Head to head · Open-weight models you run yourself · October 2026 research run
InternVL3.5-GPT-OSS-20B-A4B-Preview vs Ornith-1.5-122B
Ornith-1.5-122B scores 61 (C) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 53.2 (D), and leads in 3 of 4 scored categories. InternVL3.5-GPT-OSS-20B-A4B-Preview leads on risk. Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 6359 open weights comparisons
Which one, for what
InternVL3.5-GPT-OSS-20B-A4B-Preview D
Best for Agents with 64 GB to spare that want an Apache-licensed image and video reader with optional thinking and can live with a stale preview.
Ahead on
- Risk, 55 against 50
Watch for
No quantised build from OpenGVLab, so about 51 GB at BF16
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
- Reach, 75 against 65
- Agent interface, 85 against 45
- Documentation and upkeep, 36 against 28
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 +40
InternVL3.5-GPT-OSS-20B-A4B-Preview 45
Chat template 20, a Jinja file in the repository, though it doesn't match the tokenizer. Tool-calling format 10 of 25, only the twin's inherited harmony template mentions tools. Trained tool calling 0, no evidence it was trained to call tools. Structured output 0. Reasoning control 10, a system prompt turns thinking on and the card says the reasoning comes back in think tags. Context 5, trained at 32K tokens.
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 Ornith-1.5-122B +10
InternVL3.5-GPT-OSS-20B-A4B-Preview 65
Memory 15 of 30, the only builds are BF16 at 42.5 GB by the repository's safetensors index, or 51 GB with a fifth added, which fits 64 GB. Runtimes 25, Transformers and vLLM load this repository, and SGLang, llama.cpp and Ollama (through llama.cpp b11963, which Ollama v0.40.14 pins) run the same weights in their GitHub format. Builds 0, OpenGVLab publishes no quantised build. Terms 15, Apache 2.0 with nothing added by the bases. Download 10, not gated. Hosted 0, no provider serves it.
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 Ornith-1.5-122B +8
InternVL3.5-GPT-OSS-20B-A4B-Preview 28
Model card 8 of 20, the family card misses limitations, context, languages and a cut-off. Training data 5, described without a cut-off. Last change 0, the last commit to either repository is 8 September 2025 and to OpenGVLab's reference code 22 September 2025, over a year before 4 November 2026. Responsiveness 10, OpenGVLab answered 3 of 8 questions across the two repositories, none since September 2025. Version policy 5, a first release. File health 0, the template uses markers the tokenizer doesn't have and contradicts the twin's template, and the card names the wrong language model in its licence line.
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.
Risk InternVL3.5-GPT-OSS-20B-A4B-Preview +5
InternVL3.5-GPT-OSS-20B-A4B-Preview 55
File safety 25, safetensors only and no code in the repository. Provenance 15, the files are in the OpenGVLab organisation its GitHub links, with a checksum on every file. Safety evaluation 0 and agent guidance 0, nothing published. Licence risk 15, Apache 2.0 over Apache and MIT bases, with gpt-oss's usage policy asking only for lawful use. Security programme 0, no policy, security.txt or advisories.
Ornith-1.5-122B 50
File safety 25, safetensors and GGUF only and no Python in any of Ornith's repositories for this size, so the card's --trust-remote-code flag loads nothing from the repository. Provenance 15, the files come from the ornith-ai organisation Ornith's blog links to, with a SHA-256 for every weight file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 10, MIT carries no policy, threshold or changeable terms, but the card's licence link is broken. Security programme 0, no security.txt, disclosure policy or security contact.
Score by category
| Category | Weight this run | InternVL3.5-GPT-OSS-20B-A4B-Preview | Ornith-1.5-122B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 65 | 75 | Ornith-1.5-122B +10 |
| Risk | 20%20 | 55 | 50 | InternVL3.5-GPT-OSS-20B-A4B-Preview +5 |
| Agent interface | 10%10 | 45 | 85 | Ornith-1.5-122B +40 |
| Documentation and upkeep | 10%10 | 28 | 36 | Ornith-1.5-122B +8 |
| Negative events | ≤15 | 0 | 0 | |
| Total | D 53.2/100 | C 61/100 |
Facts side by side
| Fact | InternVL3.5-GPT-OSS-20B-A4B-Preview | Ornith-1.5-122B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | OpenGVLab (Shanghai AI Laboratory) | Ornith |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0; a merge of openai/gpt-oss-20b (Apache 2.0 with OpenAI's usage policy) and OpenGVLab/InternViT-300M-448px-V2_5 (MIT) | MIT |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2025-09-08 | 2026-10-24 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 1.8/5 (4) | none |
How the scores have moved
Ornith-1.5-122B
No score change since it was listed.
