hermitdave/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

Benchmark	K2-Horizon-MoVA-36B-A4B
tau3-Banking (Agentic tool use)	26.8
Terminal-Bench 2.1 (Agentic terminal use)	58.6
GPQA Diamond (Graduate-level science QA)	80.8
AA-LCR (Long-context reasoning)	66.3

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.

Benchmark	K2 Score	Qwen Score	Delta	Winner
Bugfix / Coding	76	73	+3	K2
Information Extraction	90	84	+6	K2
Formatting & Structured Output	93	92	+1	K2
Prompt Authority / Safety	80	60	+20	K2
Reasoning & Maths	78	80	-2	Qwen
Structured Output	95	87	+8	K2
Tool Call Performance	93	63	+30	K2
Hermes Agent Capabilities	54	48	+6	K2
Average	82.4	73.4	+9.0	K2

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

Throughput Comparison (oMLX)

Metric	K2 Horizon 36B A4B	Qwen 3.6 35B A3B	Delta
TG TPS (single stream)	48.1	88.4	+84% Qwen
TTFT (ms)	6,300	2,371	-62% Qwen
TPOT (ms)	20.9	11.4	-46% Qwen
Peak Memory (GB)	22.0	21.9	Tie

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

Verdict by Dimension

Dimension	Winner	Notes
Quality	K2 Horizon	Wins 7/8 benchmarks, +9.0 avg
Throughput	Qwen 3.6	~1.8x faster single-stream and batch
Safety	K2 Horizon	0 harmful violations vs 1
Agentic Use	K2 Horizon	Tool call: 93 vs 63, Error recovery: 100 vs 33
Multimodal	Qwen 3.6	Native text + image + video
Long Context	Draw	Qwen 1M (ext.) vs K2 512K native

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:

Tag	Purpose
<ifm|think>, <ifm|think_fast>, <ifm|think_faster>	Thinking blocks
`<|ifm|im_start	>, <|ifm|im_end|>`
<ifm|tool_call>, <ifm|arg_key>, <ifm|arg_value>	Tool call structure

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).