Head to head · Open-weight models you run yourself · October 2026 research run
Ling-3.1-flash-1019 vs occamy-1.0
occamy-1.0 scores 61 (C) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in every scored category. 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 Teams with a multi-GPU server that want an MIT-licensed flagship for long agent runs and coding
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
574 GB in FP8 with no smaller build from inclusionAI, so no single machine under 690 GB of GPU memory runs it
Best for Agents that want an Apache 2.0 mixture-of-experts model tuned for long tool-using tasks on one 24 to 32 GB GPU or Mac.
Ahead on
- Reach, 90 against 60
- Risk, 55 against 45
- Agent interface, 70 against 60
- Documentation and upkeep, 74 against 56
Watch for
No safety results, prompt-injection guidance or security contact
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 occamy-1.0 +30
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.
occamy-1.0 90
Memory 25 of 30, Accio's MLX mxfp4 build is 18.4 GB, 22.1 GB with a fifth added, and its IQ4_XS GGUF needs 22.5 GB, both within 24 GB. Runtimes 25, the most the line gives, it keeps the base's qwen3_5_moe architecture, which llama.cpp, vLLM, SGLang and Transformers run in released versions, and Ollama v0.40.14 builds a llama.cpp (b11963) that has it. Builds 15, FP8, NVFP4, GGUF and MLX from Accio. Terms 15, Apache 2.0 like its base. Download 10, nothing is gated. Hosted 0, no API or inference provider serves it. 25 + 25 + 15 + 15 + 10 + 0 = 90.
Documentation and upkeep occamy-1.0 +18
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.
occamy-1.0 74
Model card 14 of 20, languages and a knowledge cut-off are missing. Training data 15, the mix by type with token counts, the method and a published subset, without a cut-off. Last change 20, Accio's GGUF build card changed on 19 October 2026 and the main card on 2 October. Responsiveness 10, Accio answered 4 of 9 questions. Version policy 5, a first release with no files replaced. File health 10, the files agree with the card. 14 + 15 + 20 + 10 + 5 + 10 = 74.
Risk occamy-1.0 +10
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.
occamy-1.0 55
File safety 25, safetensors only and nothing from the repository runs to load it or build prompts. Provenance 15, the files are in the organisation Accio's GitHub links, with MD5 sums and SHA-256 for every file. Safety evaluation 0, none published. Agent guidance 0. Licence risk 15, plain Apache 2.0 that hasn't changed. Security programme 0, bug reports go to public GitHub issues and there's no security contact.
Agent interface occamy-1.0 +10
Ling-3.1-flash-1019 60
Chat template 20, chat_template.jinja in the repository (6,042 characters), which handles tools, the thinking switch and preserved reasoning. Tool-calling format 10, only the template says how tools are declared (JSON in <tools>) and how calls come back (<tool_call>name with <arg_key> and <arg_value> pairs). Trained tool calling 20, the template tells the model it may call one or more functions and loops over every entry in an assistant message's tool_calls, and the card reports gains on agentic benchmarks, AA-Briefcase and AutomationBench-AA among them. Structured output 0, the card doesn't document JSON or JSON Schema output. Reasoning control 0, the card documents turning thinking on or off per request with chat_template_kwargs enable_thinking, and names the ling3 reasoning parser, but doesn't say where the reasoning text comes back. Context 10, 262,144 tokens natively in config.json (max_position_embeddings 262144, rope_scaling null) and per SGLang's cookbook. 20 + 10 + 20 + 0 + 0 + 10 = 60.
occamy-1.0 70
Chat template 20, the base's Jinja template in the repository. Tool-calling format 10 of 25, the card names the parser but the format lives only in the template. Trained tool calling 20, trained on tool use with several calls a turn. Structured output 0, not documented. Reasoning control 10, enable_thinking per request and the card says to keep the returned reasoning content. Context 10, 262,144 tokens.
