# K2-Horizon-3.7B ![Organization](https://img.shields.io/badge/Org-IFM-555) ![Context](https://img.shields.io/badge/Context-512K%20tokens-1f6feb) ![Pricing](https://img.shields.io/badge/Price-Open%20weights-2da44e) ![License](https://img.shields.io/badge/License-Apache%202.0-d29922) ![Modality](https://img.shields.io/badge/Modality-Text-8957e5) ![Released](https://img.shields.io/badge/Released-Sep%202026-555) > **Quick answer:** K2-Horizon-3.7B is IFM's small dense model — 3.7B parameters with a native 512K-token context window. It leads its comparison set (Qwen3.5-4B, G9v3-3B, Granite 4.2-3B, Nemotron 3 Nano-4B) on SWE-bench Verified (68.6%) and HMMT Feb 2026 (70.5%), though it trails on GPQA Diamond and BFCL v4 function calling. Open weights, Apache 2.0. ## At a Glance **Where K2-Horizon-3.7B leads** - Dominant SWE-bench Verified score (68.6%) versus all four comparison models (next best: Qwen3.5-4B at 41.2%) - Strong competition-math result on HMMT Feb 2026 (70.5%), well ahead of Qwen3.5-4B (61.6%) - 512K context in a sub-4B model **Where it lags** - GPQA Diamond (65.4%) trails Qwen3.5-4B (77.1%) - BFCL v4 function calling (50.9%) trails Qwen3.5-4B (55.7%) and Granite 4.2-3B (50.8%) narrowly - τ³-Banking data is thin for comparison models (two show no reported score) **Best for:** lightweight, self-hosted coding-agent workloads where SWE-bench-style task completion matters more than broad science QA. ## What K2-Horizon-3.7B Is K2-Horizon-3.7B is the small dense model in IFM's K2-Horizon family, evaluated on the same agentic, coding, and reasoning benchmark suite as its larger siblings. Like the 7B model, it ships with intermediate checkpoints and public training data/recipe/code. Its benchmark profile — strong on SWE-bench and competition math, comparatively weaker on GPQA and function-calling — suggests targeted tuning toward coding-agent tasks rather than uniform improvement across all domains. ## Specifications | Field | Value | |---|---| | Organization | IFM | | Parameters | 3.7B (dense) | | Context window | 524,288 tokens (512K) | | Architecture | Dense decoder-only | | License | Apache 2.0 | | Release date | 2026-09 | | Modality | Text | ## Pricing | | Input (per 1M tokens) | Output (per 1M tokens) | |---|---|---| | Open weights | — | — | Open weights: free to download and self-host. Validated SGLang/vLLM recipe: single-GPU (TP=1). ## Context Window K2-Horizon-3.7B has a **524,288-token (512K) context window**, native from the midtraining stages onward. ## Public Benchmark Scores | Benchmark | Score | Source | Date | |---|---|---|---| | [HMMT 2026](/benchmarks/hmmt/hmmt-2026) | 70.5 | [IFM model card](https://huggingface.co/IFM/K2-Horizon-3.7B) | 2026-09 | | [SWE-bench Verified](/benchmarks/princeton-nlp-openai/swe-bench-verified) | 68.6 | [IFM model card](https://huggingface.co/IFM/K2-Horizon-3.7B) | 2026-09 | | [GPQA Diamond](/benchmarks/rein-et-al/gpqa-diamond) | 65.4 | [IFM model card](https://huggingface.co/IFM/K2-Horizon-3.7B) | 2026-09 | | [Humanity's Last Exam](/benchmarks/scale-ai/humanitys-last-exam) | 12.9 | [IFM model card](https://huggingface.co/IFM/K2-Horizon-3.7B) | 2026-09 | | [SciCode](/benchmarks/scicode-team/scicode) | 25.9 | [IFM model card](https://huggingface.co/IFM/K2-Horizon-3.7B) | 2026-09 | | [TerminalBench 2.1](/benchmarks/tbench/terminalbench-21) | 25.1 | [IFM model card](https://huggingface.co/IFM/K2-Horizon-3.7B) | 2026-09 | | [τ³ Banking](/benchmarks/tau-bench/tau3-banking) | 17.7 | [IFM model card](https://huggingface.co/IFM/K2-Horizon-3.7B) | 2026-09 | | [BFCL v4](/benchmarks/bfcl-team/bfcl-v4) | 50.9 | [IFM model card](https://huggingface.co/IFM/K2-Horizon-3.7B) | 2026-09 | *Scores above are reported by IFM and shown for context; not Benchgen measurements. Baseline protocols for comparison models may differ.* ## Use K2-Horizon-3.7B via API ```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-3.7B", 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"}}, ) print(response.choices[0].message.content) ``` ## Frequently Asked Questions

What is K2-Horizon-3.7B?

It's IFM's small dense model in the K2-Horizon family: 3.7B parameters, 512K-token context, with a standout SWE-bench Verified score for its size class.

What is K2-Horizon-3.7B's context window?

524,288 tokens (512K).

How much does K2-Horizon-3.7B cost?

Open weights — free to download; cost is hosting/inference only.

Is K2-Horizon-3.7B open source?

Yes, released under the Apache 2.0 license.

--- *Specs and scores sourced from IFM's official Hugging Face model card; third-party benchmark scores attributed inline. Last updated 2026-09-03.*

K2 Horizon 3.7B — Training jobs

Training jobs