Head to head · Open-weight models you run yourself · October 2026 research run
Inkling-Small vs Ornith-1.5-122B
Inkling-Small scores 67 (B) on agent readiness against Ornith-1.5-122B's 61 (C), and leads in 2 of 4 scored categories. Ornith-1.5-122B 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 Teams with 256 GB or more of datacentre GPU memory who want an open multimodal agent model with audio input and adjustable thinking.
Ahead on
- Risk, 70 against 50
- Documentation and upkeep, 87 against 36
Watch for
The smallest build Thinking Machines publishes needs about 205 GB
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 55
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.
Documentation and upkeep Inkling-Small +51
Inkling-Small 87
Model card 17 of 20, everything but a dated knowledge cut-off, counting Thinking Machines' own model card page. Training data 15, an EU training-content summary with sources, mix, a July 2026 collection cut-off and filtering. Last change 20, the files last changed on 31 July 2026, but Thinking Machines' Inkling renderer in tinker-cookbook changed in its 30 October 2026 release. Responsiveness 20, Thinking Machines answered 8 of the 9 questions in its discussions. Version policy 5, a first release. File health 10, the files agree and no broken-file report waits. 17+15+20+20+5+10 = 87.
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.
Reach Ornith-1.5-122B +20
Inkling-Small 55
Memory 5 of 30, the smallest 4-bit build is Thinking Machines' NVFP4 at 170.8 GB, about 205 GB with a fifth added, so it needs 256 GB. Runtimes 15, released Transformers, vLLM and SGLang run it, llama.cpp, Ollama and LM Studio don't. Builds 10, one quantised build (NVFP4). Terms 10, Apache 2.0 with a binding acceptable use policy on top, read as a custom licence. Download 10, no gate. Hosted 5, Thinking Machines' own Tinker serves it as beta serverless inference, and DeepInfra is live on Hugging Face.
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.
Risk Inkling-Small +20
Inkling-Small 70
File safety 25, safetensors only and no repository code to run. Provenance 15, the files are in the organisation Thinking Machines links to, with a SHA-256 on every LFS file. Safety evaluation 10, Thinking Machines' own StrongREJECT and FORTRESS results and described external red-teaming, but nothing an outside party published or a harness to rerun. Agent guidance 10, the card says what its refusals miss and which safeguards to add, with nothing on prompt injection. Licence risk 5, 10 off for a use policy that reaches beyond the law and that can change at any time. Security programme 5, a security.txt and a disclosure policy, with public advisories unchecked and scored as absent.
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 | Inkling-Small | Ornith-1.5-122B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 55 | 75 | Ornith-1.5-122B +20 |
| Risk | 20%20 | 70 | 50 | Inkling-Small +20 |
| Agent interface | 10%10 | 85 | 85 | even |
| Documentation and upkeep | 10%10 | 87 | 36 | Inkling-Small +51 |
| Negative events | ≤15 | 0 | 0 | |
| Total | B 67/100 | C 61/100 |
Facts side by side
| Fact | Inkling-Small | Ornith-1.5-122B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Thinking Machines Lab | Ornith |
| 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 | 2026-07-31 | 2026-10-24 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 3.5/5 (4) | none |
How the scores have moved
Ornith-1.5-122B
No score change since it was listed.
Verdicts
Inkling-Small
A mixture-of-experts generalist with images and audio in, several tool calls a turn and per-request thinking effort. It needs about 205 GB for Thinking Machines' NVFP4 build on recent NVIDIA or AMD datacentre GPUs, and Tinker or DeepInfra host it. The main risks are a use policy that binds on download and can change, and no outside 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
Inkling-Small
- Start the NVFP4 build with SGLang's recipe,
sglang serve --trust-remote-code --model-path thinkingmachines/Inkling-Small-NVFP4 --tp 8 --enable-multimodal --reasoning-parser inkling --tool-call-parser inkling, on Blackwell for W4A4 - Use the inkling tool and reasoning parsers in vLLM or SGLang, or Thinking Machines' tml-renderers package to render prompts and parse calls yourself
- Pass
reasoning_effortas none, minimal, low, medium, high (the default) or max, or a number from 0 to 0.99. The repository's template has no xhigh preset and raises an error for it, though Thinking Machines' docs and SGLang's cookbook list xhigh - Effort none conditions the model toward no reasoning but doesn't forbid it, and higher effort needs a larger max_tokens
- Tool call arguments must reach the template as an object, not a JSON string, or it raises an error
- Images should be 40 to 4096 px a side and audio 16 kHz WAV under two minutes, per the card
- Use guided decoding or validate JSON yourself, since structured output isn't documented
- Read the Model Acceptable Use Policy before deploying, it binds on download and bars some uses that are legal
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, Inkling-Small or Ornith-1.5-122B?
Inkling-Small scores 67 (B) on agent readiness against Ornith-1.5-122B's 61 (C), and leads in 2 of 4 scored categories. Ornith-1.5-122B leads on reach.
