Command A+ suits enterprises wanting self-hosted or sovereign AI: it runs on modest GPU hardware at Cohere's recommended quantization and ships under a fully open license with no per-token lock-in. It replaces four separate Cohere models but trails today's frontier closed models on raw reasoning benchmarks.
Cohere Command A+ is an open-weights sparse mixture-of-experts language model that unifies Cohere's separate reasoning, vision, and translation models into one checkpoint. Artificial Analysis measured it at 76% on GPQA Diamond and scored it 37 on its Intelligence Index; the model is released under a fully permissive Apache license for self-hosted or Cohere-managed deployment.
Where it sits
- --$ per 1M tokensBlended price (3:1)Lower is better-- / 64peer median $1.70/Mvendor price, checked by HokAI
- --tokens/sOutput speedHigher is better-- / 39peer median 90 tok/scited: Artificial Analysis
- --% solvedSWE-bench VerifiedHigher is better-- / 28peer median 78.3%per source, see benchmark scores
- 76%% correctGPQA DiamondHigher is better#30 / 44peer median 88.3%per source, see benchmark scores
In the bottom third on GPQA Diamond (rank 30 of 44), and one of 65 whose vendor states it does not train on customer data.
Ranks are against GA models on HokAI that publish the same figure; ties share a rank.
Provider: Cohere · Family: Command A
Context window: 128,000 tokens · Max output: 64,000
Input modalities: text, image, tool-calls · Output: text, tool-calls
About Cohere Command A+
Cohere Command A+ is a sparse mixture-of-experts foundation model released by Cohere on May 20, 2026 under the model ID command-a-plus-05-2026. It has 218 billion total parameters with 25 billion active per token across 128 experts (8 active per token), architected to run on far less hardware than its total parameter count suggests. Command A+ unifies capabilities that were previously split across four separate Cohere models, Command A, Command A Reasoning, Command A Vision, and Command A Translate, into a single scalable model, making it Cohere's first model to combine multimodal reasoning and machine translation in one checkpoint. It sits at the top of Cohere's Command lineup, above Command R+ and the smaller Command R7B.
On benchmarks, Command A+ posts an Artificial Analysis Intelligence Index score of 37, a meaningful step up from earlier Command generations. Cohere's own released comparisons versus its predecessor Command A Reasoning show large agentic gains: tau-squared-Bench Telecom jumped from 37% to 85%, and Terminal-Bench Hard (agentic coding) rose from 3% to 25%. Internal enterprise evals show a 20% improvement on agentic question answering and a 32% improvement on spreadsheet analysis tasks. On multimodal benchmarks, Command A+ scores 75.1% on MMMU (essentially matching Command A Vision's 75.3%) and improves MathVista from 73.5% to 80.6%. Independent tracker vals.ai lists a composite Vals Index accuracy of 24.69% for the model, measured differently from Cohere's internal comparisons.
Command A+ supports a 128,000-token input context window with a 64,000-token maximum generation length, unchanged in input size from Command A Reasoning but paired with meaningfully faster inference. Cohere reports output throughput up to 63% higher and time-to-first-token reduced 17% versus Command A Reasoning at full precision, with the W4A4 4-bit quantization adding a further 47% speed boost and 13% latency reduction, plus an additional 1.5-1.6x speedup available through speculative decoding.
The model accepts text, image, and tool-use input and produces text and tool-use output, with genuine multimodal reasoning (not just image captioning) via its unified architecture. Language support expanded significantly from Command A Reasoning, now covering all official EU languages, with tokenization efficiency gains of 20% for Arabic, 18% for Japanese, and 16% for Korean. Function calling and agentic tool use are core design targets, reflected in the large tau-squared-Bench and Terminal-Bench gains cited above.
Command A+ is released under a full Apache 2.0 license with no usage restrictions, and weights are downloadable from Hugging Face (CohereLabs/command-a-plus-05-2026) in three quantization formats: BF16 (16-bit, requiring 4x B200 or 8x H100 GPUs), FP8 (8-bit, requiring 2x B200 or 4x H100), and W4A4 (4-bit, Cohere's recommended default for most deployments). Cohere states benchmark quality differences across quantizations are negligible. Cohere has not published a standard per-token hosted API price for Command A+; the model is positioned primarily for self-hosted or Cohere Model Vault managed deployment rather than pay-as-you-go API billing.
Deployment options include self-hosting the open weights directly, or using Cohere's Model Vault managed service, which handles infrastructure while keeping data inside a customer's own environment, a deployment model Cohere markets specifically at regulated industries needing sovereign or air-gapped AI. Oracle Cloud Infrastructure's Generative AI service has added Command A Reasoning and Command A Vision as managed offerings; Command A+'s cloud marketplace availability beyond direct Hugging Face and Model Vault access was not confirmed in available sources as of mid-2026.
