Ornith-1.0 Complete Guide 2026: The MIT-Licensed Open-Source AI Coding Model That Surpasses Claude Opus

"An open-source AI coding model has finally surpassed Claude Opus."

Released on June 25, 2026 by DeepReinforce, Ornith-1.0 has exploded across the AI coding community (2.7M+ views / 2.7K+ likes on X).

The reason is simple. Despite being MIT-licensed (fully open-source, commercial use allowed), it beat Claude Opus 4.7 on Terminal-Bench 2.1.

This article covers everything about Ornith-1.0 — its full picture, benchmark performance, comparisons with other models, and how to actually use it.

What you'll learn in this article:

What Ornith-1.0 is and why it's generating so much buzz
Performance and ideal use cases for each model (9B / 31B / 35B / 397B)
Comparisons with DeepSeek V4 Pro, MiniMax M3, and Claude Opus
The revolutionary "Self-Scaffolding" technique
How to run it with Ollama / LM Studio / vLLM
How to choose the right model for your needs

The Bottom Line: Ornith-1.0 Is the Best Open-Source Coding Model of 2026

Item	Details
Developer	DeepReinforce (deepreinforce-ai)
License	MIT (fully open-source, commercial use allowed)
Base Model	Gemma 4 + Qwen 3.5, post-trained
Lineup	9B Dense / 31B Dense / 35B MoE / 397B MoE
Key Innovation	Self-improvement framework where the model learns the "scaffold" itself via RL
Terminal-Bench 2.1 (397B)	77.5 — surpasses Claude Opus 4.7 (70.3)
SWE-Bench Verified (397B)	82.4 — open-source state-of-the-art
Hugging Face	GGUF versions available. Run instantly with Ollama / LM Studio

What Is Ornith-1.0? — A Next-Gen LLM Purpose-Built for Agentic Coding

Ornith-1.0 is an open-source LLM family developed by DeepReinforce, purpose-built for agentic coding — tasks where an AI agent autonomously writes, debugs, and manages code.

Why This Matters

Traditional LLMs have excelled at "generating code," but agentic capability — autonomously looping through multi-file edits, test execution, error analysis, and fixes in a real development environment — is a completely different skill.

Ornith-1.0 is the first serious open-source model trained specifically for this "code generation capability as an agent."

Model Lineup

Model	Type	Parameters	VRAM Required (approx.)	Target
Ornith-1.0-9B	Dense	9B	~8 GB (4-bit quantized)	Edge devices / lightweight local execution
Ornith-1.0-31B	Dense	31B	~24 GB (4-bit)	Desktop / high-performance local
Ornith-1.0-35B	MoE	35B (29B active)	~16 GB (4-bit)	Best cost-performance — Recommended
Ornith-1.0-397B	MoE	397B (active ?B)	80 GB+ (4-bit)	Frontier / maximum performance

Benchmark Comparison: Ornith-1.0 vs. Major Models

The most shocking aspect of Ornith-1.0 is its benchmark results.

Flagship Comparison: 397B Model vs. Closed Models

Model	Terminal-Bench 2.1	SWE-Bench Verified	License
Ornith-1.0-397B 🔥	77.5	82.4	MIT (open-source)
Claude Opus 4.7	70.3	80.8	Proprietary
DeepSeek V4 Pro	67.9	80.6	Proprietary
MiniMax M3	66.0	80.5	Proprietary
Qwen 3.5-397B	53.5	—	Qwen License

Ornith-1.0-397B beats Claude Opus 4.7 by over 10 points on Terminal-Bench and also surpasses it on SWE-Bench. This is the first time an open-source model has exceeded cutting-edge closed models on real benchmarks at this scale.

35B MoE (Mid-Range) Comparison

Model	Terminal-Bench 2.1	SWE-Bench Verified
Ornith-1.0-35B 🔥	64.2	75.6
Qwen 3.6-35B	—	—
Qwen 3.5-35B	—	—
Gemma 4-31B	—	—
Qwen 3.5-397B	53.5	—

An astounding result: the 35B model beats the 397B model on Terminal-Bench. This demonstrates that Ornith-1.0's architecture and training methodology excel at parameter efficiency.

