What Is Ornith-1.0? The 397B Open-Source Coder That Beats Claude Opus 4.7 on SWE-Bench

Ornith-1.0 is an MIT-licensed family of open-source large language models built for agentic coding, released on June 25, 2026 by DeepReinforce. The family spans four sizes — 9B Dense, 31B Dense, 35B MoE, and 397B MoE — and is post-trained on Gemma 4 and Qwen 3.5 backbones using a novel self-scaffolding reinforcement learning framework. The flagship Ornith-1.0-397B reports 82.4 on SWE-Bench Verified and 77.5 on Terminal-Bench 2.1, edging Claude Opus 4.7 on both. Weights are live on Hugging Face and crossed three million downloads within two weeks of launch.

Key Takeaways

• Vendor: DeepReinforce (deepreinforce-ai on Hugging Face and GitHub), announced June 25, 2026.
• Sizes shipped: Ornith-1.0-9B (Dense), Ornith-1.0-35B (MoE, 3B active), and Ornith-1.0-397B (MoE, 17B active). The 31B Dense variant is listed in the release announcement but has not yet appeared on Hugging Face.
• License: MIT, fully commercial, no regional limits.
• Headline benchmark: 397B scores 82.4 on SWE-Bench Verified vs. Claude Opus 4.7's 80.8; 9B scores 69.4, matching models 3–4× larger.
• Training novelty: Self-Scaffolding RL — the model learns to generate the solution and the task harness that guides it, with a Deterministic Monitor and Frozen LLM Judge Veto to prevent reward hacking.
• Distribution: PyTorch, GGUF, FP8, and community MLX/NVFP4 quantizations on Hugging Face; three million-plus downloads in two weeks.

What Is Ornith-1.0?

Ornith-1.0 is DeepReinforce's first public model family, positioned specifically for agentic coding — the workloads where a model plans, calls tools, edits files, runs commands, and iterates against feedback. It is not a general chat model that happens to code; the training objective, benchmarks, and evaluation harnesses (Terminal-Bench 2.1, SWE-Bench Verified/Pro/Multilingual, NL2Repo, Claw-eval, SWE Atlas) all target real coding-agent trajectories.

The family launched under the tagline "self-improving open-source models for agentic coding" via the official @ornith_ announcement on June 25, 2026 and the DeepReinforce technical writeup the same day. The Ornith-1.0 collection on Hugging Face and the Ornith-1 GitHub repository together host the weights, benchmark tables, and license. All variants are already released — this is not a preview or a leak.

Ornith-1.0 at a Glance

Attribute	Value
Developer	DeepReinforce (deepreinforce-ai)
Type	Family of open-source LLMs for agentic coding
Variants	9B Dense, 31B Dense (announced, not yet on HF), 35B MoE (3B active), 397B MoE (17B active)
Base models	Post-trained on Gemma 4 and Qwen 3.5
Modality	Text; 35B MoE model card lists hybrid attention with vision/video via Qwen3VL processor
Context window	262,144 tokens (per Xinference model card for 35B); 128K–256K used in official benchmarks
Pricing	Free; open weights, MIT license
Formats available	PyTorch, GGUF, FP8; community MLX, NVFP4, W4A16 AutoRound
Announcement date	June 25, 2026
License	MIT
Hugging Face	deepreinforce-ai/Ornith-1.0-9B, -35B, -397B (+ GGUF, FP8 variants)

How Ornith-1.0 Works: The Self-Scaffolding Loop

Ornith-1.0's core mechanism is what DeepReinforce calls Self-Scaffolding Reinforcement Learning. Standard RL for coding agents freezes the harness — the fixed prompt template, tool routing, and memory pattern — and only optimizes the solution rollout. Ornith-1.0 changes that target.

Each RL step runs in two stages. Conditioned on a task and the previously used scaffold, the model first proposes a refined scaffold. Conditioned on that new scaffold plus the task description, it then produces a solution rollout. Reward from the rollout propagates to both stages, so the model learns to author the orchestration that elicits good answers, not just the answer itself. As DeepReinforce puts it, the model becomes "less like a coder following one rigid checklist and more like a coder learning which checklist works for each type of bug, repo, or terminal task."

Because a self-authored scaffold can game its own verifier — reading visible tests, hardcoding outputs — DeepReinforce adds two guards. A Deterministic Monitor inspects scaffold behavior for structural cheats, and a Frozen LLM Judge Veto rejects rollouts where the scaffold appears to shortcut the task. The pairing is what independent write-ups highlight as the interesting piece: as AshutoshShrivastava summarized, "to prevent the model from reward-hacking and gaming its own scaffolds, they added a deterministic monitor and frozen LLM judge veto."

