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IFM/K2-Horizon-32B

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Magnet

K2-Horizon-32B-Stage1

K2-Horizon-32B-Stage1 is the large dense member of the K2-Horizon family: a 32B decoder-only model with a 512K context window. Note: final checkpoint to be released.

K2-Horizon-32B-Stage1 Highlights

  • Strong dense baseline. A 32B dense model evaluated on the same agentic, coding, and reasoning benchmarks as the rest of the family (see Benchmark Results). Results are for stage 1 of the final model training; results for stage 2 will be out soon.
  • 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 dense models

K2-Horizon-32B-Stage1Qwen3.8-27BMuse Glimmer-30BIBM Granite 4.2 30B

Params32B27B30B30B

Activated params32B27B30B30B

ArchitectureDenseDenseDenseDense

Agents

tau3-Banking

Agentic tool use

22.548.023.514.4

Coding

Terminal-Bench 2.1

Agentic terminal use

36.679.851.726.6

SciCode

Scientific coding

30.244.743.636.6

Scientific Reasoning

Humanity's Last Exam (without tools)

Expert-level reasoning

22.833.922.011.2

GPQA Diamond

Graduate-level science QA

82.390.583.564.4

CritPt

Frontier physics reasoning

1.45.42.60.3

General

AA-LCR

Long-context reasoning

65.377.380.046.7

AA-Omniscience Accuracy

Factual accuracy

16.815.627.010.1

AA-Omniscience Non-Hallucination

Non-hallucination rate

58.369.718.174.4

Scores in %. Bold marks the best score in each row. Sections follow the Artificial Analysis Intelligence Index categories. Baseline scores are from Artificial Analysis; Muse Glimmer-30B at high reasoning effort, other open models in their reasoning mode.

Quickstart

Serving

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

vllm serve IFM/K2-Horizon-32B \
  --revision main \
  --model-impl vllm \
  --tensor-parallel-size 2 \
  --trust-remote-code \
  --dtype bfloat16 \
  --max-model-len 131072 \
  --reasoning-parser k2_horizon \
  --enable-auto-tool-choice \
  --tool-call-parser k2_horizon

SGLang recipe validated on 2× H200 in the SGLang K2 Horizon cookbook:

python3 -m sglang.launch_server \
  --model-path IFM/K2-Horizon-32B \
  --revision main \
  --tp 2 \
  --dtype bfloat16 \
  --attention-backend fa3 \
  --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-32B",
    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 5.15.0, PyTorch 2.13.0, Safetensors 0.8.0.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "IFM/K2-Horizon-32B"
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=2, FlashAttention-3. 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/},
}