Other comparisons with Inkling-Small or Ornith-1.5-122B
- Agents-A1-4B vs Inkling-Small
- Agents-A1-4B vs Ornith-1.5-122B
- Apertus v1.5 8B vs Inkling-Small
- Apertus v1.5 8B vs Ornith-1.5-122B
- Apodex-1.1-mini vs Inkling-Small
- Apodex-1.1-mini vs Ornith-1.5-122B
- Arete-27B vs Inkling-Small
- Arete-27B vs Ornith-1.5-122B
- BigBang-v1 vs Inkling-Small
- BigBang-v1 vs Ornith-1.5-122B
- Carbon-X vs Inkling-Small
- Carbon-X vs Ornith-1.5-122B
- Cosmos-Reason2-2B vs Inkling-Small
- Cosmos-Reason2-2B vs Ornith-1.5-122B
- Darwin-36B-Opus vs Inkling-Small
- Darwin-36B-Opus vs Ornith-1.5-122B
- DeepSeek-V3.2 vs Inkling-Small
- DeepSeek-V3.2 vs Ornith-1.5-122B
- DeepSeek V4.1 Flash vs Inkling-Small
- DeepSeek V4.1 Flash vs Ornith-1.5-122B
- DeepSeek-V4-Flash-0731 vs Inkling-Small
- DeepSeek-V4-Flash-0731 vs Ornith-1.5-122B
- DiffusionGemma 26B A4B IT vs Inkling-Small
- DiffusionGemma 26B A4B IT vs Ornith-1.5-122B
- Gemma 4 12B IT vs Inkling-Small
- Gemma 4 12B IT vs Ornith-1.5-122B
- Gemma 4 26B A4B IT vs Inkling-Small
- Gemma 4 26B A4B IT vs Ornith-1.5-122B
- Gemma 4 31B IT vs Inkling-Small
- Gemma 4 31B IT vs Ornith-1.5-122B
- Gemma 4 E2B IT vs Inkling-Small
- Gemma 4 E2B IT vs Ornith-1.5-122B
- Gemma 4 E4B IT vs Inkling-Small
- Gemma 4 E4B IT vs Ornith-1.5-122B
- GLM-4.5-Air vs Inkling-Small
- GLM-4.5-Air vs Ornith-1.5-122B
- GLM-4.7-Flash vs Inkling-Small
- GLM-4.7-Flash vs Ornith-1.5-122B
- GLM-5.3-Flash vs Inkling-Small
- GLM-5.3-Flash vs Ornith-1.5-122B
- GLM-5.3 vs Inkling-Small
- GLM-5.3 vs Ornith-1.5-122B
- gpt-oss-120b vs Inkling-Small
- gpt-oss-120b vs Ornith-1.5-122B
- gpt-oss-20b vs Inkling-Small
- gpt-oss-20b vs Ornith-1.5-122B
- Granite 4.2 30B vs Inkling-Small
- Granite 4.2 30B vs Ornith-1.5-122B
- Granite 4.2 3B vs Inkling-Small
- Granite 4.2 3B vs Ornith-1.5-122B
- Granite 4.2 8B vs Inkling-Small
- Granite 4.2 8B vs Ornith-1.5-122B
- Hy-Memory-30B-A3B vs Inkling-Small
- Hy-Memory-30B-A3B vs Ornith-1.5-122B
- Hy3 vs Inkling-Small
- Hy3 vs Ornith-1.5-122B
- Inkling-Small vs InternVL3.5-GPT-OSS-20B-A4B-Preview
- Inkling-Small vs Kimi K3
- Inkling-Small vs Kimi K3.1 Pro
- Inkling-Small vs Kolibri-1
- Inkling-Small vs Kronos
- Inkling-Small vs Laguna M.1
- Inkling-Small vs Laguna S 2.1
- Inkling-Small vs Laguna XS 2.1
- Inkling-Small vs LBS-27B
- Inkling-Small vs LFM2.5-2.6B
- Inkling-Small vs LFM3-1.7B
- Inkling-Small vs LFM3-Audio-2B
- Inkling-Small vs LFM3-Omni-2B
- Inkling-Small vs LFM3-VL-2B
- Inkling-Small vs Ling-3.0-tiny
- Inkling-Small vs Ling-3.1-flash-1019
- Inkling-Small vs MiMo-V2.6 Distill Qwen 9B
- Inkling-Small vs MiniCPM-o 4.5
- Inkling-Small vs MiniCPM-V 4.7 1B
- Inkling-Small vs MiniCPM-V 4.7 35B-A3B
- Inkling-Small vs MiniCPM5-2B
- Inkling-Small vs MiniMax-M2.5
- Inkling-Small vs MiniMax-M2.7
- Inkling-Small vs MiniMax-M3.1-Flash
- Inkling-Small vs Mistral Large 4
- Inkling-Small vs Muse Glimmer 30B
- Inkling-Small vs Nanbeige4.3-33B
- Inkling-Small vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- Inkling-Small vs Nemotron 3.5 Lightning 30B A3B
- Inkling-Small vs Nemotron 3 Nano 30B A3B
- Inkling-Small vs Nemotron 3 Nano 4B
- Inkling-Small vs Nemotron 3 Nano Omni 30B A3B Reasoning
- Inkling-Small vs Nemotron 3 Super 120B A12B
- Inkling-Small vs Nemotron 3 Ultra 550B A55B
