| Open-weight dense models | ||||
|---|---|---|---|---|
| K2-Horizon-32B-Stage1 | Qwen3.8-27B | Muse Glimmer-30B | IBM Granite 4.2 30B | |
| # Params | 32B | 27B | 30B | 30B |
| # Activated params | 32B | 27B | 30B | 30B |
| Architecture | Dense | Dense | Dense | Dense |
| Agents | ||||
tau3-Banking Agentic tool use | 22.5 | 48.0 | 23.5 | 14.4 |
| Coding | ||||
Terminal-Bench 2.1 Agentic terminal use | 36.6 | 79.8 | 51.7 | 26.6 |
SciCode Scientific coding | 30.2 | 44.7 | 43.6 | 36.6 |
| Scientific Reasoning | ||||
Humanity's Last Exam (without tools) Expert-level reasoning | 22.8 | 33.9 | 22.0 | 11.2 |
GPQA Diamond Graduate-level science QA | 82.3 | 90.5 | 83.5 | 64.4 |
CritPt Frontier physics reasoning | 1.4 | 5.4 | 2.6 | 0.3 |
| General | ||||
AA-LCR Long-context reasoning | 65.3 | 77.3 | 80.0 | 46.7 |
AA-Omniscience Accuracy Factual accuracy | 16.8 | 15.6 | 27.0 | 10.1 |
AA-Omniscience Non-Hallucination Non-hallucination rate | 58.3 | 69.7 | 18.1 | 74.4 |
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](https://recipes.vllm.ai/IFM): ```shell 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](https://docs.sglang.io/cookbook/autoregressive/IFM/K2-Horizon): ```shell 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`. ```python 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", "tool_call_format": "xml"}}, ) message = response.choices[0].message print("Reasoning:", getattr(message, "reasoning_content", None)) print("Answer:", message.content) ``` Our model supports multiple tool calls formats, which can be changed with `chat_template_kwargs`. The supported values are `json`, `xml`, and `xml_typed` . The default is `xml`. Keep `--tool-call-parser k2_horizon` enabled to parse the selected format. ### Transformers Validated with Transformers 5.15.0, PyTorch 2.13.0, Safetensors 0.8.0. ```python 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](https://docs.sglang.io/cookbook/autoregressive/IFM/K2-Horizon) and the [vLLM recipe](https://recipes.vllm.ai/IFM). 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 ```bibtex @misc{k2horizon2026, title = {Introducing K2 Horizon: Frontier Performance, Radically Open}, author = {{IFM Team}}, year = {2026}, url = {https://ifm.ai/blog/k2/}, } ```