Head to head · Open-weight models you run yourself · October 2026 research run
BigBang-v1 vs Ornith-1.5-122B
Ornith-1.5-122B scores 61 (C) on agent readiness against BigBang-v1's 56.3 (C), and leads in 2 of 4 scored categories. BigBang-v1 leads on risk and documentation and upkeep. Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 4969 open weights comparisons
Which one, for what
Best for Agents with 96 GB or more that want an Apache 2.0 mixture-of-experts model tuned for research, search and coding, and can do without publisher support.
Ahead on
- Risk, 55 against 50
- Documentation and upkeep, 46 against 36
Watch for
BF16 is the only build from the publisher, needing about 86 GB
Best 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 60
- Agent interface, 85 against 60
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
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.
Agent interface Ornith-1.5-122B +25
BigBang-v1 60
Chat template 20, the base's template in the repository. Tool-calling format 10 of 25, the card names the qwen3_coder parser and nothing more. Trained tool calling 20, tuned for tool use with several calls a turn. Structured output 0. Reasoning control 0, the card doesn't say how to turn thinking off or budget it. Context 10, 262,144 tokens. 20 + 10 + 20 + 0 + 0 + 10 = 60.
Ornith-1.5-122B 85
Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card shows how tools go in and how calls come back and names the parser for vLLM and SGLang. Trained tool calling 20, Ornith trains it on agent tasks and the template renders several calls in one turn. Structured output 0, not documented or claimed. Reasoning control 10, Ornith's template reads enable_thinking per request and the card says the reasoning comes back in reasoning_content, though the card doesn't name the switch. Context 10, 262,144 tokens native.
Reach Ornith-1.5-122B +15
BigBang-v1 60
Memory 10 of 30, the only build from the publisher is BF16 at 71.9 GB, 86.3 GB with a fifth added, which fits the reference machine but not 64 GB. Runtimes 25, it keeps the base's qwen3_5_moe architecture, which llama.cpp, vLLM, SGLang and Transformers run in released versions, and Ollama 0.40.14 runs through the llama.cpp build it pins. Builds 0, no quantised build from the publisher. Terms 15, Apache 2.0 like its base. Download 10, not gated. Hosted 0, one inference provider, Featherless AI, where the line needs two. 10 + 25 + 0 + 15 + 10 + 0 = 60.
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.
Documentation and upkeep BigBang-v1 +10
BigBang-v1 46
Model card 11 of 20, limitations, architecture and a cut-off are missing. Training data 5, a general description of about 10,000 synthetic examples. Last change 15, the card last changed on 6 August 2026, 90 days before this check. Responsiveness 0, no publisher reply to any of 7 questions. Version policy 5, a first release. File health 10, the files agree with the card and the base. 11 + 5 + 15 + 0 + 5 + 10 = 46.
Ornith-1.5-122B 36
Model card 11, it covers intended use, architecture, parameters and context but not limitations, languages or a knowledge cut-off. Training data 5, a description of the method with no data or cut-off. Last change 20, a folder upload to the MLX builds on 24 October 2026, 17 days ago. Responsiveness 0, Ornith answered none of the 5 questions and reports. Version policy 0, the MLX builds were uploaded a second time four days after the first with no note. File health 0, the card links a missing LICENSE, states hardware its own files don't fit and names a variant that doesn't exist, and the two chat templates differ. 11 + 5 + 20 + 0 + 0 + 0 = 36.
Risk BigBang-v1 +5
BigBang-v1 55
File safety 25, safetensors only and nothing from the repository runs. Provenance 15, the files are in the publisher's own organisation with a SHA-256 for every weight file. Safety evaluation 0. Agent guidance 0. Licence risk 15, plain Apache 2.0 that hasn't changed. Security programme 0, no website or security contact. 25 + 15 + 0 + 0 + 15 + 0 = 55.
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.
Score by category
| Category | Weight this run | BigBang-v1 | Ornith-1.5-122B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 60 | 75 | Ornith-1.5-122B +15 |
| Risk | 20%20 | 55 | 50 | BigBang-v1 +5 |
| Agent interface | 10%10 | 60 | 85 | Ornith-1.5-122B +25 |
| Documentation and upkeep | 10%10 | 46 | 36 | BigBang-v1 +10 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 56.3/100 | C 61/100 |
Facts side by side
| Fact | BigBang-v1 | Ornith-1.5-122B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | The Endless Frontier (endless-frontier) | Ornith |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | MIT |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-08-06 | 2026-10-24 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 2.3/5 (4) | none |
How the scores have moved
Ornith-1.5-122B
No score change since it was listed.
Verdicts
BigBang-v1
A Qwen3.6-35B-A3B fine-tuned on synthetic research problems, under plain Apache 2.0, for search, science and coding work. The publisher ships BF16 only, about 86 GB to run, and has no report, safety results or replies to questions, so smaller builds and support come from others.
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.
Before you call either
BigBang-v1
- On the reference machine use
vllm serve endless-frontier/BigBang-v1 --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder - The card says to keep at least 128K of context so its thinking isn't cut short
- Thinking control isn't documented, try the base template's
enable_thinkingfalse in chat_template_kwargs and check the output - For Ollama or llama.cpp you'll need someone else's GGUF; the publisher ships BF16 safetensors only
- Validate JSON output yourself or use the runtime's guided decoding
Ornith-1.5-122B
- Start it with
ollama run ornith-1.5:122b-q4_K_M, orllama-server -hf ornith-ai/Ornith-1.5-122B-GGUF:Q4_K_M --jinja -c 262144 - 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
- 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
- Use temperature 0.6, top_p 0.95, top_k 20 for coding and temperature 1.0 for general tasks, as the card recommends
- 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
Questions
Which is better for AI agents, BigBang-v1 or Ornith-1.5-122B?
Ornith-1.5-122B scores 61 (C) on agent readiness against BigBang-v1's 56.3 (C), and leads in 2 of 4 scored categories. BigBang-v1 leads on risk and documentation and upkeep.
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Machine-readable
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/compare/bigbang-v1-vs-ornith-1-5-122b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/bigbang-v1.json·/api/v1/tools/ornith-1-5-122b.json - From a terminal
anchor compare bigbang-v1 ornith-1-5-122b(the CLI) - Over MCP
compare_tools {"a": "bigbang-v1", "b": "ornith-1-5-122b"}at/mcp, no key