The result is a training regime that discovers per-task-category strategies automatically rather than requiring human-designed harnesses per benchmark.

Benchmark Numbers That Anchor the Claims

The published benchmark table on the Ornith-1 GitHub README is unusually detailed. Highlights, all as reported by DeepReinforce under their disclosed harnesses:

• Ornith-1.0-397B (MoE, 17B active): 82.4 SWE-Bench Verified, 77.5 Terminal-Bench 2.1 (Terminus-2), 78.2 Terminal-Bench 2.1 (Claude Code harness), 62.2 SWE-Bench Pro, 48.2 NL2Repo. Reference numbers listed for Claude Opus 4.7 in the same table are 80.8 SWE-V and 70.3 TB-2.1.
• Ornith-1.0-35B (MoE, 3B active): 75.6 SWE-Bench Verified, 64.2 Terminal-Bench 2.1, 69.3 SWE-Bench Multilingual. The table shows it surpassing Qwen 3.5-397B on Terminal-Bench 2.1 (64.2 vs. 53.5) despite the 10× parameter gap.
• Ornith-1.0-9B (Dense): 69.4 SWE-Bench Verified, 43.1 Terminal-Bench 2.1, 27.2 NL2Repo. The 9B number on SWE-V is the entry that got attention — a 9B model landing in the same neighborhood as 27B–35B peers.

Evaluation harnesses used: OpenHands with 256K context for the SWE-Bench suite; Terminal-Bench 2.1 with the Harbor/Terminus-2 framework and Claude Code 2.1.126, both at 128K context averaged over five runs.

What You Can Do With Ornith-1.0

The design center is coding agents that run tools and edit files, not chat. Real-world uses documented in the bundle:

• Local Claude Code / Codex replacement. The community project rafw007/ornith-claude-coder on Ollama wraps Ornith-1.0-9B in an Anthropic-compatible API so it drives Claude Code, Codex, and opencode fully offline on a 32 GB Mac Mini M4. Documented tests include multi-step Python scripts, disk-usage audits, and nmap network scans with graceful adaptation when sudo is unavailable.
• Terminal-first agent loops. A 35B Q6 user on r/LocalLLaMA reports adding missing unit tests to a small project via Claude Code + Ollama with a 64K context window, noting that longer conversations start to loop.
• Large-context refactors. Multi-DGX-Spark operators on the NVIDIA Developer Forums run the 397B at 262,144-token context under vLLM with tensor parallelism, targeting long-horizon repository work.
• Edge deployment. The 9B GGUF at Q4_K_M is 5.6 GB, fitting in 12–16 GB of VRAM. The community first-look on YouTube demonstrates the 9B running locally on consumer hardware with Ollama.

Users looking for a hosted, closed-source alternative for the same coding workloads often reach for Claude Sonnet 5 or Codex; Ornith-1.0's pitch is that you no longer need to.

How Ornith-1.0 Compares

The comparison the release courts is against closed frontier coding models and against similarly sized open peers. Numbers below are from DeepReinforce's own table.

Model	SWE-Bench Verified	Terminal-Bench 2.1 (Terminus-2)	License
Ornith-1.0-397B	82.4	77.5	MIT
Claude Opus 4.7	80.8	70.3	Proprietary
Claude Opus 4.8	87.6	85.0	Proprietary
DeepSeek-V4-Pro-1.6T	80.6	64.0	Open
Ornith-1.0-35B	75.6	64.2	MIT
Qwen 3.6-35B	73.4	52.5	Open
Ornith-1.0-9B	69.4	43.1	MIT
Gemma 4-31B	52.0	42.1	Open

Independent community testing paints a rougher picture. A tool-eval-bench run on the 35B from MiaAI Lab found results "inconclusive" versus Qwen 3.6-27B — better on some coding tasks, worse on others. Reddit threads on r/LocalLLaMA about the 9B and the 35B variant report frequent loop behavior on longer sessions, particularly with thinking mode enabled. Treat the official table as vendor-reported until independent third parties reproduce it under matching harnesses.

