[whatmodel.sh](../../README.md) / Command A+ (05-2026) # Command A+ (05-2026) Cohere · released 2026-05 · 218B params (25B active) · apache-2.0 **score 63.7** ±1.0 · #158 of 732 · from 23 benchmark results across 3 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+ (05-2026) among every model on that task or test, worst to best. 
task                vs the others         rank  counts  tests
agent tasks         ▁▁▃▆█▄●▅▄▄▂▁   #145 of 313     24%      6
coding              ▁▁▁▃▅▇█●▅▄▃▁   #159 of 469    9.2%      3
math                ▁▁▃▅█▆▄●▄▂▁▁   #123 of 431     17%      3
vision & documents  ······●·····      #1 of 1*    5.9%      2
agent tasks · #145 of 313 · 24% of its score Beats [o3](o3.md), [Gemini 2.5 Pro](gemini-2-5-pro.md), [DeepSeek V3.2 Exp](deepseek-v3-2-exp.md) here, though they rank higher overall. Trails [SmolLM3 3B](smollm3-3b.md), [Jamba 1.5 Large](jamba-1-5-large.md), [GPT-4o (2024-05-13)](gpt-4o-2024-05-13.md) here, though they rank lower overall. 
test                      result  vs the others in its run  difficulty            counts  why
Terminal-Bench Hard        25.0%  ▄········●█▄ beats 1/5    very hard · avg 4.6%    8.3%  sharp · several runs
τ²-bench Telecom (maker)   85.0%  ▁·▅█▆▅▂●▃▂▃▁ beats 63/89  hard · avg 26%          5.7%  sharp · at its level · small run
APEX-Agents (pass@1)        1.6%  ●▄····▄···█▄ beats 0/5    very hard · avg 1.9%    3.5%  too hard for it
MCP-Atlas (public set)     27.2%  ●▄▄·······█▄ beats 0/5    hard · avg 19%          3.1%  at its level
SWE-bench Verified         14.4%  ●··········█ beats 0/5    hard · avg 15%          2.6%  sharp · at its level · noisy
τ³-bench Banking            5.8%  ●·▄▄······▄▄ beats 1/5    very hard · avg 5.3%    1.0%  blunt
coding · #159 of 469 · 9.2% of its score Beats [Qwen3-Coder-Next (80B-A3B)](qwen3-coder-next.md), [Gemini 2.5 Pro](gemini-2-5-pro.md), [Gemini 3.1 Flash-Lite](gemini-3-1-flash-lite.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), [Phi-4-reasoning 14B](phi-4-reasoning.md) here, though they rank lower overall. 
test                                result  vs the others in its run  difficulty        counts  why
LiveCodeBench v6 (window unspecif…   86.1%  ▄······▄●▄·█ beats 2/5    medium · avg 50%    4.3%  typical
SWE-bench Verified                   14.4%  ●··········█ beats 0/5    hard · avg 15%      2.6%  sharp · at its level · noisy
SciCode (level unspecified) (make…   38.0%  ▁····▃▃▄▅●▆▂ beats 25/40  hard · avg 19%      2.3%  at its level · small run
math · #123 of 431 · 17% 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), [gpt-oss-120b](gpt-oss-120b.md) here, though they rank lower overall. 