9B (Edge / Lightweight) Comparison

Model	Terminal-Bench 2.1	SWE-Bench Verified	Notes
Ornith-1.0-9B 🔥	43.1	69.4	At 9B, rivals Gemma 4-31B 🔥
Gemma 4-31B	—	—	31B (3.4× the params of 9B)
Qwen 3.6-35B	—	—	35B (3.9× the params of 9B)

A 69.4 SWE-Bench Verified score from a 9B model is extraordinary. This rivals the performance of Gemma 4-31B (31B parameters), demonstrating industry-leading parameter efficiency.

What Is "Self-Scaffolding"? — Explained for Everyone

Ornith-1.0's greatest technical innovation is "Self-Scaffolding."

The Problem with Traditional Approaches

AI coding agents typically work through the following loop:

A human says "solve this problem"
The model writes code
Tests are run
If there are errors, fix them... and repeat

In this flow, the "procedure for how to solve the problem" (the scaffold) was designed by humans. For example, humans had to meticulously craft instructions like "first read the error log," "then fix the relevant file."

Ornith-1.0's Innovation

Ornith-1.0 learns the scaffold itself, along with everything else.

The model independently devises a strategy for "how to solve this problem"
It then generates actual code following that strategy
Results (rewards) are fed back to both
Better strategies are discovered automatically over time

In other words, it performs a kind of meta-cognitive learning — evolving not just its problem-solving ability but also its ability to strategize how to solve problems.

Why This Makes It Powerful

Traditional models were optimized for "faithfully executing given instructions," but Ornith-1.0 can "discover the optimal procedure on its own." This enables:

Flexible adaptation to unknown task types
Selection of the optimal approach per task
No performance bottlenecks from gaps in human-designed procedures

Getting Started with Ornith-1.0

The Easiest Way: Ollama or LM Studio

GGUF versions are publicly available, so you can run Ornith-1.0 locally with Ollama or LM Studio in no time.

# Run the 35B MoE model with Ollama (recommended)
ollama pull deepreinforce-ai/ornith-1.0-35b-gguf
ollama run deepreinforce-ai/ornith-1.0-35b-gguf

VRAM requirements (4-bit quantization):

9B: ~6–8 GB VRAM → works on RTX 4060 / M1 Mac 16 GB
35B MoE: ~16–20 GB VRAM → RTX 4090 / M2 Max 32 GB+ recommended
397B: 80 GB+ VRAM → multi-GPU / DGX-class

Using Hugging Face Transformers

from transformers import pipeline

pipe = pipeline("text-generation", model="deepreinforce-ai/Ornith-1.0-9B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "Write FizzBuzz in Python"},
        ]
    },
]
pipe(text=messages)

Running as a Server with vLLM

# Start an API server with vLLM
vllm serve "deepreinforce-ai/Ornith-1.0-35B-GGUF"

# Call via OpenAI-compatible API
curl -X POST "http://localhost:8000/v1/chat/completions" \
  -H "Content-Type: application/json" \
  --data '{
    "model": "deepreinforce-ai/Ornith-1.0-35B-GGUF",
    "messages": [{"role": "user", "content": "Implement quicksort in Python"}]
  }'

Using Free APIs

The free LLM API at freellm.net, covered in a previous article, may also support Ornith-1.0 in the future. Once available through free NVIDIA NIM or Groq tiers, it will become even more accessible.

