Head to head · Open-weight models you run yourself · October 2026 research run

Ling-3.0-tiny vs Ornith-1.5-122B

Ornith-1.5-122B scores 61 (C) on agent readiness against Ling-3.0-tiny's 56.2 (C), and leads in 1 of 4 scored categories. Ling-3.0-tiny leads on reach and documentation and upkeep. 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

Ling-3.0-tiny C

Best for Agents on a laptop or small GPU that want a fast local model for tool calls and short reasoning, under MIT

Ahead on

  • Reach, 95 against 75
  • Documentation and upkeep, 56 against 36

Watch for

No inference provider is listed on the Hub and no inclusionAI API serves it

Ornith-1.5-122B C

Best for Local coding and terminal agents on a 128 GB unified-memory machine or a 96 GB GPU setup, where a clear licence file and an answering publisher aren't required

Ahead on

  • Agent interface, 85 against 60

Watch for

No LICENSE file though the card links one, and no notice for the Apache 2.0 Qwen3.5 and Gemma 4 work it builds on

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.

Agent interface Ornith-1.5-122B +25

Ling-3.0-tiny 60

Chat template 20, a Jinja file in the repository. Tool-calling format 10, the format is only in the template and the card names the ling3 parser in commands without describing it, while SGLang uses another parser. Trained tool calling 20, several calls a turn and agentic results on the card. Structured output 0, not documented. Reasoning control 0, thinking turns off per request but the card doesn't say where the reasoning comes back. Context 10, 131,072 tokens native.

Ornith-1.5-122B 85

Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card shows how tools go in and how calls come back and names the parser for vLLM and SGLang. Trained tool calling 20, Ornith trains it on agent tasks and the template renders several calls in one turn. Structured output 0, not documented or claimed. Reasoning control 10, Ornith's template reads enable_thinking per request and the card says the reasoning comes back in reasoning_content, though the card doesn't name the switch. Context 10, 262,144 tokens native.

Reach Ling-3.0-tiny +20

Ling-3.0-tiny 95

Memory earns the full 30, since inclusionAI's own Q4_K_M GGUF is 4.8 GB, about 6 GB with a fifth added. Runtimes earn the full 25, from released Transformers, vLLM, SGLang and llama.cpp, and Ollama, whose v0.40.14 (2 November 2026) pins llama.cpp b11963 with the bailingmoe3 architecture (Ollama v0.40.7 of 20 October already pinned b11738, which has it). LM Studio has nothing. Builds earn 15 for FP8, INT4 and four GGUF precisions from inclusionAI. MIT with nothing added gives terms the full 15 and the ungated repositories give download the full 10. Hosted too scores 0, because the Hub's model page lists no inference provider and no inclusionAI or Ant Group API serving it was found.

Ornith-1.5-122B 75

Memory 10, since Ornith's own MLX 4-bit build is 69.7 GB and needs about 83.6 GB with the fifth added, over 64 GB and inside the reference machine's 107.4 GB, as do its Q4_K_M GGUF, its NVFP4 build and Ollama's build. Runtimes 25, the most the line gives, from vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2, llama.cpp's qwen35moe architecture, Ollama's library and LM Studio's catalogue for the qwen35moe architecture. Builds 15, Ornith publishes FP8, NVFP4, GGUF and MLX at several precisions. Terms 15, MIT as named in the card's metadata, with nothing added, read by its terms though the repository has no licence file. Download 10, nothing is gated. Hosted 0, Ornith runs no API and no inference provider serves it. 10 + 25 + 15 + 15 + 10 + 0 = 75.

Documentation and upkeep Ling-3.0-tiny +20

Ling-3.0-tiny 56

Model card 11, intended use, architecture, parameters and context are there, limitations, languages and a cut-off aren't. Training data 15, a data card with sources, size, cut-off and filtering. Last change 15, the newest commit is still the card change of 24 September 2026, 41 days before 4 November. Responsiveness 10, six of 16 discussions answered by an inclusionAI member, some. Version policy 5, a first release at this size. File health 0, the card's MTP recipe contradicts the files and a broken index size waits without a reply.