test                result  vs the others in its run    difficulty        counts  why
AIME 2026            96.0%  ▄▄··▄····●·▄ beats 4/5      medium · avg 65%    8.3%  sharp
AIME 2025 (maker)    90.0%  ▁·▁▁▁▂▅▆█▇●▁ beats 116/133  medium · avg 47%    4.5%  sharp · small run
HMMT February 2026   73.5%  █▄●········█ beats 3/5      hard · avg 31%      3.8%  sharp · at its level · noisy
vision & documents · #1 of 1 · 5.9% of its score 
test                                result  vs the others in its run   difficulty      counts  why
CharXiv (reasoning questions) (ma…   52.7%  ▂▅▃▅●▆▆▅▆█▆▂ beats 32/101  hard · avg 32%    3.0%  at its level · small run
CharXiv descriptive questions (ma…   88.0%  ▂▄▄▅▇▄█●▇▂▁▂ beats 43/59   easy · avg 70%    2.9%  small run
other tests (9) 
test                                result  vs the others in its run  difficulty            counts  why
GPQA Diamond                         75.6%  █···●█·█··██ beats 1/5    hard · avg 41%           21%  sharp · at its level
MMLU-Pro                             79.0%  ●·▄▄····▄··█ beats 0/5    medium · avg 55%        6.7%  typical
Humanity's Last Exam                 11.4%  ●▄······▄·▄▄ beats 0/5    very hard · avg 4.7%    6.1%  typical
IFBench (aggregation unspecified)    73.9%  ▄···▄▄··●··█ beats 3/5    medium · avg 43%        4.1%  at its level
MultiChallenge                       45.8%  █··●·▄·····█ beats 2/5    hard · avg 25%          3.2%  at its level
WMT25 Terminology Translation (te…   68.7%  ▂·●··▂·▄▂·█▆ beats 1/12   medium · avg 63%        1.8%  noisy
IFBench prompt-level (loose) (mak…   74.0%  ▃········●█▅ beats 2/8    medium · avg 42%        1.3%  at its level · small run
WMT24++ (maker)                      81.0%  █·▃·▅▅▃···▃● beats 10/10  easy · avg 70%          0.9%  blunt
WMT26 General MT (GEMBA-ESA, LLM …   76.5%  ▄·▄▄▄·▄●█▄█▄ beats 6/12   medium · avg 65%        0.7%  blunt · at its level
## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its *counts* column above; here it is added up by who ran the tests. [Method](../method.md) 
source                                    results  share of the score
Solar Open 2 Technical Report                  13   72% ▇▇▇▇▇▇▇▇▇
Cohere vendor-reported benchmark scores…        7   24% ▇▇▇
Cohere (submitted / maker-reported)             3    3% ▇
prior: pull toward the middle                        0% ▇
weighted average                                   = 63.7
Licences: Solar Open 2 Technical Report ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · Cohere ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) ## How to run it ### Through an API api (Cohere) · price unknown · [docs.cohere.com](https://docs.cohere.com/docs/command-a-plus) [context source](https://docs.cohere.com/docs/models): Cohere API model table: command-a-plus-05-2026 context length 128k (stated rounded), maximum output 64k tokens. Observed 2026-09-25. ### 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+ (05-2026) (Q4_K_M, needs ~131.3 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/command-a-plus-05-2026-GGUF/tree/main)) ```sh llama cli -hf bartowski/command-a-plus-05-2026-GGUF:Q4_K_M ``` 3. Or start a local server and open the address it prints (http://localhost:9931) ```sh llama serve -hf bartowski/command-a-plus-05-2026-GGUF:Q4_K_M ``` Other versions: IQ2_M (~65.9 GB) `llama cli -hf bartowski/command-a-plus-05-2026-GGUF:IQ2_M` ### 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+ (05-2026) (Q4_K_M, needs ~131.3 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/command-a-plus-05-2026-GGUF/tree/main)) ```sh lms get https://huggingface.co/bartowski/command-a-plus-05-2026-GGUF@Q4_K_M -y ``` 3. Then run lms ls to see its name, and lms chat followed by that name Other versions: IQ2_M (~65.9 GB) `lms get https://huggingface.co/bartowski/command-a-plus-05-2026-GGUF@IQ2_M -y` Model repository and documentation: [huggingface.co/CohereLabs](https://huggingface.co/CohereLabs/command-a-plus-05-2026-bf16) [interactive version](https://whatmodel.sh/model/command-a-plus) · [JSON](https://whatmodel.sh/api?format=json&all=1)