NeoHorse is TokenRhythm's family of open-weight models for agent workflows. NeoHorse-1 provides 4B and 9B language models for tool use, coding, and instruction following. NeoHorse-Jev builds on NeoHorse-1-4B to make structured decisions through prefill-only inference.
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[2026-09-24] 🚀 NeoHorse-Jev-4B is here! Our new decision model brings Choice, Noul, and Score to the NeoHorse family. Explore the release.
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[2026-09-09] 📄 Technical report on arXiv! Our technical report is now available, covering the routing harness, agentic post-training, and evaluation of NeoHorse-1.
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[2026-09-08] 📦 GGUF and quantized models on Hugging Face! We release NeoHorse-1-4B-GGUF and NeoHorse-1-9B-GGUF. Both include 16-bit (BF16) weights and smaller 8-bit, 5-bit, and 4-bit quantized versions that use less disk space and memory, making it easier to run NeoHorse on your own hardware. MLX versions for Apple silicon are also available.
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[2026-09-08] 🚀 Now on ModelScope! NeoHorse-1-4B and NeoHorse-1-9B are now available on ModelScope.
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[2026-09-07] 🎉 NeoHorse-1 is here! We release NeoHorse-1-4B and NeoHorse-1-9B under the Apache 2.0 license.
NeoHorse-Jev-4B turns application state into decisions and probabilities. Built on NeoHorse-1-4B, it uses prefill-only inference to select an action with Choice, check a condition with Noul, or assign a rating with Score. Applications define the questions and possible answers, then use the results to route requests, select tools, or control a workflow.
In our evaluation, it scores 77.70 across six text benchmark groups, the highest among the four open-weight decision models with complete results in the comparison. Across Nimble, VitaminC, and MASSIVE, it reaches 83.26% mean accuracy, 11.50 percentage points above NeoHorse-1-4B. See the full comparison and evaluation protocols.
| Model | JevBench | Kev | OpenJev text | Nimble | VitaminC | MASSIVE | AVG |
|---|---|---|---|---|---|---|---|
| Open-Jev-9B | 77.13 | 77.87 | 65.39 | 80.50 | 68.28 | 84.86 | 75.67 |
| Kev-4B | 73.71 | 81.47 | 54.75 | 73.40 | 76.46 | 85.71 | 74.25 |
| Laya English | 55.82 | 61.30 | 40.07 | 45.04 | 78.63 | 68.57 | 58.24 |
| Laya Typed Decisions | -- | -- | -- | 48.94 | 78.30 | 65.43 | -- |
| NeoHorse-1-4B | -- | -- | -- | 69.15 | 63.27 | 82.86 | -- |
| NeoHorse-Jev-4B | 75.73 | 81.92 | 58.74 | 87.23 | 77.13 | 85.43 | 77.70 |
Scores are on a 0–100 scale; higher is better. Bold scores mark the best result and underlined scores the second-best among the listed entries. AVG is the equal-weight mean of the six benchmark groups. -- indicates unavailable results; only models with all six groups are ranked by AVG.
Tetris, Snake, robot manipulation, Mahjong, four-player bomb arena, and autonomous driving — left to right, top to bottom.
NeoHorse-1 is a family of causal language models and an initial prototype on the path toward recursive self-improvement (RSI). The 4B and 9B checkpoints are post-trained from Qwen3.5 for text-based agent harnesses, tool use, coding, and instruction following.
The routing harness assigns tasks to a heterogeneous model pool, records tool interactions and outcomes, estimates capability demand, and feeds capability-level feedback into the next training mixture. Updated models can return to the harness, forming a prototype evaluation–selection–update loop; extending this loop across successive iterations is the next step toward RSI.
- Agentic post-training framework: routing-guided curriculum SFT and routing-guided on-policy distillation turn execution trajectories into training signal while preserving execution and harness context.
- Data quality: exact and near-duplicate removal, evaluation decontamination, structural validation, six-dimensional semantic evaluation, and subscene-level Scene/Goal/Outcome labeling.
- Two release sizes: 4B for a lighter local deployment footprint and 9B for higher capacity on the same text-first serving interface.
| Checkpoint | Parameters | Hugging Face | ModelScope | Base model |
|---|---|---|---|---|
| NeoHorse-Jev-4B | ~4B | NeoHorse-Jev-4B | NeoHorse-Jev-4B | NeoHorse-1-4B |
| NeoHorse-1-4B | ~4B | NeoHorse-1-4B | NeoHorse-1-4B | Qwen3.5-4B |
| NeoHorse-1-9B | ~9B | NeoHorse-1-9B | NeoHorse-1-9B | Qwen3.5-9B |
NeoHorse-1 checkpoints use a text input/text output interface. NeoHorse-Jev returns structured decisions and probabilities; download its complete model bundle and use the matching runtime or backend adapter. Each model card contains evaluation results and deployment instructions for that model.
