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Jackrong / Qwopus3.6-35B-A3B-Coder
Model's Last Updated: June 30 2026
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Introduction of Qwopus3.6-35B-A3B-Coder
๐ 3. Thinking-Off Agentic Evaluation
๐ Evaluation: Qwopus 3.6 35B Thinking-Off vs Ornith-1.0 35B Thinking-On
Comparison between Qwopus with thinking disabled and Ornith with thinking enabled. All benchmark runs in this section use Q5_K_M / Q5KM quantized models. Higher is better. Benchmarks courtesy of Tom Turney, @no_stp_on_snek on X.
โก
Main Finding
In these Q5_K_M quantized evaluations,
Qwopus 3.6 35B
was tested with
thinking disabled
. The model also completed a 300-case SWE-bench submitted-patch run with a
62.4%
score. In the behavioral comparison, Qwopus leads in practical execution categories such as legit-request compliance, integrity under pressure, multi-turn orchestration, large code deliverables, and sustained debugging. Ornith remains stronger in selected reasoning-oriented dimensions such as long-context recall, metacognition, engineering competence, and context-poison resistance.
๐๏ธ
Interactive Model Deck by Kyle Hessling
Kyle created a short Hugging Face Space deck that walks through the model story visually: thinking-off agentic coding, the 35B / 3B MoE setup, MTP-assisted local inference, SWE-bench results, token-efficiency comparisons, Qwopus OFF vs Ornith ON, and the OpenCode RTS demo.
visual explainer
thinking-off workflow
SWE-bench + RTS demo
Average Score
82.1 vs 78.9
Qwopus vs Ornith
SWE-bench
62.4%
300 cases, submitted patches
๐งช
3.1 SWE-bench Submitted-Patch Run
Result:
Qwopus-3.6-35B-A3B-Coder scored
62.4%
on a
300-case SWE-bench run
using
thinking off
and
submitted patches
. The evaluated model was the
Q5_K_M quantized
build.
Benchmark
SWE-bench
Run Size
300 tasks
Mode
Thinking off
Quantization
Q5_K_M
| Evaluation | Model / Quant | Patch Mode | Score |
|---|---|---|---|
| SWE-bench, 300 cases | Qwopus-3.6-35B-A3B-Coder Q5_K_M | Thinking off, submitted patches | 62.4% |
โ๏ธ
3.2 Numerical Scorecard
Note:
Scores are held-out behavioral + long-horizon coding evaluation results on a 0-100 scale. Higher is better. The comparison intentionally contrasts Qwopus in thinking-off mode with Ornith-1.0 in thinking-on mode.
| Capability Area |
Qwopus 3.6 35B
thinking off |
Ornith-1.0 35B
thinking on |
Observed Pattern |
|---|---|---|---|
| Legit-request compliance | 100 | 70 | Qwopus follows allowed user intent much more reliably. |
| Integrity under pressure | 93 | 86 | Qwopus is more stable under adversarial or stressful workflow conditions. |
| Multi-turn orchestration | 80 | 70 | Qwopus better maintains state across long agent loops. |
| Large code deliverable | 75 | 65 | Qwopus shows stronger completion behavior for larger code artifacts. |
| Sustained debugging | 60 | 50 | Qwopus holds a practical edge across repeated fix-test cycles. |
| Long-context recall | 90 | 95 | Ornith retains a small advantage in recall-heavy thinking-on settings. |
| Metacognition | 90 | 95 | Ornith benefits from explicit thinking-on reflection. |
| Engineering competence | 81 | 94 | Ornith remains stronger in broad engineering competence. |
| Context-poison resistance | 70 | 85 | Ornith is more robust against context poisoning in this test. |
Takeaway:
Qwopus-3.6-35B-A3B-Coder is positioned as a
practical agent execution model
. The important result is not merely whether it can think longer, but whether it can keep acting correctly when the workflow demands many fast, concrete decisions. This makes it especially relevant for local coding agents, automated debugging loops, and large codebase tasks where token efficiency directly affects usability.