Score by category
| Category | Weight this run | Ling-3.1-flash-1019 | occamy-1.0 | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 60 | 90 | occamy-1.0 +30 |
| Risk | 20%20 | 45 | 55 | occamy-1.0 +10 |
| Agent interface | 10%10 | 60 | 70 | occamy-1.0 +10 |
| Documentation and upkeep | 10%10 | 56 | 74 | occamy-1.0 +18 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 54.8/100 | C 61/100 |
Facts side by side
| Fact | Ling-3.1-flash-1019 | occamy-1.0 |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | inclusionAI (Ant Group) | Accio (Accio-Lab) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-11-04 | 2026-10-02 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 3/5 (4) |
How the scores have moved
Ling-3.1-flash-1019
No score change since it was listed.
Verdicts
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.
occamy-1.0
A Qwen3.6-35B-A3B fine-tuned for long agent work with tools, files and code, under plain Apache 2.0, and Accio's 4-bit builds run in about 23 GB. The main gaps are no safety results, agent guidance or security contact, and every benchmark figure is Accio's own.
Before you call either
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
occamy-1.0
- Serve with
--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coderon vLLM 0.19.0 or SGLang 0.5.10 and later - BF16 is 70.2 GB, so on a 128 GB machine run it without the card's 8-way tensor parallelism
- Keep the whole assistant message, reasoning and tool calls included, in later turns, as the card says
- Sampling per the card is temperature 1.0, top_p 0.95, top_k 20 and presence_penalty 1.5
- With the GGUFs, NFC-normalise input text and read TOKENIZER.md, since the files store qwen2 pre-tokenizer metadata
- The base's MTP layer isn't in this checkpoint, Accio-Lab/occamy-1.0-MTP is an experimental separate head
- Ollama has no Occamy library entry, so load Accio's GGUF in Ollama 0.40.14 or later, which builds a llama.cpp with the qwen35moe architecture
Questions
Which is better for AI agents, Ling-3.1-flash-1019 or occamy-1.0?
occamy-1.0 scores 61 (C) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in every scored category.
Other comparisons with Ling-3.1-flash-1019 or occamy-1.0
- Agents-A1-4B vs Ling-3.1-flash-1019
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- Arete-27B vs occamy-1.0
- BigBang-v1 vs Ling-3.1-flash-1019
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- Carbon-X vs Ling-3.1-flash-1019
- Carbon-X vs occamy-1.0
- Cosmos-Reason2-2B vs Ling-3.1-flash-1019
- Cosmos-Reason2-2B vs occamy-1.0
- Darwin-36B-Opus vs Ling-3.1-flash-1019
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- DeepSeek-V3.2 vs Ling-3.1-flash-1019
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- DeepSeek V4.1 Flash vs Ling-3.1-flash-1019
- DeepSeek V4.1 Flash vs occamy-1.0
- DeepSeek-V4-Flash-0731 vs Ling-3.1-flash-1019
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- DiffusionGemma 26B A4B IT vs Ling-3.1-flash-1019
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- Gemma 4 12B IT vs Ling-3.1-flash-1019
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- Gemma 4 26B A4B IT vs Ling-3.1-flash-1019
- Gemma 4 26B A4B IT vs occamy-1.0
- Gemma 4 31B IT vs Ling-3.1-flash-1019
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- Gemma 4 E4B IT vs occamy-1.0
- GLM-4.5-Air vs Ling-3.1-flash-1019
- GLM-4.5-Air vs occamy-1.0
- GLM-4.7-Flash vs Ling-3.1-flash-1019
- GLM-4.7-Flash vs occamy-1.0
