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IFM/K2-Horizon-375B-A23B

🤗 On Hugging Facetext-generationapache-2.0379B params758 GBsafetensors✓ HF checksums availableupdated today
Magnet

K2-Horizon-375B-A23B

K2-Horizon-375B-A23B is the flagship of the K2-Horizon family: a sparse Mixture-of-Experts model that stores 375B parameters and runs 23B per token, with a 512K context window. We have released the final checkpoint; intermediate checkpoints, along with the data and the training code, will be released.

K2-Horizon-375B-A23B Highlights

  • Frontier-class agentic performance. On agentic tool use, terminal, and long-horizon workflow benchmarks it matches or beats open-weight MoE models up to 2.6× its size and is competitive with closed frontier models (see Benchmark Results).
  • 512K context. Native 524,288-token context from the midtraining stages onward.
  • Intermediate checkpoints. Intermediate checkpoints will be released so capability changes can be studied across training rather than at a single checkpoint.
  • Fully open. Training data/recipe and the training code will be made public.

Benchmark Results

Open-weight modelsClosed models

K2-Horizon-375B-A23BNemotron 3 UltraInkling (xhigh)MiniMax-M3GLM 5.2 (max)GPT 5.6 Luna (max)GPT 5.6 Terra (high)Claude Sonnet5 (max)

Params375B550B975B428B753B------

Activated params23B55B41B23B40B------

ArchitectureMoEMoEMoEMoEMoEClosedClosedClosed

Agents

GDPVal-AA

Real-world professional tasks (Elo)

1,4411,1621,2341,3801,4981,5691,5031,584

tau3-Banking

Agentic tool use

34.014.229.115.334.631.128.737.3

Toolathlon Verified

Agentic tool use

65.334.345.553.759.967.564.871.6

Automation Bench Public

Workflow automation

25.38.012.820.526.233.528.034.7

Apex-Agents (pass@1)

Long-horizon professional workflows

24.89.019.023.826.928.625.431.7

MCPMark

MCP tool use

67.745.751.248.872.466.974.065.3

BrowseComp

Deep web research

72.844.477.183.5--83.3--84.7

WildClawBench

In-the-wild agentic tasks

50.934.252.356.455.050.460.0--

Coding

Terminal-Bench 2.1

Agentic terminal use

70.253.955.165.277.980.975.780.5

SciCode

Scientific coding

42.739.946.145.450.552.550.153.6

SWE-Atlas-QnA (strict)

Repo-level code Q&A

48.4--25.542.346.4------

SWE Bench Pro (strict)

Software engineering

42.638.743.143.846.748.8----

Scientific Reasoning

Humanity's Last Exam (without tools)

Expert-level reasoning

32.028.431.939.041.139.538.541.3

GPQA Diamond

Graduate-level science QA

87.386.787.292.989.591.189.691.1

CritPt

Frontier physics reasoning

8.63.15.43.720.921.022.916.9

General

AA-LCR

Long-context reasoning

76.071.073.380.376.778.373.377.0

AA-Omniscience Accuracy

Factual accuracy

23.023.042.017.024.043.045.040.0

AA-Omniscience Non-Hallucination

Non-hallucination rate

74.770.032.082.074.07.010.061.0

Scores in %. SWE-Atlas-QnA and SWE Bench Pro: strict = no internet. BrowseComp: different models use different harness, we use the Discard-all@95k context length proposed in DeepSeek-V3.2 technical report. WildClawBench: we use a subset of the English text-only-modality tasks. Apex-Agents: we use a subset of text-only-modality tasks. GDPVal-AA is the Elo rating.

Quickstart

Serving

vLLM, recipe at recipes.vllm.ai/IFM:

vllm serve IFM/K2-Horizon-375B-A23B \
  --revision main \
  --tensor-parallel-size 8 \
  --enable-expert-parallel \
  --trust-remote-code \
  --dtype bfloat16 \
  --max-model-len 131072 \
  --reasoning-parser k2_horizon \
  --tool-call-parser k2_horizon \
  --enable-auto-tool-choice

SGLang recipe validated on 8× H200 in the SGLang K2 Horizon cookbook:

python3 -m sglang.launch_server \
  --model-path IFM/K2-Horizon-375B-A23B \
  --revision main \
  --tp 8 \
  --ep 8 \
  --dtype bfloat16 \
  --attention-backend fa3 \
  --model-loader-extra-config '{"enable_multithread_load":false}' \
  --reasoning-parser k2_horizon \
  --tool-call-parser k2_horizon \
  --host 0.0.0.0 --port 30000

API Usage

[!Tip]
Recommended settings: reasoning_effort="high", temperature=1.0, top_p=0.95.
Reasoning depth is selected per request through chat_template_kwargs. Thinking is returned in reasoning_content and the answer in content.
from openai import OpenAI

client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")
response = client.chat.completions.create(
    model="IFM/K2-Horizon-375B-A23B",
    messages=[{"role": "user", "content": "Explain the result step by step."}],
    temperature=1.0,
    top_p=0.95,
    max_tokens=32768,
    extra_body={"chat_template_kwargs": {"reasoning_effort": "high"}},
)
message = response.choices[0].message
print("Reasoning:", getattr(message, "reasoning_content", None))
print("Answer:", message.content)

Transformers

Validated with Transformers 4.57.6, PyTorch 2.13.0, Safetensors 0.8.0.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "IFM/K2-Horizon-375B-A23B"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id, device_map="auto", dtype="bfloat16", low_cpu_mem_usage=True, trust_remote_code=True
)

inputs = tokenizer("Explain why long-context evaluation is difficult.", return_tensors="pt").to(model.device)
inputs.pop("token_type_ids", None)
outputs = model.generate(**inputs, max_new_tokens=32768, temperature=1.0, top_p=0.95, do_sample=True)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Best Practices

1. Reasoning effort: always high. All reported results use high reasoning effort. Pass {"chat_template_kwargs": {"reasoning_effort": "high"}} on every request.

2. Sampling parameters. temperature=1.0, top_p=0.95.

3. Serving. Use the validated SGLang recipe above: BF16, TP=8 on one 8× H200 node, FlashAttention-3, with multithreaded weight loading disabled. Full recipes for every K2-Horizon size, with measured H200 latency and throughput, are in the SGLang cookbook and the vLLM recipe.

4. Parsers. Enable the k2_horizon reasoning parser for chat, and add the k2_horizon tool-call parser for agent use. Leave both off for plain completion-style generation.

Citation

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