A reasoning SFT fine-tune of Qwen/Qwen3.6-35B-A3B on chain-of-thought (CoT) distillation mostly sourced from Claude Opus 4.6. The goal is to preserve Qwen3.6's strong agentic coding and reasoning base while nudging the model toward structured Claude Opus-style reasoning traces and more stable long-form problem solving.
The training path is text-only. The Qwen3.6 base architecture includes a vision encoder, but this fine-tuning run did not train on image or video examples.
The MMLU-Pro pass used 70 total questions per model: --limit 5 across 14 MMLU-Pro subjects. Treat this as a smoke/comparative check, not a release-quality full benchmark.
Benchmark
Harness
Samples per model
Setting
Metric
Base model
Fine-tuned merged model
Delta
MMLU-Pro overall
lm-evaluation-harness
70
--limit 5 across 14 subjects
exact_match, custom-extract
42.86%
75.71%
+32.85 pp
Base model: Qwen/Qwen3.6-35B-A3B. Fine-tuned model: hesamation/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled.
[!WARNING]
Community benchmarks welcome
To better understand this fine-tuned model's capabilities, I welcome independent benchmark results. If you run evaluations, please include the benchmark name, harness/script, sample count, decoding settings, and raw logs or result files when possible.
Share results by opening a PR/discussion or DMing @hesamation on X.
Base Qwen3.6 Highlights
This release delivers substantial upgrades, particularly in:
Agentic Coding: the model now handles frontend workflows and repository-level reasoning with greater fluency and precision.
Thinking Preservation: Qwen introduced a new option to retain reasoning context from historical messages, streamlining iterative development and reducing overhead.
Benchmark Results
For more details, please refer to the Qwen blog post Qwen3.6-35B-A3B.
Base Model Overview
Type: Causal Language Model with Vision Encoder
Training Stage: Pre-training & Post-training
Language Model:
Number of Parameters: 35B in total and 3B activated
Hidden Dimension: 2048
Token Embedding: 248320 (Padded)
Number of Layers: 40
Hidden Layout: 10 x (3 x (Gated DeltaNet -> MoE) -> 1 x (Gated Attention -> MoE))
Gated DeltaNet:
Number of Linear Attention Heads: 32 for V and 16 for QK
Head Dimension: 128
Gated Attention:
Number of Attention Heads: 16 for Q and 2 for KV
Head Dimension: 256
Rotary Position Embedding Dimension: 64
Mixture Of Experts:
Number of Experts: 256
Number of Activated Experts: 8 Routed + 1 Shared
Expert Intermediate Dimension: 512
LM Output: 248320 (Padded)
MTP: trained with multi-steps
Context Length: 262,144 natively and extensible up to 1,010,000 tokens.
Base Benchmark Results
The following table is from the upstream Qwen3.6-35B-A3B release context and is included for base-model reference. It is not a benchmark of this fine-tuned checkpoint unless explicitly stated in the fine-tune benchmark table above.
