[whatmodel.sh](../../README.md) / Falcon-H1R 7B
# Falcon-H1R 7B
TII · released 2026-01 · 7.59B params · falcon-llm
**score 59.8** ±1.0 · #205 of 732 · from 12 benchmark results across 1 source · † no independently run results verified
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Falcon-H1R 7B among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃▆●▄▅▅▄▄▂▁ #199 of 313* 20% 2
coding ▁▁▁▃▅▇●▇▅▄▃▁ #213 of 469* 9.2% 2
math ▁▁▃▅█▆▄●▄▂▁▁ #126 of 431 38% 4
agent tasks · #199 of 313 · 20% of its score
Beats [o1 (2024-12-17)](o1-2024-12-17.md), [o3-mini](o3-mini.md), [DeepSeek R1 0528](deepseek-r1-0528.md) here, though they rank higher overall. Trails [SmolLM3 3B](smollm3-3b.md), [Jamba 1.5 Mini](jamba-1-5-mini.md), [Mellum2 12B-A2.5B Instruct](mellum2-12b-a2-5b-instruct.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
τ²-bench Telecom (maker) 25.4% ▁·▅●▆▅▂▃▃▂▃▁ beats 15/89 very hard · avg 11% 10% sharp · at its level · small run
Terminal-Bench Hard (maker) 4.9% ▂▅▆●▂▆▂▂█▅▅▂ beats 8/27 very hard · avg 1.3% 10% sharp · too hard for it
coding · #213 of 469 · 9.2% of its score
Beats [Qwen3.5 4B](qwen3-5-4b.md) here, though they rank higher overall. Trails [QwQ-32B-Preview](qwq-32b-preview.md), [Ministral 3 3B Reasoning 2512](ministral-3-3b-reasoning.md), [DeepSeek-Coder-V2 Instruct (0724)](deepseek-coder-v2-0724.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LiveCodeBench v6 (window unspecif… 68.6% █·▄·▄····●·▄ beats 4/5 medium · avg 41% 7.3% at its level
SciCode main problems (maker) 3.9% ▄··●····█▄·▄ beats 1/5 very hard · avg 3.8% 1.8% too hard for it
math · #126 of 431 · 38% of its score
Beats [Claude Sonnet 4.6](claude-sonnet-4-6.md), [MiniMax-M3](minimax-m3.md), [MiMo-V2.5 (310B-A15B)](mimo-v2-5.md) here, though they rank higher overall. Trails [Olmo 3.1 32B Think](olmo-3-1-32b-think.md), [Granite 4.2 8B](granite-4-2-8b.md), [Nemotron-Cascade-14B-Thinking](nemotron-cascade-14b-thinking.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HMMT 2025 (edition unspecified) (… 64.9% ▄··▄▄·▄····● beats 5/5 hard · avg 29% 12% sharp · at its level
AIME 2024 (maker) 88.1% █▄···█·····● beats 5/5 medium · avg 62% 11% sharp
AIME 2025 (maker) 83.1% ▄·█··▄····●▄ beats 4/5 medium · avg 52% 11% sharp
MATH-500 (maker) 97.4% ▄·▄·····█··● beats 5/5 very easy · avg 92% 4.3% maxed out
other tests (4)
test result vs the others in its run difficulty counts why
GPQA Diamond (maker) 61.3% ●·········▂▂ beats 2/5 hard · avg 44% 16% sharp · at its level
Humanity's Last Exam (maker) 11.1% █··██···█·█● beats 5/5 very hard · avg 3.7% 7.9% typical
MMLU-Pro (maker) 72.1% █··█··●██··█ beats 2/5 medium · avg 57% 6.8% at its level
IFBench (aggregation unspecified)… 53.4% ███···█●···█ beats 4/5 hard · avg 34% 2.0% at its level
source results share of the score
Falcon-H1R: Pushing the Reasoning Front… 12 100% ▇▇▇▇▇▇▇▇▇▇▇▇
prior: pull toward the middle 0% ▇
weighted average = 59.8
Licences: Falcon-H1R: Pushing the Reasoning Frontiers with a Hybrid Model for Efficient Test-Time Scaling ([CC BY 4.0](../../licenses/report-2601.02346v1.txt))
## How to run it
### llama.cpp (recommended)
1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md)
```sh
# macOS / Linux
curl -LsSf https://llama.app/install.sh | sh
# Windows
irm https://llama.app/install.ps1 | iex
```
2. Run Falcon-H1R 7B (gguf-q4_k_m, needs ~5.05 GB, from [huggingface.co/tiiuae](https://huggingface.co/tiiuae/Falcon-H1R-7B-GGUF/tree/main))
```sh
llama cli -hf tiiuae/Falcon-H1R-7B-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf tiiuae/Falcon-H1R-7B-GGUF:Q4_K_M
```
Other versions: gguf-q8_0 (~9.61 GB) `llama cli -hf tiiuae/Falcon-H1R-7B-GGUF:Q8_0`
### LM Studio
1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download)
2. Run Falcon-H1R 7B (gguf-q4_k_m, needs ~5.05 GB, from [huggingface.co/tiiuae](https://huggingface.co/tiiuae/Falcon-H1R-7B-GGUF/tree/main))
```sh
lms get https://huggingface.co/tiiuae/Falcon-H1R-7B-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: gguf-q8_0 (~9.61 GB) `lms get https://huggingface.co/tiiuae/Falcon-H1R-7B-GGUF@Q8_0 -y` · 4bit (~4.3 GB, Mac only) `lms get https://huggingface.co/mlx-community/Falcon-H1R-7B-4bit -y` · 8bit (~8.1 GB, Mac only) `lms get https://huggingface.co/mlx-community/Falcon-H1R-7B-8bit -y`
### Ollama
1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download)
```sh
# Linux
curl -fsSL https://ollama.com/install.sh | sh
```
2. Run Falcon-H1R 7B (gguf-q4_k_m, needs ~5.05 GB, from [huggingface.co/tiiuae](https://huggingface.co/tiiuae/Falcon-H1R-7B-GGUF/tree/main))
```sh
ollama run hf.co/tiiuae/Falcon-H1R-7B-GGUF:Q4_K_M
```
Other versions: gguf-q8_0 (~9.61 GB) `ollama run hf.co/tiiuae/Falcon-H1R-7B-GGUF:Q8_0`
### MLX (Mac)
1. Install MLX (Mac). Apple silicon Macs only, text only.
```sh
# with uv
uv tool install mlx-lm
# or
pip install mlx-lm
```
2. Run Falcon-H1R 7B (4bit, needs ~4.3 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Falcon-H1R-7B-4bit/tree/0e3ed6b0e4de5581f22500d2cfdc58e6f37568c6))
```sh
mlx_lm.chat --model mlx-community/Falcon-H1R-7B-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/Falcon-H1R-7B-4bit
```
Other versions: 8bit (~8.1 GB, Mac only) `mlx_lm.chat --model mlx-community/Falcon-H1R-7B-8bit`
Model repository and documentation: [huggingface.co/tiiuae](https://huggingface.co/tiiuae/Falcon-H1R-7B)
[interactive version](https://whatmodel.sh/model/falcon-h1r-7b) · [JSON](https://whatmodel.sh/api?format=json&all=1)