- GLM-5.3-Flash vs Ling-3.1-flash-1019
- GLM-5.3-Flash vs occamy-1.0
- GLM-5.3 vs Ling-3.1-flash-1019
- GLM-5.3 vs occamy-1.0
- gpt-oss-120b vs Ling-3.1-flash-1019
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- gpt-oss-20b vs Ling-3.1-flash-1019
- gpt-oss-20b vs occamy-1.0
- Granite 4.2 30B vs Ling-3.1-flash-1019
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- Granite 4.2 3B vs occamy-1.0
- Granite 4.2 8B vs Ling-3.1-flash-1019
- Granite 4.2 8B vs occamy-1.0
- Hy-Memory-30B-A3B vs Ling-3.1-flash-1019
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- Hy3 vs Ling-3.1-flash-1019
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- Kolibri-1 vs Ling-3.1-flash-1019
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- Kronos vs Ling-3.1-flash-1019
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- Laguna M.1 vs Ling-3.1-flash-1019
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- Laguna S 2.1 vs Ling-3.1-flash-1019
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- LFM3-Audio-2B vs Ling-3.1-flash-1019
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- Ling-3.1-flash-1019 vs ThinkingCap (Qwen3.8 27B)
- Ling-3.1-flash-1019 vs Baidu Unlimited-OCR-2
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- MiniCPM-o 4.5 vs occamy-1.0
- MiniCPM-V 4.7 1B vs occamy-1.0
- MiniCPM-V 4.7 35B-A3B vs occamy-1.0
- MiniCPM5-2B vs occamy-1.0
- MiniMax-M2.5 vs occamy-1.0
- MiniMax-M2.7 vs occamy-1.0
- MiniMax-M3.1-Flash vs occamy-1.0
- Mistral Large 4 vs occamy-1.0
- Muse Glimmer 30B vs occamy-1.0
- Nanbeige4.3-33B vs occamy-1.0
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs occamy-1.0
- Nemotron 3.5 Lightning 30B A3B vs occamy-1.0
- Nemotron 3 Nano 30B A3B vs occamy-1.0
- Nemotron 3 Nano 4B vs occamy-1.0
- Nemotron 3 Nano Omni 30B A3B Reasoning vs occamy-1.0
- Nemotron 3 Super 120B A12B vs occamy-1.0
- Nemotron 3 Ultra 550B A55B vs occamy-1.0
- occamy-1.0 vs Olmo 3 7B Think
- occamy-1.0 vs Ornith-1.5-122B
- occamy-1.0 vs Ornith-1.5-35B-A3B
- occamy-1.0 vs Ornith-1.5-397B
- occamy-1.0 vs Ornith-1.5-9B
- occamy-1.0 vs Qwen-AgentWorld-35B-A3B
- occamy-1.0 vs Qwen3-30B-A3B-Instruct-2507
- occamy-1.0 vs Qwen3-4B-Instruct-2507
- occamy-1.0 vs Qwen3.5-0.8B
- occamy-1.0 vs Qwen3.5-122B-A10B
- occamy-1.0 vs Qwen3.5-2B
- occamy-1.0 vs Qwen3.5-397B-A17B
- occamy-1.0 vs Qwen3.5-4B
- occamy-1.0 vs Qwen3.5-9B
- occamy-1.0 vs Qwen3.6-27B
- occamy-1.0 vs Qwen3.6-35B-A3B
- occamy-1.0 vs Qwen3.8-27B
- occamy-1.0 vs Qwen3.8-Flash-Next
- occamy-1.0 vs Qwen3-Coder-30B-A3B-Instruct
- occamy-1.0 vs Qwen3-Coder-Next
- occamy-1.0 vs Qwen3-Omni-30B-A3B-Instruct
- occamy-1.0 vs Qwen3-VL-2B-Instruct
- occamy-1.0 vs Qwen3-VL-30B-A3B-Instruct
- occamy-1.0 vs Qwen3-VL-32B-Instruct
- occamy-1.0 vs Qwen3-VL-4B-Instruct
- occamy-1.0 vs Qwen3-VL-8B-Instruct
- occamy-1.0 vs Qwopus3.8-27B-Flash-V2
- occamy-1.0 vs Recursive Flint
- occamy-1.0 vs Ripple-65B Preview
- occamy-1.0 vs SmolLM3-3B
- occamy-1.0 vs Spark-X2.6-4B
- occamy-1.0 vs Swift 1.5 Qwen3.8-27B
- occamy-1.0 vs Swift 1.5 Qwen3.8-Flash-Next
- occamy-1.0 vs ThinkingCap (Qwen3.8 27B)
- occamy-1.0 vs Baidu Unlimited-OCR-2
Machine-readable
- This page as Markdown
/compare/ling-3-1-flash-1019-vs-occamy-1-0.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/ling-3-1-flash-1019.json·/api/v1/tools/occamy-1-0.json - From a terminal
anchor compare ling-3-1-flash-1019 occamy-1-0(the CLI) - Over MCP
compare_tools {"a": "ling-3-1-flash-1019", "b": "occamy-1-0"}at/mcp, no key