Ornith-1.5-122B vs DeepSeek-V4.1-Flash

Official Ornith-1.5-122B benchmark figure from the Hugging Face model card — Ornith-1.5-122B card assets/ornith_122b_eval.png. Source · License: MIT.

Official DeepSeek-V4.1-Flash agentic benchmark figure from the original Hugging Face card — DeepSeek-AI model card. Source · License: MIT.

Verdict. Pick Ornith-1.5-122B if you want a MIT 122B MoE the card says serves on one 80 GB GPU, with a 262k native context and published vLLM/SGLang tool-calling recipes. Pick DeepSeek-V4.1-Flash-1017 if you want DeepSeek’s MIT multimodal checkpoint (552B backbone, 1M context) and the official DeepInfer engine. Single-80GB and all bench rows stay vendor or third-party. We have no sourced bake-off.

Compare Ornith-1.5-122B and DeepSeek-V4.1-Flash.

Dimension	Ornith-1.5-122B	DeepSeek-V4.1-Flash	Source
License	MIT (HF card tag; LICENSE blob 404 on raw fetch)	MIT (1017 LICENSE file + README sentence)	Hugging Face cards (2026-10-22 / 2026-10-21)
Gating	No	No	Hugging Face API
Developer	Ornith	DeepSeek-AI	Official cards
Parameters	122B MoE on the card; HF safetensors ~125.1B	552B backbone; 8B prefill / 16B decode; 196B Engram (family README)	Official cards
Active parameters	Not stated on the card	8B prefill / 16B decode	Official cards
Modalities	text-generation (HF pipeline); card focuses on coding/agents	Images + text in, text out	Official cards
Context length	262,144 native; YaRN factor 4.0 ~1M (vendor)	1M tokens	Official cards
Serving named	vLLM ≥ 0.19.1; SGLang ≥ 0.5.9; Transformers ≥ 5.8.1	DeepInfer official; card inference/ folder; DeepSeek recipe	Official cards / READMEs
Hardware (vendor)	Single 80 GB GPU (card). BF16 usedStorage ~233 GiB. Untested here.	Datacenter-class 552B-backbone MoE. Peak VRAM unmeasured here.	Official cards; HF usedStorage
Hugging Face likes	129 on the BF16 card (2026-10-22)	1,970 on -1017; 4,674 on the original Flash card (2026-10-21)	Hugging Face API
Quality vs ChatGPT	Not independently tested	Not independently tested	n/a

When to choose which

Published single-80GB-GPU serve claim (vendor): Ornith-1.5-122B — Card sentence, not a forkalt VRAM measurement. BF16 usedStorage is ~233 GiB.
Official DeepSeek inference stack: DeepSeek-V4.1-Flash — DeepInfer README (2026-10-21) lists Flash only. Ornith’s card points at vLLM/SGLang.
Native 1M context without YaRN: DeepSeek-V4.1-Flash — Flash README 1M. Ornith native 262k; ~1M is a YaRN override.
Multimodal (image + text in): DeepSeek-V4.1-Flash — Flash is image-text-to-text. Ornith’s pipeline_tag is text-generation.
MIT coding/agent weights with a Qwen3 tool-call parser recipe: Ornith-1.5-122B — Card publishes vLLM/SGLang parser flags. Not a quality ranking.

Read the entity pages: Ornith-1.5-122B and DeepSeek-V4.1-Flash (1017 checkpoint). Both are listed on ChatGPT alternatives and closed LLM APIs. Ornith also hangs off Claude Code alternatives.

Why this comparison exists

Both families published MIT, ungated, coding-and-agent-oriented weights in 2026. Searchers who already know Flash need a same-site fact sheet for the newer 122B Ornith card (created 2026-10-16) versus the 1017 Flash checkpoint (created 2026-10-17). This is identification, not a bake-off.

Sourced snapshot (2026-10-22 / Flash 2026-10-21)

Field	Ornith-1.5-122B	DeepSeek-V4.1-Flash-1017
Host	huggingface.co/ornith-ai/Ornith-1.5-122B (not gated)	huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017 (not gated)
HF likes / downloads	129 / 742 (2026-10-22)	1,970 / 27,737 (2026-10-21)
License	MIT (card tag)	MIT (LICENSE file)
Size	122B MoE (card); ~125.1B safetensors	552B backbone; 8B/16B active
Job	text-generation; coding/agents	Images + text in, text out
Context	262,144 native	1M
Official serve path	vLLM / SGLang recipes	DeepInfer + `inference/`
Single-GPU claim	80 GB (vendor)	Not claimed as a laptop/single-80GB SKU
Quality vs ChatGPT	Not independently tested	Not independently tested

License

Both cards tag MIT. Flash-1017 has a fetchable LICENSE file (copyright 2023 DeepSeek). Ornith’s YAML license_link 404’d as a raw file on 2026-10-22; we still record the published mit tag. Not legal advice.

How to choose

Need the 122B card and a vLLM / SGLang one-liner with Qwen3 parsers: Ornith-1.5-122B. Engine page: vLLM.
Need DeepSeek’s 1017 multimodal checkpoint and DeepInfer: DeepSeek-V4.1-Flash — DeepInfer vs vLLM.
Need Apache-2.0 US text MoE instead: Reflection Beam.
Need MIT 560B/25B hybrid MoE: Ling-3.1-flash.

Pixel editors are a different job: PhotoCraft vs GIMP and How to install PhotoCraft. See also AI Chat & Agents.

FAQ

Which model is smaller / easier to host?

Ornith’s card says a 122B MoE on one 80 GB GPU. Flash is a 552B-backbone multimodal MoE. We have not measured either VRAM. Ornith BF16 usedStorage is still ~233 GiB; the 80 GB line is vendor.

Can I serve both with vLLM?

Ornith’s card publishes a vLLM ≥ 0.19.1 recipe. Flash’s 1017 card points at inference/ and DeepSeek’s recipe; this site’s Flash page also names DeepInfer as the official engine. See vLLM and DeepInfer vs vLLM. Untested here.

Which is better at SWE-bench or Terminal-Bench?

Both cards print self-reported tables. The harnesses, dates, and opponent columns differ. We do not merge them into a ranking.

Does either replace ChatGPT or Claude Code?

No sourced bake-off on this site. Both hang off those hubs as identification rows.

Sources & verification

Last verified 2026-10-22 · Last updated 2026-10-22· Edited by forkalt editors · Editorial policy

License notes are not legal advice. Read the original LICENSE. Trademark & disclaimer.