K2-Horizon-MoVA-36B-A4B-oQ4e

K2-Horizon-MoVA-36B-A4B oQ4e

oQ4e (imatrix-enhanced mixed-precision 4-bit) conversion of IFM/K2-Horizon-MoVA-36B-A4B, a sparse Mixture-of-Experts model with Mixture-of-Values attention (36B total / 4B active parameters, 512K context).

Upstream model: IFM/K2-Horizon-MoVA-36B-A4B by the IFM Team, released under Apache-2.0.

Conversion: Quantized to MLX format using Hermes Agent with mlx-lm and oMLX.

Quickstart

pip install -U mlx-lm

python3 -m mlx_lm.generate \
  --model hermitdave/K2-Horizon-MoVA-36B-A4B-oQ4e \
  --prompt "Explain why long-context evaluation is difficult." \
  --max-tokens 512 --temp 1.0 --top-p 0.95

Reasoning

K2-Horizon is a reasoning model. Always use reasoning_effort="high" for best results:

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
response = client.chat.completions.create(
    model="hermitdave/K2-Horizon-MoVA-36B-A4B-oQ4e",
    messages=[{"role": "user", "content": "Explain quantum entanglement."}],
    extra_body={"chat_template_kwargs": {"reasoning_effort": "high"}},
)
print("Reasoning:", getattr(response.choices[0].message, "reasoning_content", None))
print("Answer:", response.choices[0].message.content)

Benchmark Results

Leaderboard Benchmarks

Scores in %. See model card for full results.

Head-to-Head Comparison vs Qwen 3.6 35B A3B

Independent benchmark comparison — raw scores collected via BenchLocal, report compiled by Hermes Agent. See full report.

K2 wins 7/8 benchmarks. Dominates extraction, formatting, tool calling, and safety. Qwen wins only Reasoning & Maths by 2 points.

Throughput Comparison (oMLX)

Qwen is ~1.8x faster due to 3B active parameters (vs 4B) + MTP (multi-token prediction). K2 trades throughput for quality.

Verdict by Dimension

Bottom line: For local agent pipelines where tool reliability and structured output matter: K2 Horizon. For high-volume batch inference where throughput dominates: Qwen 3.6.

oMLX Patch

K2-Horizon requires oMLX v0.6.4+ with the K2-Horizon support patch (PR #3441). This patch adds:

• k2_horizon model type support
• Reasoning content handling (<ifm|think> tags)
• Tool call parsing (plain text and XML formats)
• Multi-turn conversation support

Without this patch, oMLX will refuse to load K2-Horizon models with ValueError: Model type k2_horizon not supported.

Chat Template

K2-Horizon uses IFM's custom chat template with reasoning and tool calling support. Key tags:

All tags are automatically stripped by oMLX before responses reach users.

Citation

@misc{k2horizon2026,
  title  = {Introducing K2 Horizon: Frontier Performance, Radically Open},
  author = {{IFM Team}},
  year   = {2026},
  url    = {https://ifm.ai/blog/k2/},
}

License

Apache-2.0 (same as upstream).