, MIT in the card's metadata, with no use policy or other terms added on the card or in the repository. Download 10, the Hub API's model info gives gated false on 10 November 2026, so the files download with no account and no terms to accept. Hosted 0, the Hub API's provider mapping is empty for this repository on 10 November 2026. 0 + 25 + 10 + 15 + 10 + 0 = 60.
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.
Ling-3.1-flash-1019 45
File safety 15, safetensors only, no .bin, .pt or .pth files (25), less 10 because the card's documented vLLM command passes --trust-remote-code, which runs the repository's configuration_bailing_moe_v3.py, since vLLM 0.33.0 registers no bailing_hybrid config of its own. Provenance 15, files in the inclusionAI organisation that inclusion-ai.org links to, per the Ling-3.0-tiny listing (10). Safety evaluation 0, no safety results for the model on its card or in the training-content summary. Agent guidance 0, the card has nothing on prompt injection, refusals or safeguards, and inclusionAI publishes no guardrail probe for this model as it did for Ling 3.0. Licence risk 15, MIT with no use policy, no field-of-use limits, no threshold and no clause letting inclusionAI change or revoke it. Security programme 0, www.inclusion-ai.org answers 404 for security.txt on 10 November 2026, and the Ling-3.0-tiny listing found no SECURITY.md or disclosure policy in the inclusionAI .github repository. 15 + 15 + 0 + 0 + 15 + 0 = 45.
Documentation and upkeep Arete-27B +7
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.
Ling-3.1-flash-1019 56
Model card 11, Intended use (complex reasoning, multi-step tasks and agentic workflows), architecture, parameters (560B total, 25B active) and context (1M claimed) are on the card. Training data 15, the training-content summary the card links names the source categories (Common Crawl and other public datasets, commercially licensed data, the Ant Spider crawler, synthetic data), the size band (over 10 trillion text tokens), data collected up to about July 2026 and the filtering, without listing datasets with sizes. Last change 20, the newest commit, on 4 November 2026, edited the card, six days before this check, and the files went up on 27 October. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (both opened on 27 October 2026) has a reply from an inclusionAI member. Version policy 5, the update went into a repository of its own and Ling-3.1-flash and Ling 3.0 stay in theirs. File health 5, the card states a 1M-token context while config.json gives 262,144 with no rope scaling, and its serve commands name the earlier repository as the model path. 11 + 15 + 20 + 0 + 5 + 5 = 56.
Score by category
| Category | Weight this run | Arete-27B | Ling-3.1-flash-1019 | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 50 | 60 | Ling-3.1-flash-1019 +10 |
| Risk | 20%20 | 55 | 45 | Arete-27B +10 |
| Agent interface | 10%10 | 60 | 60 | even |
| Documentation and upkeep | 10%10 | 63 | 56 | Arete-27B +7 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 55.1/100 | C 54.8/100 |
Facts side by side
| Fact | Arete-27B | Ling-3.1-flash-1019 |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Nous Research | inclusionAI (Ant Group) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | MIT |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-11-06 | 2026-11-04 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
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.
Ling-3.1-flash-1019
inclusionAI's newest flagship, a 560B hybrid mixture of experts under plain MIT that vLLM, SGLang, Transformers and llama.cpp run in released versions. It ships only as 574 GB of FP8, so it needs a multi-GPU server. The card overstates the context, documents little about tool calls, and has no safety results.
Before you call either
Arete-27B
- 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 - 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 - Thinking switches with
enable_thinkingin the template. The card's own evaluation ran with it off - Expect answers on politically sensitive topics to differ from Qwen3.8-27B's. That is what the fine-tune is for
- Validate JSON output yourself, since structured output isn't documented
Ling-3.1-flash-1019
- Serve it with SGLang 0.5.23 or later on 8 H200 or 4 GB300 GPUs, per the card, and use this repository's name as the model path, not the earlier one the card's command shows
- With vLLM pass
--trust-remote-code,--tool-call-parser ling3and--reasoning-parser ling3 - Set
chat_template_kwargsenable_thinkingfalse for direct answers. Reasoning comes back inreasoning_contentper SGLang's cookbook - Plan on 262,144 tokens of context. The 1M figure needs the YaRN override in SGLang's cookbook
- Use temperature 1.0, top_p 0.95 and top_k 20, per the card
- Validate JSON output yourself, since structured output isn't documented
Questions
Which is better for AI agents, Arete-27B or Ling-3.1-flash-1019?
Arete-27B and Ling-3.1-flash-1019 score within a point of each other on agent readiness, 55.1 (C) and 54.8 (C). Ling-3.1-flash-1019 leads on reach.
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Machine-readable
- This page as Markdown
/compare/arete-27b-vs-ling-3-1-flash-1019.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/arete-27b.json·/api/v1/tools/ling-3-1-flash-1019.json - From a terminal
anchor compare arete-27b ling-3-1-flash-1019(the CLI) - Over MCP
compare_tools {"a": "arete-27b", "b": "ling-3-1-flash-1019"}at/mcp, no key