Local MoE Models Compared: GLM-4.7 vs Qwen 3.6 vs Gemma 4 — Which Runs Best on Your GPU?

Credibility score: 69/100 — Mostly Credible. Mixed credibility - some claims are solid, others need verification.

BSmeter analyzed "Local MoE Models Compared: GLM-4.7 vs Qwen 3.6 vs Gemma 4 — Which Runs Best on Your GPU?" and rated it 69/100 for credibility (a BS score of 31/100 — mostly credible), on 2026-04-29. Its weakest claim — "GLM-4.7: 2s first token latency, >50 t/s throughput" — scored 45/100 and was flagged as dubious. 22 claims were checked against the video transcript. Scores are produced by BSmeter's AI analysis of the transcript, not independent human verification.

Claims analyzed

GLM-4.7, Qwen 3.6, Gemma 4 released April 2026, run locally. — Solid (75/100)

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Frontier AI now runs on gaming PCs via MoE activating 3-4B of 25-35B params — Solid (85/100)

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GLM-4.7: 30B total/3-3.6B active, 128k ctx; Qwen 3.6: 35B/3B active, 1M ctx, text+vision — Solid (82/100)

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Gemma 4 uses SWA attending to nearest 1024 tokens per layer — Solid (82/100)

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Gemma 4 26B: 25.2B/3.8B active, 256k ctx, text+vision+60s video; GLM uses Eagle speculative decoding — OK (65/100)

Gemma 4 MoE? Close but model names off — still tracks tho ⚠️

GLM-4.7: 2s first token latency, >50 t/s throughput — Dubious (45/100)

50+ t/s on consumer GPU? Optimistic AF without hardware specs 🚩

Gemma 4 4-bit weights: 14-15.4GB, fits 16GB VRAM cards — OK (65/100)

Close enough for local runner lingo ⚠️

GLM-4.7: 91.6% AIME 2025; Gemma 4: 88.3% AIME 2026 — Solid (75/100)

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Humanities Last Exam: Qwen 3.6 21.4%, GLM 4.7 14.4%, Gemma 4 8.7% — Solid (78/100)

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Qwen 3.6 35B at 4-bit uses ~21.5GB VRAM — Solid (85/100)

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GPQA Diamond: Qwen 3.6 86%, Gemma 4 82.3%, GLM 4.7 75.2% — Solid (80/100)

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GLM 4.7 30B at 4-bit uses 18-19GB VRAM — Solid (82/100)

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Qwen 3.6 more efficient in total agent loop time — Opinion (50/100)

Smart reframe on verbosity as 'cognitive insurance' — makes sense for agents 👌

GLM-4.7 Flash: 59.2% SWE-Bench Verified; Qwen 3.6: 67.2% SWE-Bench ML — Solid (88/100)

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4-bit quantization causes micro hallucinations in coding — Solid (75/100)

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Gemma 4 uses SWA architecture for VRAM optimization — Dubious (45/100)

SWA? Googling that — sounds made up for Gemma 4 🚩

GLM-4.7 Flash scored 91.6% on AIME 2025 — OK (65/100)

Specific score but AIME 2025? Smells like future benchmark 🚩

Gemma 4 with 4 slots overwhelms 16GB VRAM due to SWA cache despite 14GB weights — Solid (75/100)

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GLM-4.7, Qwen 3.6, Gemma 4 are best local AI models of 2026 — Opinion (50/100)

Boldest 2026 hot takes 👑

Gemma 4 fast/concise agent, excels at Tailwind CSS UI generation — Personal Story (65/100)

Real-world dev take — benchmarks miss the agent feel ⚙️

Gemma 4 26B best for 16GB GPU multimodal coding/UI with SWA tweaks — Opinion (65/100)

Specific rec with the asterisk* ⚠️

Qwen 3.6 verbose traces = insurance for perfect first-try code — Personal Story (68/100)

Long chain-of-thought actually wins coding tasks 🧠💻

See the full analysis with sources and timestamps →