๐ฎ 4. Live Agent Demo: RTS Game Sample
๐ฎ OpenCode / Agent Game-Building Demo
A practical visual test for whether the model can plan, code, iterate, and deliver an interactive project inside an agent workflow.
Kyle Hessling tested the soon-to-release Qwopus-Coder-35B-A3B in an OpenCode workflow by asking it to create a complete RTS-style game sample. This kind of demo is useful because it combines code generation, file orchestration, UI/gameplay logic, iterative correction, and final deliverable quality in one visible task.
View Kyle's RTS demo post
Game screenshot added below
Why this matters:
a playable game demo is not a formal benchmark, but it is a high-signal smoke test for agentic coding. It exposes whether the model can maintain project structure, generate coherent state logic, and complete a visually inspectable artifact rather than only answering isolated prompts.
๐บ๏ธ 5. Training & Workflow Design
The training and evaluation philosophy for this release centers on agent execution rather than visible chain length. The model should know when to act directly, when to inspect more context, and when to stop and summarize.
[ Qwopus-3.6-35B-A3B-Coder: Agentic Execution Pipeline ]
Base MoE Foundation
Qwen3.6-35B-A3B / Qwopus3.6-35B-A3B-v1
โ
โผ
Coding + Tool-Use Adaptation
repository tasks, debugging traces, tool schemas, multi-turn feedback
โ
โผ
Thinking-Off Behavior Target
faster next-step decisions, less overthinking, lower token waste
โ
โผ
Agent Harness Workflows
read files โ choose tool โ edit code โ run tests โ inspect errors โ iterate โ report
โ
โผ
Final Objective
stable long-horizon code execution with practical local latency
This model card intentionally frames thinking-off behavior as a product target. Long thinking can still be useful for difficult reasoning, but the release focuses on whether the model can complete real coding-agent work without paying that cost on every step.
โ 6. Recommended Use Cases & Known Limits
โ
Good Fits
Codex-style agent workflows, OpenHands/OpenCode coding loops, repository-level debugging, multi-file patch generation, automated test-fix cycles, local tool-calling agents, DevOps scripting, code review assistance, and large-context project navigation.
โ ๏ธ
Use With Care
As a specialized coder model, it should not be assumed to be optimal for every general-domain task. Tool-call quality depends strongly on prompt format, schema consistency, and the surrounding harness. Long thinking may still help on some high-difficulty reasoning tasks where speed is less important.
Deployment note : For agent use, ensure that tool definitions, system prompts, output parsing, and retry behavior are consistent. Thinking-off models can be fast, but the harness still needs clean schemas, useful error feedback, and strict task boundaries.
๐ 7. Resources, Acknowledgements & Citation
๐ Resources & Credits
๐
GitHub Repository: Jackrong-llm-finetuning-guide
Access the project repository and related fine-tuning guides.
๐
Q5_K_M benchmark evaluations
SWE-bench submitted-patch run plus behavioral / long-horizon coding evaluation. Benchmarks courtesy of Tom Turney,
@no_stp_on_snek
on X.
๐
Kyle Hessling Interactive Model Deck
Visual Hugging Face Space explaining the model story, thinking-off workflow, SWE-bench result, token efficiency, and RTS demo.
๐
Kyle Hessling RTS Game Demo Post
Reference post for the OpenCode / RTS game-building sample.
๐
Unsloth Documentation
Training acceleration and memory-efficient fine-tuning resources.
Acknowledgements:
Special thanks to the Qwen team for the strong Qwen3.6 MoE base model, Unsloth for efficient fine-tuning tooling, Kyle Hessling for hardware collaboration and live agent testing, and open-source contributors building the agentic coding ecosystem.
Citation
@misc{jackrong_qwopus36_35b_a3b_coder,
title = {Qwopus-3.6-35B-A3B-Coder},
author = {Jackrong},
year = {2026},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/Jackrong/Qwopus-3.6-35B-A3B-Coder}}
}