[whatmodel.sh](../../README.md) / Command A Reasoning (08-2025)
# Command A Reasoning (08-2025)
Cohere
**score 54.5** ±2.0 · #294 of 732 · from 6 benchmark results across 2 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Command A Reasoning (08-2025) among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃▆█●▅▅▄▄▂▁ #163 of 313 59% 3
coding ▁▁▁▃▅▇█●▅▄▃▁ #192 of 469* 9.0% 1
math ▁▁▃▅█●▄█▄▂▁▁ #229 of 431* 24% 1
agent tasks · #163 of 313 · 59% 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), [Jamba 1.5 Large](jamba-1-5-large.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
τ²-bench Telecom (maker) 37.0% ▁·▅●▆▅▂▃▃▂▃▁ beats 30/89 hard · avg 26% 27% sharp · at its level · small run
BFCL V4 (December 2025 archive) 57.1% ▁▁▁▂██▅▅●▂▂▂ beats 68/77 hard · avg 31% 20% at its level
Terminal-Bench Hard (maker) 3.0% ▂▅●▂▂▆▂▂█▅▅▂ beats 4/27 very hard · avg 4.3% 13% sharp · too hard for it · small run
coding · #192 of 469 · 9.0% of its score
Beats [Gemini 2.5 Flash](gemini-2-5-flash.md), [Devstral 2 (2512)](devstral-2.md), [Qwen3 30B-A3B Thinking 2507](qwen3-30b-a3b-thinking-2507.md) here, though they rank higher overall. Trails [QwQ-32B-Preview](qwq-32b-preview.md), [Ministral 3 8B Reasoning 2512](ministral-3-8b-reasoning.md), [OpenThinker 32B](openthinker-32b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
SciCode (level unspecified) (make… 30.0% ▁····▃▃●▅█▆▂ beats 12/40 hard · avg 19% 9.0% small run
math · #229 of 431 · 24% of its score
Beats [MiniMax-M2.1](minimax-m2-1.md), [GPT-4o (2024-08-06)](gpt-4o-2024-08-06.md), [Command A (03-2025)](command-a.md) here, though they rank higher overall. Trails [Gemma 7B (base)](gemma-7b-base.md), [Phi-2 (2.7B)](phi-2.md), [Granite 8B Code Base (4K)](granite-8b-code-base-4k.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
AIME 2025 (maker) 57.0% ▁·▁▁▁▂▅●█▇▅▁ beats 43/133 medium · avg 47% 24% sharp · at its level · small run
other tests (1)
test result vs the others in its run difficulty counts why
IFBench prompt-level (loose) (mak… 36.0% ●········██▅ beats 0/8 medium · avg 42% 6.0% at its level · small run
source results share of the score
Cohere vendor-reported benchmark scores… 5 79% ▇▇▇▇▇▇▇▇▇
Berkeley Function Calling Leaderboard r… 1 20% ▇▇
prior: pull toward the middle 2% ▇
weighted average = 54.5
Licences: Berkeley Function Calling Leaderboard result archive ([Apache-2.0](../../licenses/bfcl-result-archive-v4-Apache-2.0.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 Command A Reasoning (08-2025) (Q4_K_M, needs ~67.1 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/CohereLabs_command-a-reasoning-08-2025-GGUF/tree/f87d047e4df129d235c84bb86ab22409a0385db8))
```sh
llama cli -hf bartowski/CohereLabs_command-a-reasoning-08-2025-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf bartowski/CohereLabs_command-a-reasoning-08-2025-GGUF:Q4_K_M
```
Other versions: Q8_0 (~118 GB) `llama cli -hf bartowski/CohereLabs_command-a-reasoning-08-2025-GGUF:Q8_0` · IQ2_XXS (~30.5 GB) `llama cli -hf bartowski/CohereLabs_command-a-reasoning-08-2025-GGUF:IQ2_XXS`
### 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 Command A Reasoning (08-2025) (Q4_K_M, needs ~67.1 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/CohereLabs_command-a-reasoning-08-2025-GGUF/tree/f87d047e4df129d235c84bb86ab22409a0385db8))
```sh
lms get https://huggingface.co/bartowski/CohereLabs_command-a-reasoning-08-2025-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: Q8_0 (~118 GB) `lms get https://huggingface.co/bartowski/CohereLabs_command-a-reasoning-08-2025-GGUF@Q8_0 -y` · IQ2_XXS (~30.5 GB) `lms get https://huggingface.co/bartowski/CohereLabs_command-a-reasoning-08-2025-GGUF@IQ2_XXS -y` · 8bit (~118 GB, Mac only) `lms get https://huggingface.co/mlx-community/command-a-reasoning-08-2025-8bit -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 Command A Reasoning (08-2025) (IQ2_XXS, needs ~30.5 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/CohereLabs_command-a-reasoning-08-2025-GGUF/tree/f87d047e4df129d235c84bb86ab22409a0385db8))
```sh
ollama run hf.co/bartowski/CohereLabs_command-a-reasoning-08-2025-GGUF:IQ2_XXS
```
### 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 Command A Reasoning (08-2025) (8bit, needs ~118 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/command-a-reasoning-08-2025-8bit/tree/ab4a70cf5857f4515443c086833fb520a7071dc1))
```sh
mlx_lm.chat --model mlx-community/command-a-reasoning-08-2025-8bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/command-a-reasoning-08-2025-8bit
```
Model repository and documentation: [huggingface.co/CohereLabs](https://huggingface.co/CohereLabs/command-a-reasoning-08-2025)
[interactive version](https://whatmodel.sh/model/command-a-reasoning-08-2025) · [JSON](https://whatmodel.sh/api?format=json&all=1)