# Ornith-1.5-122B vs Step 3.7 Flash

> Ornith-1.5-122B scores 61 (C) to Step 3.7 Flash's 59.8 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-step-3-7-flash
- Markdown: https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-step-3-7-flash.md (~8,950 tokens)
- Site index for agents: https://www.anchorterminal.com/llms.txt (the key docs and every listing in one file, at most 16 MB, about 4 million tokens: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key)
- DNS: dig +short TXT <slug>.grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.<category>. and <a>.vs.<b>. under the zone, and the same under grade.letme.dev)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-11-10

Ornith-1.5-122B scores 61 (C) on agent readiness against Step 3.7 Flash's 59.8 (C), and leads in 2 of 4 scored categories. Both do open-weight models you run yourself.

- Ornith-1.5-122B: grade C, 61/100, rank #81 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/ornith-1-5-122b.md · JSON https://www.anchorterminal.com/api/v1/tools/ornith-1-5-122b.json
- Step 3.7 Flash: grade C, 59.8/100, rank #93 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/step-3-7-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/step-3-7-flash.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### Ornith-1.5-122B (C)

Good 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 70
- Risk, 50 against 45

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

### Step 3.7 Flash (C)

Good for: Teams with a GPU server or a provider account that want an open, long-context agent model with vision

Watch for: About 131 GB to run at 4 bits with images, over a 128 GB machine's share


## Why the edge

### Reach: Ornith-1.5-122B +5

- 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.
- Step 3.7 Flash (70): Memory 5, the smallest build at 4 bits is StepFun's IQ4_XS GGUF, 104.99 GB with the 3.97 GB projector, and 108.96 GB × 1.2 = 130.8 GB, over what the reference machine can give a model and within 256 GB. The IQ3 and Q3 builds are below 4 bits and aren't read. Runtimes 20, Released vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 have the step3p7 model, and llama.cpp b12242 has the architecture, with StepFun's own GGUF. Four at 5 each, and the card documents all four. Builds 15, StepFun publishes GGUF at five quantised precisions, an FP8 build and an NVFP4 build. Terms 15, Apache 2.0 in the card's metadata and its licence section, an OSI licence with nothing added. Download 10, the Hub API reports gated false for all four repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, StepFun's own API serves it, per the card and our StepFun API listing, and Featherless and DeepInfra are live for it on Hugging Face. 5 + 20 + 15 + 15 + 10 + 5 = 70.

### Risk: Ornith-1.5-122B +5

- 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.
- Step 3.7 Flash (45): File safety 15, safetensors and GGUF only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the stepfun-ai organisation, which the card's StepFun platform and model page belong to (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations section and gives no guidance on prompt injection, instruction priority, refusals or safeguards. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 0, no way to report a flaw on the card, and our StepFun API listing found no security.txt, bug bounty or disclosure policy. 15 + 15 + 0 + 0 + 15 + 0 = 45.

## Score by category

| Category | Weight | Ornith-1.5-122B | Step 3.7 Flash | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 75 | 70 | Ornith-1.5-122B +5 |
| Risk | 20% (20 this run) | 50 | 45 | Ornith-1.5-122B +5 |
| Agent interface | 10% (10 this run) | 85 | 85 | even |
| Documentation and upkeep | 10% (10 this run) | 36 | 39 | Step 3.7 Flash +3 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **61 · C** | **59.8 · C** | |

## Facts side by side

| Fact | Ornith-1.5-122B | Step 3.7 Flash |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Ornith | StepFun |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | Apache-2.0 (metadata and card; no LICENSE file) |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-24 | 2026-06-03 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |

## Verdicts

**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.

**Step 3.7 Flash.** A capable open agent model with a clean licence, every kind of build and four released runtimes. Two things hold it back: it's a little too big for a 128 GB machine at 4 bits, whatever the card says, and StepFun has gone quiet on the repository since June. No safety results, limitations or data description.

