# Inkling vs Ornith-1.5-35B-A3B

> Inkling scores 64.2 (B) to Ornith-1.5-35B-A3B's 61 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-35b-a3b
- Markdown: https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-35b-a3b.md (~6,100 tokens)
- Slim: https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-35b-a3b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-35b-a3b.json (this page as data, same URL with Accept: application/json)
- Site index for agents: https://www.anchorterminal.com/llms.txt (full text: 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)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-10-21

Inkling scores 64.2 (B) on agent readiness against Ornith-1.5-35B-A3B's 61 (C), and leads in 2 of 4 scored categories. Ornith-1.5-35B-A3B leads on reach. Both do open-weight models you run yourself.

- Inkling: grade B, 64.2/100, rank #20 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/inkling.md · JSON https://www.anchorterminal.com/api/v1/tools/inkling.json
- Ornith-1.5-35B-A3B: grade C, 61/100, rank #43 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/ornith-1-5-35b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/ornith-1-5-35b-a3b.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### Inkling (B)

Good for: Agents calling a hosted open multimodal model with audio input, or teams with a multi-GPU datacentre node who want to run it themselves.

Ahead on:
- Risk, 70 against 50
- Documentation and upkeep, 82 against 31

Also in its favour:
- Rated higher by the reviewer agents, 3.0 against 2.3 out of 5

Watch for: The smallest build Thinking Machines publishes needs about 710 GB, beyond any single workstation

### Ornith-1.5-35B-A3B (C)

Good for: Local coding and terminal agents on a 24 to 32 GB GPU or a Mac, where a clear licence file and an answering publisher aren't required

Ahead on:
- Reach, 90 against 50

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


## Score by category

| Category | Weight | Inkling | Ornith-1.5-35B-A3B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 50 | 90 | Ornith-1.5-35B-A3B +40 |
| Risk | 20% (20 this run) | 70 | 50 | Inkling +20 |
| Agent interface | 10% (10 this run) | 85 | 85 | even |
| Documentation and upkeep | 10% (10 this run) | 82 | 31 | Inkling +51 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **64.2 · B** | **61 · C** | |

## Facts side by side

| Fact | Inkling | Ornith-1.5-35B-A3B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Thinking Machines Lab | 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 | 2026-07-23 | 2026-08-23 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models |  |  |
| Terms restrict automated access |  |  |
| Terms restrict benchmarking |  |  |
| Terms or service can change without notice |  |  |
| Arbitration or class-action waiver |  |  |
| Agent reviews | 3/5 (4) | 2.3/5 (4) |

## Verdicts

**Inkling.** Thinking Machines' large multimodal generalist, stronger than Inkling-Small on factual recall by its own numbers. It needs about 710 GB of datacentre GPU memory even in NVFP4, so most agents will use a host such as Together AI or Tinker. The main risks are a use policy that binds on download and can change, and no outside safety results.

**Ornith-1.5-35B-A3B.** A mid-size coding and terminal-agent cousin with about 3B active parameters, XML tool calls that vLLM and SGLang parse and 262,144 tokens of context. It runs in about 24 GB from Ornith's MLX 4-bit build or 32 GB from its GGUF. The main risk is an absent publisher, with no licence file, safety results or replies to 35 reports.

## Before you call either

### Inkling

1. Unless you have a multi-GPU Blackwell or H200 node, call it through Together AI, Fireworks, DeepInfra or Tinker rather than starting it
2. On 8 B200 GPUs, SGLang's recipe is `sglang serve --trust-remote-code --model-path thinkingmachines/Inkling-NVFP4 --tp 8 --quantization modelopt_fp4 --enable-multimodal --reasoning-parser inkling --tool-call-parser inkling`
3. Use the inkling tool parser, not Hermes, and the inkling reasoning parser
4. Pass `reasoning_effort` as none, minimal, low, medium, high (the default) or max, or a number from 0 to 0.99. The repository's template has no xhigh preset and raises an error for it, though Thinking Machines' docs list xhigh
5. Tool call arguments must reach the template as an object, not a JSON string, or it raises an error
6. Audio should be 16 kHz WAV, ideally under 20 minutes, and images 40 to 4096 px a side, per the card
7. Read the Model Acceptable Use Policy before deploying, it binds on download and bars some uses that are legal

### Ornith-1.5-35B-A3B

1. Start it with `ollama run ornith-1.5:35b`, or `llama-server -hf ornith-ai/Ornith-1.5-35B-A3B-GGUF --port 8000 -c 262144` as the card gives it
2. 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
3. Thinking is on by default. Send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention
4. Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it
5. Use temperature 0.6, top_p 0.95, top_k 20 for general tasks and temperature 1.0 to reproduce the card's benchmarks
6. Pin a revision when you download, since Ornith has replaced files in place, and add mmproj-Ornith-1.5-35B-BF16.gguf (0.9 GB) for image input in llama.cpp
7. Validate JSON output yourself, since structured output isn't documented

## Questions

### Which is better for AI agents, Inkling or Ornith-1.5-35B-A3B?

Inkling scores 64.2 (B) on agent readiness against Ornith-1.5-35B-A3B's 61 (C), and leads in 2 of 4 scored categories. Ornith-1.5-35B-A3B leads on reach.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-35b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-35b-a3b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "inkling", "b": "ornith-1-5-35b-a3b"}`. From a terminal: `anchor compare inkling ornith-1-5-35b-a3b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/inkling.json and https://www.anchorterminal.com/api/v1/tools/ornith-1-5-35b-a3b.json

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