Category
Benchmark
Qwen3.5-27B
Gemma4-31B
Qwen3.5-35BA3B
Gemma4-26BA4B
Qwen3.6-35BA3B
Coding Agent
SWE-bench Verified
75.0
52.0
70.0
17.4
73.4
Coding Agent
SWE-bench Multilingual
69.3
51.7
60.3
17.3
67.2
Coding Agent
SWE-bench Pro
51.2
35.7
44.6
13.8
49.5
Coding Agent
Terminal-Bench 2.0
41.6
42.9
40.5
34.2
51.5
Coding Agent
Claw-Eval Avg
64.3
48.5
65.4
58.8
68.7
Coding Agent
Claw-Eval Pass^3
46.2
25.0
51.0
28.0
50.0
Coding Agent
SkillsBench Avg5
27.2
23.6
4.4
12.3
28.7
Coding Agent
QwenClawBench
52.2
41.7
47.7
38.7
52.6
Coding Agent
NL2Repo
27.3
15.5
20.5
11.6
29.4
Coding Agent
QwenWebBench
1068
1197
978
1178
1397
General Agent
TAU3-Bench
68.4
67.5
68.9
59.0
67.2
General Agent
VITA-Bench
41.8
43.0
29.1
36.9
35.6
General Agent
DeepPlanning
22.6
24.0
22.8
16.2
25.9
General Agent
Tool Decathlon
31.5
21.2
28.7
12.0
26.9
General Agent
MCPMark
36.3
18.1
27.0
14.2
37.0
General Agent
MCP-Atlas
68.4
57.2
62.4
50.0
62.8
General Agent
WideSearch
66.4
35.2
59.1
38.3
60.1
Knowledge
MMLU-Pro
86.1
85.2
85.3
82.6
85.2
Knowledge
MMLU-Redux
93.2
93.7
93.3
92.7
93.3
Knowledge
SuperGPQA
65.6
65.7
63.4
61.4
64.7
Knowledge
C-Eval
90.5
82.6
90.2
82.5
90.0
STEM & Reasoning
GPQA
85.5
84.3
84.2
82.3
86.0
STEM & Reasoning
HLE
24.3
19.5
22.4
8.7
21.4
STEM & Reasoning
LiveCodeBench v6
80.7
80.0
74.6
77.1
80.4
STEM & Reasoning
HMMT Feb 25
92.0
88.7
89.0
91.7
90.7
STEM & Reasoning
HMMT Nov 25
89.8
87.5
89.2
87.5
89.1
STEM & Reasoning
HMMT Feb 26
84.3
77.2
78.7
79.0
83.6
STEM & Reasoning
IMOAnswerBench
79.9
74.5
76.8
74.3
78.9
STEM & Reasoning
AIME26
92.6
89.2
91.0
88.3
92.7
Notes from the upstream Qwen3.6 release:
SWE-Bench Series: internal agent scaffold with bash and file-edit tools; temp=1.0, top_p=0.95, 200K context window.
Terminal-Bench 2.0: Harbor/Terminus-2 harness; 3h timeout, 32 CPU/48 GB RAM; temp=1.0, top_p=0.95, top_k=20, max_tokens=80K, 256K ctx; average of 5 runs.
SkillsBench: evaluated via OpenCode on 78 tasks, using a self-contained subset excluding API-dependent tasks; average of 5 runs.
NL2Repo: evaluated via Claude Code for other models, with temp=1.0, top_p=0.95, max_turns=900.
TAU3-Bench: official user model with gpt-5.2 low reasoning effort and default BM25 retrieval.
VITA-Bench: average subdomain scores, using claude-4-sonnet as judge.
MCPMark: GitHub MCP v0.30.3, Playwright responses truncated at 32K tokens.
MCP-Atlas: public set score, gemini-2.5-pro judge.
AIME 26: full AIME 2026 I and II.
Training Pipeline
text
1Qwen/Qwen3.6-35B-A3B
2 -> supervised fine-tuning with LoRA
3 -> merged full model
4 -> Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled
Training configuration:
Setting
Value
Fine-tuning method
Supervised fine-tuning with LoRA
LoRA target
Attention-only modules
LoRA rank / alpha
32 / 32
Micro-batch size
1
Gradient accumulation
32
Epochs
2
Completed steps
762 / 762
Final reported training loss
0.3362497625740494
Dataset max tokens
8192
Max sequence length
32768
Training Data
The recipe samples and normalizes reasoning conversations from three datasets, then renders them with the qwen3-thinking chat template and response-only SFT masking.
This model is intended for reasoning-heavy text workflows such as coding assistance, planning, math-style reasoning, and structured analytical responses. Because the fine-tune is text-only, image/video behavior should be treated as inherited from the base model rather than improved by this training run.
Acknowledgements
Thanks to the Qwen team for the base model, Unsloth for the training stack, and Jackrong for the public reasoning-distillation workflow that inspired this fine-tune.