→ FreeLLM.net Complete Guide 2026: How to Use 220+ Free LLM APIs at Zero Cost

Recommended Models by Use Case

User Type	Recommended Model	Why
🖥️ Local Developer (16 GB VRAM)	Ornith-1.0-35B MoE	Best cost-performance — 35B delivers 397B-class performance
💻 M1/M2 Mac User	Ornith-1.0-9B	Lightweight — 69.4 SWE-Bench is highly practical
🏢 Team Use	Ornith-1.0-35B MoE	MIT license — commercial use allowed — ideal for server deployment
🔬 Research / Benchmarking	Ornith-1.0-397B	Maximum performance — surpasses Claude Opus
🤖 Pairing with AI Agents	Ornith-1.0-35B	Excellent agentic capability

How Ornith-1.0 Differs from Existing AI Coding Agents

Comparison	Ornith-1.0	Cursor / Claude Code / Hermes
Category	🚀 LLM Model	🛠️ Agent Tool
Role	The "brain" that generates code	The "platform" that runs models like Ornith
License	MIT (fully open)	Proprietary (Cursor) or OSS (Hermes)
Relationship	Mutually compatible	Ornith can be used as a backend for Cursor / Hermes
Requirements	Local environment or API	Install each tool and configure the model

Ornith-1.0 is a "model," not a competitor to "agent tools" like Cursor or Claude Code. By using Ornith-1.0 as the backend for those tools, you can build an even more powerful AI coding environment.

Limitations and Caveats

Of course, it's not all good news. Here's an honest assessment.

1. Just Released — Limited Track Record

Released June 25, 2026. The benchmark results are excellent, but there is no real-world project track record yet. The community will need time to validate it.

2. The 397B Model Isn't for Everyone

Running the 397B MoE (397 billion parameters) requires 80 GB+ of VRAM even at 4-bit quantization. You'll need DGX-class hardware or cloud GPUs (H100) — not practical for a personal desktop.

3. Benchmarks ≠ Real-World Performance

SWE-Bench and Terminal-Bench simulate only a portion of real software development. Terminal-Bench in particular is still relatively new and lacks sufficient community validation. Real-world performance remains unknown.

4. Training Data Transparency

While the base models are Gemma 4 and Qwen 3.5, the details of the datasets used for post-training have not been disclosed.

Frequently Asked Questions

Q: Is Ornith-1.0 completely free?

Yes. It is released under the MIT license, so commercial use, modification, and redistribution are all freely permitted. GGUF versions are also free to download.

Q: Does it work with Ollama?

Yes. GGUF versions are available, so you can get started with a simple ollama pull. It also works with LM Studio.

Q: Should I use Ornith-1.0 or Cursor / Claude Code?

Use both — that's the correct answer. Ornith-1.0 is a model; Cursor / Claude Code are tools. Set up Ornith as the backend for those tools and you get the ultimate combination.

Q: Which model should I choose?

The 35B MoE offers the best cost-performance. If VRAM is limited (~8 GB), go with 9B. If you need maximum performance and have the resources, choose 397B.

Q: How does it compare to DeepSeek V4 Pro?

On Terminal-Bench 2.1, Ornith-1.0-397B (77.5) significantly outperforms DeepSeek V4 Pro (67.9). On SWE-Bench, Ornith (82.4) > DeepSeek (80.6) as well.

Q: Does it support Japanese?

Since Qwen 3.5 is used as a base model, Japanese understanding and generation are possible. However, benchmarks focus on English coding tasks, and no Japanese-specific tuning has been performed.

Q: Is the MIT license for real?

Yes. You can verify this on Hugging Face. It's a full MIT license — commercial use, modification, and redistribution are all free.

Q: When will the 31B Dense be released?

According to @ornith_'s X post, it's "coming soon." Currently, only 9B, 35B, and 397B are available.

Conclusion: Ornith-1.0 Sets a New Standard for Open-Source AI Coding

The release of Ornith-1.0 is a historic milestone for open-source AI coding models.

✅ MIT-licensed — fully open, commercial use allowed
✅ Benchmark performance surpassing Claude Opus 4.7
✅ Revolutionary Self-Scaffolding technique
✅ Broad lineup from 9B to 397B
✅ Try it immediately with Ollama / GGUF

It has only just been released and its real-world track record is still ahead of it, but its benchmark potential is undeniably real. In particular, the 35B MoE model will likely be the most cost-effective choice for individual developers.

Start by giving it a quick spin with Ollama:

ollama pull deepreinforce-ai/ornith-1.0-35b-gguf
ollama run deepreinforce-ai/ornith-1.0-35b-gguf

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