Availability: How to Access Ornith-1.0

All weights are on Hugging Face under the deepreinforce-ai organization. As of mid-July 2026 the Ornith-1.0 collection lists:

• deepreinforce-ai/Ornith-1.0-9B — 1.47M downloads
• deepreinforce-ai/Ornith-1.0-35B — 665K downloads
• deepreinforce-ai/Ornith-1.0-397B — 233K downloads
• GGUF, FP8, and 397B-FP8 variants for quantized deployment

Inference engines with confirmed support include vLLM, SGLang, and Transformers per the Xinference model card. Community quants add MLX for Apple Silicon and Intel AutoRound W4A16 for the 397B on dual DGX Spark setups, documented on the NVIDIA developer forum. There is no first-party hosted API — DeepReinforce ships weights, not a service.

For local use, Ollama pulls of the 9B GGUF are the fastest path, and the ornith-claude-coder wrapper exposes an Anthropic-compatible endpoint for drop-in use with Claude Code and Codex CLIs.

What We Don't Know Yet

A few items remain open even a month after launch:

• The 31B Dense variant. Listed in the official announcement and blog but absent from the Hugging Face collection. Multiple community threads have noted its absence without an ETA.
• Independent benchmark reproduction. No third-party lab has yet reproduced the full SWE-Bench Verified or Terminal-Bench 2.1 numbers under identical harnesses. Community tests so far are partial and mixed.
• Vision and multimodal support. The 35B MoE card mentions vision and video via a Qwen3VL processor, but usage documentation for that path is thin, and NVIDIA forum testers noted vision capabilities appear limited.
• Any future hosted API or pricing tier. DeepReinforce has not signaled a commercial service.

Frequently Asked Questions

What is Ornith-1.0?

Ornith-1.0 is an MIT-licensed family of open-source agentic coding LLMs from DeepReinforce, spanning 9B Dense, 31B Dense, 35B MoE, and 397B MoE variants. It is post-trained on Gemma 4 and Qwen 3.5 with a self-scaffolding reinforcement learning framework that jointly optimizes the model's solutions and the task-specific harnesses that guide them.

Who made Ornith-1.0?

DeepReinforce (deepreinforce-ai) built Ornith-1.0, announcing it on June 25, 2026. The company published weights, benchmarks, and technical notes on Hugging Face and its own blog. Investors including Fusion Fund have publicly amplified the release.

Is Ornith-1.0 open source?

Yes. Ornith-1.0 is released under an MIT license, allowing full commercial use. Weights for the 9B, 35B, and 397B variants are hosted on Hugging Face in PyTorch, GGUF, and FP8 formats, with community MLX and Intel AutoRound quantizations available.

How much does Ornith-1.0 cost?

Ornith-1.0 has no vendor pricing because DeepReinforce distributes it as open weights. Users pay only for the compute they run it on, whether that is a laptop, a workstation GPU, or cloud infrastructure. There is no first-party API subscription.

How does Ornith-1.0 compare to Claude Opus 4.7?

The flagship Ornith-1.0-397B scores 82.4 on SWE-Bench Verified and 77.5 on Terminal-Bench 2.1, beating Claude Opus 4.7's reported 80.8 and 70.3 on those benchmarks. Community testers report mixed real-world results outside the official numbers, and Claude Opus 4.8 sits higher on the same table at 87.6 and 85.0.

What is self-scaffolding training in Ornith-1.0?

Self-scaffolding is Ornith-1.0's training innovation where the model learns to generate both the solution and the task-specific harness — the plan, memory pattern, and tool rhythm — that produces it. Reinforcement learning updates both stages jointly against a Deterministic Monitor plus Frozen LLM Judge Veto to prevent the model from reward-hacking its own scaffold.

Can I run Ornith-1.0 locally?

Yes. The 9B GGUF at Q4_K_M is 5.6 GB and runs on gaming GPUs and Apple Silicon Macs with 12–16 GB of memory. The 35B MoE variant runs on higher-end consumer hardware at 25–35 tokens per second on some laptops. The 397B flagship requires multi-GPU setups such as dual DGX Spark systems with W4A16 quantization.

What to Watch Next

Three signals will decide whether Ornith-1.0's benchmark story survives contact with production. First, whether the 31B Dense variant actually ships — its absence is the most visible gap in the announced family. Second, whether an independent third party reproduces the SWE-Bench Verified and Terminal-Bench 2.1 numbers under matching harnesses, since current community tests are split. Third, whether the loop-and-drift issues reported on long agent sessions get addressed via a point release or updated chat templates.

Building similar agentic coding workflows? On kie.ai you can try Kimi K3, OpenAI Codex, and Claude Opus 4.8.