Datasets:

witcheer
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rtx-5090-benchmarks

Rig: single RTX 5090 32GB · llama.cpp b9562 · native OpenAI tool-calling (--jinja) · temp 0 Subjects: Nex-N2-mini (35B-A3B, agentic post-train) vs its base Qwen3.5-35B-A3B, both Q4_K_M Harness: the rig's new Agentic Score — a model-agnostic native tool-calling loop over 15 deterministic, programmatically-checked tasks (tool-use chains, multi-step dependencies, sandboxed coding). Calibration-grade, not SWE-bench; a sub-5% gap is a tie.

The finding

Nex-N2's headline claim is that "Adaptive Thinking" cuts ~20% of tokens at zero performance loss. On this rig, the token claim is not just true — it's an understatement. Nex-N2-mini does the same agentic work in 93 tokens/task vs the base's 264 — a 65% cut, ~2.8x leaner. But "zero performance loss" does not hold: the post-train trades 13 points of task success for that efficiency.

axis	base Qwen3.5-35B-A3B	Nex-N2-mini	read
Agentic Score	98.0	92.0	base wins overall
Task success	100% (15/15)	86.7% (13/15)	base completes more
Tool efficiency	0.90	0.933	Nex slightly tighter
Loop stability	100%	100%	tie — neither stalls
Tokens / task	264.3	93.3	Nex −65%

Per axis, Nex's misses are concentrated: chain 4/5, multistep 5/5, coding 4/5. The base is clean 5/5/5.

The mechanism (why the post-train loses success)

Both failures trace to the same cause — terse Adaptive Thinking skips the sanity-check a longer pass would have caught:

chain_vram_sq — Nex searched (got 32GB), then called calc("32^2"). The calculator is Python eval, where ^ is XOR, so it returned 34. Nex trusted the surprising number and answered
1. The base used a power/multiply expression and got 1024. A model that paused on "34 ≠ 32²" would have caught it; Nex didn't pause.
coding_sum_evens — Nex's code print()ed the answer instead of assigning result (the tool's documented contract), got an error, then guessed 90 (correct: 110). The base followed the contract and verified in the sandbox.

Same tools, same prompts, temp 0, both reproduced on a re-run. This is the agentic cost of aggressive brevity: fewer tokens, fewer self-checks.

Methodology note (the harness, suspected first)

The first Nex run scored 73% — and was wrong. Traces showed the mock web_search only matched an exact key, so the model's reasonable paraphrases ("RTX 5090 specs VRAM GB") returned "no match" and it looped to the step cap; a second task tripped a safety refusal. Those were harness artifacts, not model weakness. Fixed (search now tolerates phrasing like a real engine; tasks ground via named tools; the safety-trap task reframed), re-ran both models on the identical corrected set. The numbers above are from the corrected harness. Suspect the harness before the model — every time.

Worth it?

Reach for Nex-N2-mini if tokens, latency, or serving cost dominate and your tasks are well-specified — it does the same tool-driving for a third of the tokens, with tighter tool use and zero stalls.
Reach for the base if maximum task completion matters more than token budget — it self-checks surprising tool outputs and honors tool contracts that the terse post-train skips.

Adaptive Thinking is a real, large efficiency win. Just not a free one.

Harness: lib/agentic/native/ in notwitcheer/llm-bench-rig (17 unit tests). Charts: reports/agentic-nex-n2-mini.png, reports/agentic-tokens.png.