## Before you call either

### 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

### Step 3.7 Flash

1. On a 128 GB machine the 4-bit GGUF won't fit with a working context. Use StepFun's API or a provider, or a server
2. On vLLM or SGLang pass `--tool-call-parser step3p5 --reasoning-parser step3p5`
3. The card's commands pass trust-remote-code. Released Transformers 5.22.2 has the model built in if you'd rather not run the repository's code
4. For llama.cpp load the mmproj file from StepFun's GGUF repository for image input
5. StepFun's API has two hosts. Use the one where your key was issued, `api.stepfun.ai` or `api.stepfun.com`
6. The same model is on our StepFun API listing, with prices and terms

## Questions

### Which is better for AI agents, Ornith-1.5-122B or Step 3.7 Flash?

Ornith-1.5-122B scores 61 (C) on agent readiness against Step 3.7 Flash's 59.8 (C), and leads in 2 of 4 scored categories.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-step-3-7-flash.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-step-3-7-flash.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "ornith-1-5-122b", "b": "step-3-7-flash"}`. From a terminal: `anchor compare ornith-1-5-122b step-3-7-flash`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/ornith-1-5-122b.json and https://www.anchorterminal.com/api/v1/tools/step-3-7-flash.json

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- [Ornith-1.5-122B vs Qwen3.8-27B](https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-8-27b.md)
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- [Ornith-1.5-122B vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-coder-next.md)
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- [Ornith-1.5-122B vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-30b-a3b-instruct.md)
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- [Ornith-1.5-122B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-4b-instruct.md)
- [Ornith-1.5-122B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-8b-instruct.md)
- [Ornith-1.5-122B vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwopus3-8-27b-flash-v2.md)
- [Ornith-1.5-122B vs Recursive Flint](https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-recursive-flint.md)
- [Ornith-1.5-122B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-ripple-65b-preview.md)
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- [Ornith-1.5-122B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-swift-1-5-qwen3-8-flash-next.md)
- [Ornith-1.5-122B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-thinkingcap-qwen3-8-27b.md)
- [Ornith-1.5-122B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-unlimited-ocr-2.md)
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- [Ornith-1.5-122B vs ZAYA1-8B](https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-zaya1-8b.md)
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- [Qwen3-4B-Instruct-2507 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-step-3-7-flash.md)
- [Qwen3.5-0.8B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-step-3-7-flash.md)
- [Qwen3.5-122B-A10B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-step-3-7-flash.md)
- [Qwen3.5-2B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-step-3-7-flash.md)
- [Qwen3.5-397B-A17B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-step-3-7-flash.md)
- [Qwen3.5-4B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-5-4b-vs-step-3-7-flash.md)
- [Qwen3.5-9B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-5-9b-vs-step-3-7-flash.md)
- [Qwen3.6-27B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-step-3-7-flash.md)
- [Qwen3.6-35B-A3B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-step-3-7-flash.md)
- [Qwen3.8-27B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-step-3-7-flash.md)
- [Qwen3.8-Flash-Next vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-step-3-7-flash.md)
- [Qwen3-Coder-30B-A3B-Instruct vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-step-3-7-flash.md)
- [Qwen3-Coder-Next vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-coder-next-vs-step-3-7-flash.md)
- [Qwen3-Omni-30B-A3B-Instruct vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-step-3-7-flash.md)
- [Qwen3-VL-2B-Instruct vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-step-3-7-flash.md)
- [Qwen3-VL-30B-A3B-Instruct vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-step-3-7-flash.md)
- [Qwen3-VL-32B-Instruct vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-step-3-7-flash.md)
- [Qwen3-VL-4B-Instruct vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-step-3-7-flash.md)
- [Qwen3-VL-8B-Instruct vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-step-3-7-flash.md)
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- [Ripple-65B Preview vs Step 3.7 Flash](https://www.anchorterminal.com/compare/ripple-65b-preview-vs-step-3-7-flash.md)
- [SenseNova-Loop-9B-Preview vs Step 3.7 Flash](https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-step-3-7-flash.md)
- [SmolLM3-3B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/smollm3-3b-vs-step-3-7-flash.md)
- [Spark-X2.6-4B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-step-3-7-flash.md)
- [Step 3.7 Flash vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/step-3-7-flash-vs-swift-1-5-qwen3-8-27b.md)
- [Step 3.7 Flash vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/step-3-7-flash-vs-swift-1-5-qwen3-8-flash-next.md)
- [Step 3.7 Flash vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/step-3-7-flash-vs-thinkingcap-qwen3-8-27b.md)
- [Step 3.7 Flash vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/step-3-7-flash-vs-unlimited-ocr-2.md)
- [Step 3.7 Flash vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-2b.md)
- [Step 3.7 Flash vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-4b.md)
- [Step 3.7 Flash vs ZAYA1-8B](https://www.anchorterminal.com/compare/step-3-7-flash-vs-zaya1-8b.md)

---

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