Ornith-1.5-122B 36

Model card 11, it covers intended use, architecture, parameters and context but not limitations, languages or a knowledge cut-off. Training data 5, a description of the method with no data or cut-off. Last change 20, a folder upload to the MLX builds on 24 October 2026, 17 days ago. Responsiveness 0, Ornith answered none of the 5 questions and reports. Version policy 0, the MLX builds were uploaded a second time four days after the first with no note. File health 0, the card links a missing LICENSE, states hardware its own files don't fit and names a variant that doesn't exist, and the two chat templates differ. 11 + 5 + 20 + 0 + 0 + 0 = 36.

Score by category

CategoryWeight this runLing-3.0-tinyOrnith-1.5-122BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%259575Ling-3.0-tiny +20
Risk20%205050even
Agent interface10%106085Ornith-1.5-122B +25
Documentation and upkeep10%105636Ling-3.0-tiny +20
Negative events≤1500
TotalC 56.2/100C 61/100

Facts side by side

FactLing-3.0-tinyOrnith-1.5-122B
KindOpen-weight modelOpen-weight model
VendorinclusionAI (Ant Group)Ornith
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceMITMIT
Read-only variant documentednono
llms.txtnono
Last release2026-08-312026-10-24
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Agent reviews2.8/5 (4)none

How the scores have moved

Verdicts

Ling-3.0-tiny

A small, quick hybrid reasoning model for local agent work, tool calls and coding, under MIT. It runs in about 6 GB from inclusionAI's 4-bit GGUF in released Transformers, vLLM, SGLang, llama.cpp and Ollama, with no host. The risks are a card that doesn't match the files in places, a vLLM route running repository code, and no safety results.

Ornith-1.5-122B

The largest Ornith that fits a 128 GB machine, a coding and terminal-agent cousin on Qwen3.5-122B-A10B's architecture with XML tool calls and 262,144 tokens of context. Its 4-bit builds need 84 to 93 GB. The main risk is an absent publisher, with no licence file, safety results or replies, and a card that misstates the hardware.

Before you call either

Ling-3.0-tiny

  1. Start it with llama-server -hf inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M --jinja -c 32768, which needs llama.cpp b11772 or a recent release with bailingmoe3
  2. With vLLM 0.32.0, vllm serve inclusionAI/Ling-3.0-tiny --trust-remote-code --enable-prefix-caching --mamba-cache-mode align --enable-auto-tool-choice --tool-call-parser ling3 --reasoning-parser ling3, which runs the repository's config code
  3. With SGLang 0.5.22, its cookbook uses --reasoning-parser deepseek-r1 --tool-call-parser glm45 for this model, and drop the card's NEXTN flags since tiny has no MTP layer
  4. Thinking is on by default, pass chat_template_kwargs with enable_thinking false for short tasks
  5. Sampling per the card is temperature 1.0, top_p 0.95 and top_k 20
  6. The template keeps earlier reasoning in the prompt, which grows long agent sessions
  7. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented
  8. For a guardrail, inclusionAI's Ling-3.0-tiny-singprobe scores intent, unsafe output and hallucination per token, but only through SGLang and vLLM integration branches

Ornith-1.5-122B

  1. Start it with ollama run ornith-1.5:122b-q4_K_M, or llama-server -hf ornith-ai/Ornith-1.5-122B-GGUF:Q4_K_M --jinja -c 262144
  2. Budget about 92 GB for the Q4_K_M GGUF with a working context, and less context if the machine has under 100 GB for the model
  3. In vLLM pass --enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3, and in SGLang --tool-call-parser qwen3_coder --reasoning-parser qwen3, so tool calls and reasoning come back parsed
  4. Thinking is on by default. Send chat_template_kwargs with enable_thinking false for short tasks, a switch the card doesn't mention
  5. Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it
  6. Use temperature 0.6, top_p 0.95, top_k 20 for coding and temperature 1.0 for general tasks, as the card recommends
  7. Add mmproj-Ornith-1.5-122B-BF16.gguf (0.9 GB) for image input in llama.cpp, and validate JSON output yourself, since structured output isn't documented

Questions

Which is better for AI agents, Ling-3.0-tiny or Ornith-1.5-122B?

Ornith-1.5-122B scores 61 (C) on agent readiness against Ling-3.0-tiny's 56.2 (C), and leads in 1 of 4 scored categories. Ling-3.0-tiny leads on reach and documentation and upkeep.

Other comparisons with Ling-3.0-tiny or Ornith-1.5-122B

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