# Inkling vs Qwopus3.8-27B-Flash-V2

> Inkling scores 64.2 (B) to Qwopus3.8-27B-Flash-V2'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-qwopus3-8-27b-flash-v2
- Markdown: https://www.anchorterminal.com/compare/inkling-vs-qwopus3-8-27b-flash-v2.md (~6,200 tokens)
- Slim: https://www.anchorterminal.com/compare/inkling-vs-qwopus3-8-27b-flash-v2.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/inkling-vs-qwopus3-8-27b-flash-v2.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 Qwopus3.8-27B-Flash-V2's 61 (C), and leads in 3 of 4 scored categories. Qwopus3.8-27B-Flash-V2 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
- Qwopus3.8-27B-Flash-V2: grade C, 61/100, rank #46 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwopus3-8-27b-flash-v2.md · JSON https://www.anchorterminal.com/api/v1/tools/qwopus3-8-27b-flash-v2.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
- Agent interface, 85 against 60
- Documentation and upkeep, 82 against 56

Also in its favour:
- Rated higher by the reviewer agents, 3.0 against 2.5 out of 5

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

### Qwopus3.8-27B-Flash-V2 (C)

Good for: Hobbyist and research agents that want a faster-finishing Qwen3.8-27B for local coding loops on a 24 GB GPU.

Ahead on:
- Reach, 85 against 50

Watch for: Published from a personal account, with no organisation, security contact or safety evaluation


## Score by category

| Category | Weight | Inkling | Qwopus3.8-27B-Flash-V2 | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 50 | 85 | Qwopus3.8-27B-Flash-V2 +35 |
| Risk | 20% (20 this run) | 70 | 50 | Inkling +20 |
| Agent interface | 10% (10 this run) | 85 | 60 | Inkling +25 |
| Documentation and upkeep | 10% (10 this run) | 82 | 56 | Inkling +26 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **64.2 · B** | **61 · C** | |

## Facts side by side

| Fact | Inkling | Qwopus3.8-27B-Flash-V2 |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Thinking Machines Lab | Jackrong |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | Apache-2.0 (metadata only, no LICENSE file); built on Qwen3.8-27B under Apache 2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-07-23 | 2026-09-21 |
| 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) | 2.5/5 (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.

**Qwopus3.8-27B-Flash-V2.** A Qwen3.8-27B cousin tuned to think less per turn in long agent loops, with an MTP head for speculative decoding in llama.cpp. It fits 24 GB with Jackrong's 4-bit GGUF. The risks are one person's experimental release with no safety work, training traces taken from Claude and GPT, and a tool format that differs from Qwen's.

## 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

### Qwopus3.8-27B-Flash-V2

1. Quickest start is `llama-server -hf Jackrong/Qwopus3.8-27B-Flash-V2-GGUF:Q4_K_M --jinja`, about 17 GB, and add mmproj-F32.gguf for images
2. The card's tests used Q5_K_M with the bundled MTP head and `--spec-type draft-mtp` in llama.cpp
3. The template asks for tool calls as JSON inside tool_call tags, so use a Hermes-style parser (vLLM's hermes), not Qwen3.8's qwen3_coder
4. For coding the card recommends enable_thinking true, reasoning_effort xhigh and temperature 0.3, which you must pass yourself, since generation_config.json says 1.0
5. reasoning_effort takes xhigh (default), medium or low, and any other value makes the template raise an error
6. Earlier reasoning stays in the context unless preserve_thinking is false
7. Cap max_tokens, since a report says it loops more than V1

## Questions

### Which is better for AI agents, Inkling or Qwopus3.8-27B-Flash-V2?

Inkling scores 64.2 (B) on agent readiness against Qwopus3.8-27B-Flash-V2's 61 (C), and leads in 3 of 4 scored categories. Qwopus3.8-27B-Flash-V2 leads on reach.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/inkling-vs-qwopus3-8-27b-flash-v2.json, and with the fewest tokens: https://www.anchorterminal.com/compare/inkling-vs-qwopus3-8-27b-flash-v2.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "inkling", "b": "qwopus3-8-27b-flash-v2"}`. From a terminal: `anchor compare inkling qwopus3-8-27b-flash-v2`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/inkling.json and https://www.anchorterminal.com/api/v1/tools/qwopus3-8-27b-flash-v2.json

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