The specifications below cover NeoHorse-1-4B and NeoHorse-1-9B. Details for the decision model are in the NeoHorse-Jev documentation.
| Property | NeoHorse-1-4B | NeoHorse-1-9B |
|---|---|---|
| Model family | NeoHorse Agent-Native Causal Language Model | NeoHorse Agent-Native Causal Language Model |
| Parameters | Approximately 4B | Approximately 9B |
| Post-training | Routing-guided agentic post-training | Routing-guided agentic post-training |
| Interface | Text input and text output | Text input and text output |
| Context length | 262,144 natively; base capability extensible up to 1,010,000 tokens | 262,144 natively; base capability extensible up to 1,010,000 tokens |
| Weight format / precision | Safetensors / BF16 | Safetensors / BF16 |
The tables report NeoHorse-1 results under the ten-benchmark protocol from the technical report. Results are grouped by capability. Higher is better; Δ is NeoHorse minus the same-size Qwen baseline. Bold marks the best result in each benchmark row; ties share the same formatting. For structured decision benchmarks, see the NeoHorse-Jev evaluation.
The 4B comparison includes five representative open-weight baselines. Underline marks the second-best result in each benchmark row.
| Benchmark | Qwen3.5-4B | Gemma-4-E4B-it | Nanbeige-4.2-3B | Agents-A1-4B | Spark-X2.5-4B | NeoHorse-1-4B | Δ vs Qwen3.5-4B |
|---|---|---|---|---|---|---|---|
| 🤖 Agentic | |||||||
| QwenClawBench | 38.47 | 22.98 | 40.66 | 43.16 | 43.52 | 44.68 | +6.21 |
| WorkBuddy Bench | 24.62 | 11.65 | 21.03 | 33.37 | 26.47 | 34.41 | +9.79 |
| PinchBench | 71.19 | 47.60 | 66.78 | 75.07 | 62.37 | 77.33 | +6.14 |
| VitaBench | 21.50 | 5.00 | 31.50 | 39.25 | 37.00 | 32.00 | +10.50 |
| BFCL v4 | 61.02 | 47.18 | 67.28 | 46.60 | 63.71 | 61.79 | +0.77 |
| tau2-Bench | 84.29 | 43.60 | 85.08 | 81.00 | 77.72 | 88.46 | +4.17 |
| 💻 Coding | |||||||
| HumanEval | 87.20 | 84.76 | 98.78 | 92.68 | 92.07 | 96.95 | +9.75 |
| LiveCodeBench v6 | 53.71 | 52.00 | 72.50* | 56.57 | 54.86 | 59.43 | +5.72 |
| 📚 Instruction Following | |||||||
| IFBench | 60.33 | 40.00 | 55.00 | 63.33 | 73.33 | 65.33 | +5.00 |
| IFEval | 87.06 | 74.68 | 84.47 | 83.55 | 91.13 | 88.35 | +1.29 |
| 📊 Overall | |||||||
| Ten-benchmark average | 58.94 | 42.95 | 62.31 | 61.46 | 62.22 | 64.87 | +5.93 |
The 9B comparison includes five representative open-weight baselines from the technical report. Underline marks the second-best result in each benchmark row.
| Benchmark | Granite-4.2-8B | Qwen3.5-9B | Ornith-1.5-9B | Gemma-4-12B-it | Muse-Glimmer-30B | NeoHorse-1-9B | Δ vs Qwen3.5-9B |
|---|---|---|---|---|---|---|---|
| 🤖 Agentic | |||||||
| QwenClawBench | 37.01 | 44.04 | 47.27 | 43.53 | 46.11 | 48.73 | +4.69 |
| WorkBuddy Bench | 35.07 | 39.60 | 29.29 | 29.65 | 45.85 | 40.15 | +0.55 |
| PinchBench | 56.93 | 74.55 | 68.22 | 58.89 | 71.35 | 82.25 | +7.70 |
| VitaBench | 23.00 | 31.25 | 26.75 | 36.50 | 48.50 | 42.25 | +11.00 |
| BFCL v4 | 52.06 | 64.88 | 65.03 | 62.06 | 53.74 | 67.43 | +2.55 |
| tau2-Bench | 62.28 | 88.04 | 83.68 | 59.37 | 76.64 | 90.82 | +2.78 |
| 💻 Coding | |||||||
| HumanEval | 96.34 | 92.68 | 93.90 | 100.00 | 98.17 | 98.17 | +5.49 |
| LiveCodeBench v6 | 72.00 | 65.14 | 47.43 | 73.14 | 65.71 | 65.14 | +0.00 |
| 📚 Instruction Following | |||||||
| IFBench | 78.00 | 66.33 | 40.00 | 77.67 | 78.67 | 66.33 | +0.00 |
| IFEval | 92.98 | 89.46 | 71.35 | 94.27 | 93.90 | 89.09 | -0.37 |
| 📊 Overall | |||||||
| Ten-benchmark average | 60.57 | 65.60 | 57.29 | 63.51 | 67.86 | 69.04 | +3.44 |
* Nanbeige-4.2-3B LiveCodeBench v6 result is reported from its official blog post or technical report.