Verdicts
InternVL3.5-GPT-OSS-20B-A4B-Preview
An image and video reader on gpt-oss-20b with an optional thinking mode, a preview untouched for over a year. It needs about 51 GB in BF16, since OpenGVLab publishes no quantised build, and runs in Transformers and vLLM. The main risk is a chat template in the Transformers-format repository that doesn't match its tokenizer, with 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
InternVL3.5-GPT-OSS-20B-A4B-Preview
- Serve with
vllm serve OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF --max-model-len 32768, which is our command; the card gives none for vLLM - Check the rendered prompt before relying on it, since the shipped template's <|im_start|> markers aren't tokens in the gpt-oss tokenizer; the GitHub-format repository's harmony template is the alternative
- Use Transformers 4.55.0 or later, as the card requires for this size
- For thinking, send the card's thinking system prompt and parse the reasoning out of the <think> tags, with temperature 0.6 and sampling on as the card advises
- Keep inputs within 32K tokens, the length it was trained at, whatever the config says
Ornith-1.5-122B
- Start it with
ollama run ornith-1.5:122b-q4_K_M, orllama-server -hf ornith-ai/Ornith-1.5-122B-GGUF:Q4_K_M --jinja -c 262144 - 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
- 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 - Thinking is on by default. Send
chat_template_kwargswithenable_thinkingfalse for short tasks, a switch the card doesn't mention - Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it
- Use temperature 0.6, top_p 0.95, top_k 20 for coding and temperature 1.0 for general tasks, as the card recommends
- 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, InternVL3.5-GPT-OSS-20B-A4B-Preview or Ornith-1.5-122B?
Ornith-1.5-122B scores 61 (C) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 53.2 (D), and leads in 3 of 4 scored categories. InternVL3.5-GPT-OSS-20B-A4B-Preview leads on risk.
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- Nemotron 3 Ultra 550B A55B vs Ornith-1.5-122B
- North Mini Code 1.0 vs Ornith-1.5-122B
- occamy-1.0 vs Ornith-1.5-122B
- Olmo 3 7B Think vs Ornith-1.5-122B
- Ornith-1.5-122B vs Ornith-1.5-35B-A3B
- Ornith-1.5-122B vs Ornith-1.5-397B
- Ornith-1.5-122B vs Ornith-1.5-9B
- Ornith-1.5-122B vs OTel-2.0-LLM-31B-IT
- Ornith-1.5-122B vs Qwen-AgentWorld-35B-A3B
- Ornith-1.5-122B vs Qwen3-30B-A3B-Instruct-2507
- Ornith-1.5-122B vs Qwen3-4B-Instruct-2507
- Ornith-1.5-122B vs Qwen3.5-0.8B
- Ornith-1.5-122B vs Qwen3.5-122B-A10B
- Ornith-1.5-122B vs Qwen3.5-2B
- Ornith-1.5-122B vs Qwen3.5-397B-A17B
- Ornith-1.5-122B vs Qwen3.5-4B
- Ornith-1.5-122B vs Qwen3.5-9B
- Ornith-1.5-122B vs Qwen3.6-27B
- Ornith-1.5-122B vs Qwen3.6-35B-A3B
- Ornith-1.5-122B vs Qwen3.8-27B
- Ornith-1.5-122B vs Qwen3.8-Flash-Next
- Ornith-1.5-122B vs Qwen3-Coder-30B-A3B-Instruct
- Ornith-1.5-122B vs Qwen3-Coder-Next
- Ornith-1.5-122B vs Qwen3-Omni-30B-A3B-Instruct
- Ornith-1.5-122B vs Qwen3-VL-2B-Instruct
- Ornith-1.5-122B vs Qwen3-VL-30B-A3B-Instruct
- Ornith-1.5-122B vs Qwen3-VL-32B-Instruct
- Ornith-1.5-122B vs Qwen3-VL-4B-Instruct
- Ornith-1.5-122B vs Qwen3-VL-8B-Instruct
- Ornith-1.5-122B vs Qwopus3.8-27B-Flash-V2
- Ornith-1.5-122B vs Recursive Flint
- Ornith-1.5-122B vs Ripple-65B Preview
- Ornith-1.5-122B vs SenseNova-Loop-9B-Preview
- Ornith-1.5-122B vs SmolLM3-3B
- Ornith-1.5-122B vs Spark-X2.6-4B
- Ornith-1.5-122B vs Step 3.7 Flash
- Ornith-1.5-122B vs Swift 1.5 Qwen3.8-27B
- Ornith-1.5-122B vs Swift 1.5 Qwen3.8-Flash-Next
- Ornith-1.5-122B vs ThinkingCap (Qwen3.8 27B)
- Ornith-1.5-122B vs Baidu Unlimited-OCR-2
- Ornith-1.5-122B vs WeVisOne-VL-2B
- Ornith-1.5-122B vs WeVisOne-VL-4B
- Ornith-1.5-122B vs ZAYA1-8B
Machine-readable
- This page as Markdown
/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-ornith-1-5-122b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/internvl3-5-gpt-oss-20b-a4b-preview.json·/api/v1/tools/ornith-1-5-122b.json - From a terminal
anchor compare internvl3-5-gpt-oss-20b-a4b-preview ornith-1-5-122b(the CLI) - Over MCP
compare_tools {"a": "internvl3-5-gpt-oss-20b-a4b-preview", "b": "ornith-1-5-122b"}at/mcp, no key