Cohere Command A
B Tier · 7.5/10
Cohere's enterprise-multilingual flagship -- 111B params, 256K context, runs on 2x H100. 23 languages. CC-BY-NC 4.0 on weights (research / non-commercial), commercial requires Cohere enterprise contract. Follow-ups: Command A Reasoning + Command A Vision
Score Breakdown
The Good and the Bad
What we like
- +Best-in-class multilingual open-weight model for enterprise -- 23-language coverage with consistent quality (including Arabic, Korean, Japanese, Hindi, European languages) beats Mistral and Llama for genuinely global enterprise deployment
- +Runs on just 2x H100 at FP16 for the full 111B model -- much lower infrastructure bar than DeepSeek V3.2 (671B MoE needs 8x H100) or GLM-5.1 (744B MoE). Makes 'run your own frontier model on-prem' realistically achievable for mid-size enterprises
- +256K context natively, strong retrieval-augmented generation tuning, and enterprise-grade tool use. Cohere's enterprise heritage shows -- the model is tuned for real business workflows (RAG over internal docs, multi-language customer support, structured extraction) rather than consumer chat
- +Command A Reasoning + Command A Vision ship as family members, not separate products -- one evaluation / one procurement / one support contract covers reasoning, vision, and multilingual coverage. Simpler enterprise procurement story than piecing together three different vendors
What could be better
- −CC-BY-NC 4.0 on weights means the free self-hosted version is research / non-commercial only -- commercial deployment requires a Cohere enterprise contract. This is a material difference versus Apache-2.0 competitors (Llama, Qwen, GLM, Granite, Arcee Trinity, gpt-oss). Worth flagging clearly for procurement
- −Cohere has pivoted away from the consumer chatbot market -- there is no Cohere consumer product that competes with ChatGPT / Claude / Gemini. Command A is positioned squarely for enterprise developers, which means less community buzz and fewer third-party fine-tunes
- −Absolute benchmarks don't top DeepSeek, GLM, or Qwen flagships -- Command A is optimized for enterprise multilingual RAG, not for peak reasoning or code benchmarks. Pick on fit, not on leaderboard
- −Smaller open-source community than Llama or Qwen. Ollama / llama.cpp support is present but less aggressively optimized. Cohere's own API is the polished path
Pricing
Self-hosted (CC-BY-NC 4.0, research only)
- ✓Research + non-commercial use only
- ✓Weights on Hugging Face
- ✓2x H100 minimum for full-capability deployment
- ✓Fine-tuning permitted for research purposes
Cohere API
- ✓Full commercial deployment via Cohere's hosted API
- ✓Enterprise SLAs, data residency, private deployments available
- ✓Command A Reasoning + Command A Vision sold as part of the family
Cohere Enterprise contract
- ✓Required for commercial self-hosting / on-premises deployment
- ✓Private cloud / air-gapped options available
- ✓Governance + compliance add-ons for regulated industries
System Requirements
Hardware needed to self-host. Min = smallest viable setup (usually heavy quantization). Max = full-precision / production-grade.
| Model variant | Min | Max |
|---|---|---|
| Command A (111B)CC-BY-NC 4.0 on weights (research only). Commercial requires Cohere enterprise contract | 2× H100 80 GB FP16 | 4× H100 for production serving with multi-tenant load |
Known Issues
- CC-BY-NC 4.0 licensing creates commercial-use ambiguity -- research and evaluation are fine on self-hosted weights, but production / revenue-generating deployment requires a Cohere enterprise contract. Many open-weight-adopters miss this and assume the model is Apache-2.0-equivalent. It is notSource: Cohere model documentation · 2025-12
- Command A's multilingual strength sometimes surprises Western teams -- quality in languages like Korean, Arabic, Hindi is genuinely better than Llama or Mistral at comparable sizes. Worth testing specifically if multilingual is a requirement rather than trusting general benchmarksSource: Enterprise customer reports, Cohere docs · 2025-11
Best for
Mid-size to large enterprises needing a multilingual open-weight model with low-ish infrastructure requirements (2x H100 for full model). Especially good for retrieval-augmented generation over internal document stores, multi-language customer support, and workflows touching Asian / Middle Eastern / African languages where Command A's coverage materially beats Llama or Mistral. Also a strong pick for teams already in Cohere's enterprise ecosystem.
Not for
Teams that require strict Apache-2.0 / MIT licensing for commercial self-hosting (go with Llama, Qwen, GLM, Granite, Arcee Trinity, gpt-oss instead). Also not the right pick for consumer chat or for peak-benchmark chasing -- Command A optimizes for enterprise fit, not leaderboard position.
