Head to head · Open-weight models you run yourself · October 2026 research run
Ornith-1.5-397B vs Step 3.7 Flash
Step 3.7 Flash and Ornith-1.5-397B score within a point of each other on agent readiness, 59.8 (C) and 59 (C). Ornith-1.5-397B 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
Best for A self-hosted frontier-scale coding agent on an 8-GPU server, where a clear licence file and an answering publisher aren't required
Ahead on
- Risk, 50 against 45
Watch for
Needs about 290 GB of memory at 4 bits and about 970 GB in BF16, so it doesn't fit the reference machine
Best for Teams with a GPU server or a provider account that want an open, long-context agent model with vision
Ahead on
- Reach, 70 against 65
Watch for
About 131 GB to run at 4 bits with images, over a 128 GB machine's share
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.
Reach Step 3.7 Flash +5
Ornith-1.5-397B 65
Memory 0, since the smallest 4-bit build, Ollama's at 242 GB, needs about 290 GB with the fifth added, above the 256 GB band, and Ornith's own Q4_K_M GGUF and NVFP4 builds need about the same. Runtimes 25, the most the line gives, from vLLM 0.19.1, SGLang 0.5.9, Transformers 5.8.1, llama.cpp's qwen35moe architecture and Ollama's library, while LM Studio's entry is the base's. Builds 15, Ornith publishes FP8, NVFP4 and GGUF at several precisions. Terms 15, MIT as named in the card's metadata on 21 October 2026, 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.
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-1.5-397B +5
Ornith-1.5-397B 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 this run | Ornith-1.5-397B | Step 3.7 Flash | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 65 | 70 | Step 3.7 Flash +5 |
| Risk | 20%20 | 50 | 45 | Ornith-1.5-397B +5 |
| Agent interface | 10%10 | 85 | 85 | even |
| Documentation and upkeep | 10%10 | 36 | 39 | Step 3.7 Flash +3 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 59/100 | C 59.8/100 |
Facts side by side
| Fact | Ornith-1.5-397B | 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 | 2026-08-23 | 2026-06-03 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 2/5 (4) | none |
Verdicts
Ornith-1.5-397B
Ornith's largest coding and terminal-agent model, with XML tool calls that vLLM and SGLang parse and 262,144 tokens of context. It needs about 290 GB even at 4 bits, so a multi-GPU server, not the 128 GB reference machine. The main risks are a missing licence file, no safety results, no publisher replies and files replaced without a note.
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-1.5-397B
- Start it with the card's vLLM command at
--tensor-parallel-size 8, orollama run ornith-1.5:397bon a machine with about 300 GB of memory - 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
- When downloading the NVFP4 build, fetch only the files its model.safetensors.index.json names (the 25-shard set, 241.7 GB), not the eight extra shards
- Pin a revision when you download, since Ornith has replaced files in place
- Validate JSON output yourself, since structured output isn't documented
Step 3.7 Flash
- 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
- On vLLM or SGLang pass
--tool-call-parser step3p5 --reasoning-parser step3p5 - 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
- For llama.cpp load the mmproj file from StepFun's GGUF repository for image input
- StepFun's API has two hosts. Use the one where your key was issued,
api.stepfun.aiorapi.stepfun.com - The same model is on our StepFun API listing, with prices and terms
Questions
Which is better for AI agents, Ornith-1.5-397B or Step 3.7 Flash?
Step 3.7 Flash and Ornith-1.5-397B score within a point of each other on agent readiness, 59.8 (C) and 59 (C). Ornith-1.5-397B leads on risk.
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