Reported protocol: SGLang v0.5.17 ·
temperature=1.0·top_p=0.95·top_k=20·min_p=0.0·presence_penalty=1.5·repetition_penalty=1.0· thinking mode enabled withenable_thinking=trueandforce_nonempty_content=true. QwenClawBench, WorkBuddy Bench, and tau2-Bench use three runs; PinchBench and VitaBench use one run; the remaining benchmarks follow their official protocols. VitaBench uses the DeepSeek-V4-Flash simulator and judge.
For NeoHorse-Jev-4B, follow its deployment guide for vLLM, SGLang, or the native Python runtime, including Choice, Noul, and Score examples.
The examples below serve NeoHorse-1-4B or NeoHorse-1-9B downloaded to local disk. Set MODEL_PATH to the directory containing config.json, tokenizer files, and weights. The request model value is the served alias (neohorse-1-4B or neohorse-1-9B), not the filesystem path.
pip install "sglang==0.5.17"
MODEL_PATH="/path/to/NeoHorse-1-4B" # or /path/to/NeoHorse-1-9B
python3 -m sglang.launch_server \
--model-path "$MODEL_PATH" \
--served-model-name neohorse-1-4B \
--host 0.0.0.0 --port 30000 \
--context-length 262144 \
--reasoning-parser qwen3 \
--tool-call-parser qwen3_coderpip install -U vllm
MODEL_PATH="/path/to/NeoHorse-1-4B" # or /path/to/NeoHorse-1-9B
vllm serve "$MODEL_PATH" \
--served-model-name neohorse-1-4B \
--host 0.0.0.0 --port 8000 \
--max-model-len 262144 \
--reasoning-parser qwen3 \
--enable-auto-tool-choice \
--tool-call-parser qwen3_coderThe 262,144-token setting is a configured limit; actual capacity depends on GPU memory and serving settings. Reduce it if needed.
The examples below target vLLM at http://127.0.0.1:8000. For SGLang, use http://127.0.0.1:30000. For the 9B checkpoint, use --model neohorse-1-9B and launch the server with --served-model-name neohorse-1-9B.
Install the client dependency:
pip install requestsRun the provided chat example from the repository root:
python examples/chat.py \
--url http://127.0.0.1:8000 \
--model neohorse-1-4BThis example sends a predefined prompt to the deployed model and prints the generated response.
Alternatively, send an OpenAI-compatible request directly:
curl http://127.0.0.1:8000/v1/chat/completions \
-H 'Content-Type: application/json' \
-d '{"model":"neohorse-1-4B","messages":[{"role":"user","content":"Write a Python function that returns the first n Fibonacci numbers."}],"max_tokens":512}'Run the provided tool-calling example from the repository root:
python examples/tool_call.py \
--url http://127.0.0.1:8000 \
--model neohorse-1-4BThis example sends a predefined weather query and prints the tool call generated by the model.
NeoHorse-1 is released under the Apache License 2.0. The upstream models are Qwen3.5-4B and Qwen3.5-9B.
NeoHorse-Jev-4B is also released under Apache License 2.0. Its inference package includes components adapted from Kev; see the NeoHorse-Jev license and acknowledgments for attribution and bundled third-party notices.
@misc{neohorse2026,
title = {NeoHorse-1: Towards Recursive Self-Improvement via Agentic Post-Training with Routing Harness},
author = {NeoHorse Team},
year = {2026},
howpublished = {arXiv preprint},
eprint = {2609.08183},
archivePrefix = {arXiv},
primaryClass = {cs.CL},
url = {https://arxiv.org/abs/2609.08183}
}