- Inkling-Small vs occamy-1.0
- Inkling-Small vs Olmo 3 7B Think
- Inkling-Small vs Ornith-1.5-35B-A3B
- Inkling-Small vs Ornith-1.5-397B
- Inkling-Small vs Ornith-1.5-9B
- Inkling-Small vs Qwen-AgentWorld-35B-A3B
- Inkling-Small vs Qwen3-30B-A3B-Instruct-2507
- Inkling-Small vs Qwen3-4B-Instruct-2507
- Inkling-Small vs Qwen3.5-0.8B
- Inkling-Small vs Qwen3.5-122B-A10B
- Inkling-Small vs Qwen3.5-2B
- Inkling-Small vs Qwen3.5-397B-A17B
- Inkling-Small vs Qwen3.5-4B
- Inkling-Small vs Qwen3.5-9B
- Inkling-Small vs Qwen3.6-27B
- Inkling-Small vs Qwen3.6-35B-A3B
- Inkling-Small vs Qwen3.8-27B
- Inkling-Small vs Qwen3.8-Flash-Next
- Inkling-Small vs Qwen3-Coder-30B-A3B-Instruct
- Inkling-Small vs Qwen3-Coder-Next
- Inkling-Small vs Qwen3-Omni-30B-A3B-Instruct
- Inkling-Small vs Qwen3-VL-2B-Instruct
- Inkling-Small vs Qwen3-VL-30B-A3B-Instruct
- Inkling-Small vs Qwen3-VL-32B-Instruct
- Inkling-Small vs Qwen3-VL-4B-Instruct
- Inkling-Small vs Qwen3-VL-8B-Instruct
- Inkling-Small vs Qwopus3.8-27B-Flash-V2
- Inkling-Small vs Recursive Flint
- Inkling-Small vs Ripple-65B Preview
- Inkling-Small vs SmolLM3-3B
- Inkling-Small vs Spark-X2.6-4B
- Inkling-Small vs Swift 1.5 Qwen3.8-27B
- Inkling-Small vs Swift 1.5 Qwen3.8-Flash-Next
- Inkling-Small vs ThinkingCap (Qwen3.8 27B)
- Inkling-Small vs Baidu Unlimited-OCR-2
- Inkling vs Inkling-Small
- Inkling vs Ornith-1.5-122B
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Ornith-1.5-122B
- Kimi K3.1 Pro vs Ornith-1.5-122B
- Kimi K3 vs Ornith-1.5-122B
- Kolibri-1 vs Ornith-1.5-122B
- Kronos vs Ornith-1.5-122B
- Laguna M.1 vs Ornith-1.5-122B
- Laguna S 2.1 vs Ornith-1.5-122B
- Laguna XS 2.1 vs Ornith-1.5-122B
- LBS-27B vs Ornith-1.5-122B
- LFM2.5-2.6B vs Ornith-1.5-122B
- LFM3-1.7B vs Ornith-1.5-122B
- LFM3-Audio-2B vs Ornith-1.5-122B
- LFM3-Omni-2B vs Ornith-1.5-122B
- LFM3-VL-2B vs Ornith-1.5-122B
- Ling-3.0-tiny vs Ornith-1.5-122B
- Ling-3.1-flash-1019 vs Ornith-1.5-122B
- MiMo-V2.6 Distill Qwen 9B vs Ornith-1.5-122B
- MiniCPM-o 4.5 vs Ornith-1.5-122B
- MiniCPM-V 4.7 1B vs Ornith-1.5-122B
- MiniCPM-V 4.7 35B-A3B vs Ornith-1.5-122B
- MiniCPM5-2B vs Ornith-1.5-122B
- MiniMax-M2.5 vs Ornith-1.5-122B
- MiniMax-M2.7 vs Ornith-1.5-122B
- MiniMax-M3.1-Flash vs Ornith-1.5-122B
- Mistral Large 4 vs Ornith-1.5-122B
- Muse Glimmer 30B vs Ornith-1.5-122B
- Nanbeige4.3-33B vs Ornith-1.5-122B
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Ornith-1.5-122B
- Nemotron 3.5 Lightning 30B A3B vs Ornith-1.5-122B
- Nemotron 3 Nano 30B A3B vs Ornith-1.5-122B
- Nemotron 3 Nano 4B vs Ornith-1.5-122B
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Ornith-1.5-122B
- Nemotron 3 Super 120B A12B vs Ornith-1.5-122B
- Nemotron 3 Ultra 550B A55B 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 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 SmolLM3-3B
- Ornith-1.5-122B vs Spark-X2.6-4B
- 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
Machine-readable
- This page as Markdown
/compare/inkling-small-vs-ornith-1-5-122b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/inkling-small.json·/api/v1/tools/ornith-1-5-122b.json - From a terminal
anchor compare inkling-small ornith-1-5-122b(the CLI) - Over MCP
compare_tools {"a": "inkling-small", "b": "ornith-1-5-122b"}at/mcp, no key