Leaderboard / Edge0

Edge0 35B-A3B (preview)

Edge0 · Edge0 · released 2026-09-08 · data checked 2026-11-01

Edge0 35B-A3B (preview) is a 34.7B-parameter Mixture-of-Experts model (3B active per token) from Edge0, released 2026-09-08 under the Apache-2.0 license. It scores 62.8 on the Small LM Index, #15 of 34 medium models. It needs about 22.4 GB of memory at 4-bit with an 8K-token context.

On-device build of Qwen3.6 35B-A3B. Scores were measured at int4; Edge0 claims ~3 GB peak memory on a Mac mini.

Small LM Index
62.8
#15 of 34 scored medium models
Likely 58.5–66.8 · from 3 of 6 core benchmarks
Parameters
34.7B
3B active · MoE
Price per 1M tokens
—
No hosted API on OpenRouter; self-host
Context window
262K
tokens

Benchmarks

Each bar shows this model's score. The tick marks the best medium model on that benchmark. Sources: 4 reported by Edge0. How we label sources.

MMLU-Pro Lab-reported Knowledge81.0
Class best: Qwen3.6 27B 86.2
GPQA Diamond Lab-reported Knowledge79.8
Class best: Nanbeige4.3 33B 89.5
AIME Lab-reported Math · AIME 2026, measured at int486.6
Class best: Ternary Bonsai 2 27B 95.8
IFBench Lab-reported Instruction following57.9
Class best: Sage-A1.5 81.6

Not reported: estimated

What the index model expects from this model's other scores. Not a measurement.

Humanity's Last Exam≈ 12
LiveCodeBench≈ 72
SWE-bench Verified≈ 50

Specifications

Lab
Edge0 (country not published · Other)
Based on
Qwen3.6 35B-A3B (fine-tune or distillation)
Architecture
Mixture-of-Experts
Total parameters
34.7B
Active parameters
3B
Size class
Medium
Context window
262K tokens
Input modalities
text, image
Reasoning
Hybrid (thinking on/off)
License
Apache-2.0
Openness
Open weights
Memory to run
~22.4 GB at 4-bit, 8K context (more sizes)
Hugging Face
4.2K downloads (30 days) · 1.1K likes

Compare with

Sources

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Run it locally

No GGUF build from a well-known publisher yet.

vLLM (GPU server)
vllm serve Edge0/Edge0-35B-A3B-preview
Full-precision weights, for a server GPU.

Memory needed: weights, context cache and runtime buffers.

Quantization4K context32K context256K context
16-bit F1671 GB71 GB76 GB
8-bit Q8_038 GB39 GB43 GB
4-bit Q4_K_M22 GB23 GB28 GB

Check it against your GPU, Mac or phone →