Our Verdict
Cohere Command A is the most credible enterprise-multilingual open-weight option in 2026. The 23-language coverage is genuinely best-in-class at the 111B size, the 2x H100 deployment bar is realistic for serious mid-size enterprises, and the Reasoning + Vision siblings simplify procurement. The major caveat is CC-BY-NC 4.0 licensing -- commercial self-hosting requires a Cohere enterprise contract, not just a weights download. For research and evaluation, it's free. For production revenue, you're in a Cohere contract either way, which is fine if you were heading there anyway and a dealbreaker if you strictly need Apache-2.0 freedom.
Sources
- Cohere: Command A model documentation (accessed 2026-04-17)
- VentureBeat: Cohere Command A launches (accessed 2026-04-17)
Alternatives to Cohere Command A
Llama 4 (Meta)
Meta's open-weights flagship family -- Scout (10M context), Maverick (multimodal 400B MoE), Behemoth in preview
Mistral AI
European AI lab with open and commercial models -- Mistral Small 4 (Mar 2026, 119B MoE Apache 2.0 unified model), Medium 3 (Apr 9 2026), and Voxtral TTS (open-source speech, Mar 2026)
DeepSeek
Near-frontier reasoning for pennies on the dollar -- the open-source LLM that made Silicon Valley nervous
Gemma 4 (Google)
Google DeepMind's open-weights model family -- multimodal, 256K context, runs on edge devices
Qwen (Alibaba)
Alibaba's open-weights + API family -- Qwen 3.6-Plus (Mar 30 2026, 1M context + always-on CoT + agentic tool-use), Qwen3.5 Small (2B runs on iPhone, 9B matches 120B-class models), plus Qwen3.5-Omni native multimodal. Apache 2.0 on the open sizes
GLM / Z.ai (Zhipu AI)
Zhipu AI's open-weights family -- GLM-5.1 (launched 2026-04-07) is 744B MoE / 40B active, topped SWE-Bench Pro at 58.4 (beating GPT-5.4 and Claude Opus 4.6), MIT licensed, 200K context. Trained entirely on 100K Huawei Ascend 910B chips -- first frontier model with zero Nvidia in the training stack
Kimi K2.5 (Moonshot)
Moonshot's 1T-parameter MoE open-weights flagship -- best open-source agentic coder, rivals Claude Opus 4.5
Nemotron (Nvidia)
Nvidia's open-weights family -- hybrid Mamba-Transformer MoE architecture, optimized for efficient reasoning on Nvidia hardware
MiniMax M2 / M2.5
MiniMax's open-weights frontier -- first open model to match Claude Opus 4.6 on SWE-Bench at 10-20× lower cost
Falcon (TII)
UAE's Technology Innovation Institute open-weights family -- Falcon 3 optimized for efficient sub-10B deployment on consumer hardware
gpt-oss (OpenAI)
OpenAI's FIRST open-weight models -- gpt-oss-120b (single 80GB GPU, near parity with o4-mini on reasoning) and gpt-oss-20b (runs on 16GB edge devices). Apache 2.0. Launched 2025-08-05. gpt-oss-safeguard ships in 2026 as the safety-tuned variant
IBM Granite 4.0
IBM's enterprise-focused open-weight family -- Granite 4.0 hybrid Mamba-2 + transformer architecture (70-80% memory reduction vs pure transformer), 3B to 32B sizes, Apache 2.0. First open model family to secure ISO 42001 certification. Nano 350M runs on CPU with 8-16GB RAM. 3B Vision variant landed 2026-04-01
Arcee Trinity-Large-Thinking
Arcee AI's US-made open-weight frontier reasoning model -- launched 2026-04-01. 398B total params, ~13B active. Sparse MoE (256 experts, 4 active = 1.56% routing). Apache 2.0, trained from scratch. #2 on PinchBench trailing only Claude 3.5 Opus. ~96% cheaper than Opus-4.6 on agentic tasks
Olmo 3 (AI2)
Allen Institute for AI's fully-open frontier reasoning models -- Olmo 3 family (2025-11-20) includes 7B and 32B sizes, four variants (Base, Think, Instruct, RLZero). Apache 2.0 with fully open data + checkpoints + training logs. Olmo 3-Think 32B matches Qwen3-32B-Thinking at 6x fewer training tokens
AI21 Jamba2
AI21 Labs' hybrid SSM-Transformer (Mamba-style) open-weight family -- Jamba2 launched 2026-01-08. Two sizes: 3B dense (runs on phones / laptops) and Jamba2 Mini MoE (12B active / 52B total). Apache 2.0, 256K context, mid-trained on 500B tokens
StepFun Step 3.5 Flash
StepFun's (China) agent-focused open-weight model -- Step 3.5 Flash launched 2026-02-01. 196B sparse MoE, ~11B active. Benchmarks slightly ahead of DeepSeek V3.2 at over 3x smaller total size. Step 3 (321B / 38B active, Apache 2.0) and Step3-VL-10B multimodal also in the family