Command A+ carries Cohere's standard two-mode safety configuration: contextual mode for wide-ranging interactions with fewer output constraints while still rejecting clearly harmful or illegal content, and strict mode that avoids sensitive topics like violence, sexual content, and profanity entirely. Cohere has not published a dedicated third-party red-team partner list for Command A+ specifically, and detailed safety benchmark numbers were not found in public sources at time of research; Cohere directs safety documentation requests to labs@cohere.com.
Command A+ replaces four prior Command variants at once, so existing Command A Vision or Command A Translate users should expect a migration path onto the unified model going forward.
Pricing
Command A+ has no confirmed public per-token API price as of mid-2026; it is released open-weights under Apache licensing, priced for self-hosting or a Cohere Model Vault contract instead of metered API billing. Infrastructure is the real cost: 24-hour on-demand cloud rental runs about $55 for a single B200 GPU or $72 for two H100 GPUs at the recommended W4A4 quantization. For a hosted reference point, Cohere's older Command R+ API charges $2.50 per 1M input tokens and $10.00 per 1M output tokens.
Key Features
- Unified Reasoning, Vision, and Translation: Merges four previously distinct Cohere models, spanning reasoning, vision, and translation workloads, into one checkpoint that handles multimodal reasoning and machine translation together.
- Sparse Mixture-of-Experts Routing: Routes each token through a small subset of specialized experts, letting the model operate at frontier scale while running on a fraction of the compute a similarly sized dense model would need.
- Expanded Multilingual Coverage: Covers all official EU languages, with Cohere reporting measurable tokenization efficiency gains for Arabic, Japanese, and Korean text versus the prior Command A Reasoning model.
- 4-bit W4A4 Quantized Deployment: Cohere's recommended default deployment mode, which Cohere reports keeps benchmark quality loss negligible while trimming GPU requirements down to a single modest card.
- Fully Open License: Weight files are downloadable from Hugging Face with no usage restrictions, letting teams audit, fine-tune, or run the model fully air-gapped.
Pros
- Ships under a fully open license with weights on Hugging Face, so there is no per-token billing lock-in or dependency on Cohere's API uptime.
- Posts large agentic gains over its predecessor: Terminal-Bench Hard rose from 3% to 25% and tau-squared-Bench Telecom from 37% to 85%.
- Runs at Cohere's recommended W4A4 quantization on far more modest hardware than its total parameter count implies, unusually light for a model this size.
Cons
- The Artificial Analysis Intelligence Index of 37 trails frontier closed models such as GPT-5-series and Claude Opus on composite reasoning.
- There is no confirmed low-cost hosted per-token API; production access effectively requires self-hosting or a Cohere Model Vault contract.
- The 128,000-token context window trails several 2026 rivals now offering windows past 1 million tokens.
Benchmarks
- MMMU-Pro: 63% vendor-reported · 08 Sep 2026 — College-level questions that need reading images and diagrams, % correct.
- GPQA Diamond: 76% cited: Artificial Analysis · 08 Sep 2026 — PhD-level science questions that are hard to search for, % correct.
- AA Intelligence Index: 37 cited: Artificial Analysis · 08 Sep 2026 — Composite of 10 evaluations run by Artificial Analysis, 0 to 100.
A benchmark is an exam, not the job. Scores transfer unevenly between tasks, so weigh the one closest to your workload and read every figure with its source.
Frequently Asked Questions
What are Cohere Command A+'s pricing plans in 2026?
There is no published per-token price for Command A+ itself since Cohere ships it as open weights rather than a metered API. Expect to pay for compute instead: renting a single high-end GPU for a day runs roughly $55, and a two-GPU setup runs about $72, both at Cohere's recommended 4-bit quantization. Cohere's legacy Command R+ API, priced at $2.50 input and $10.00 output per 1M tokens, is the closest hosted comparison if a per-token number is what you need.
Is Cohere Command A+ free to use?
Yes: Command A+ is released under a permissive open-weights license with no usage restrictions, so the model itself costs nothing to download or run commercially. The only real cost is GPU infrastructure, since Cohere has not published a free hosted API tier for it the way it has for some smaller Command models.
What are the best alternatives to Cohere Command A+?
Mistral Large 3 and DeepSeek V4 are the closest open-weights alternatives if self-hosting matters: Mistral Large 3 trades some scale for a smaller footprint, while DeepSeek V4 leans further into agentic coding benchmarks. Hy4 Preview is worth a look for teams that want an even larger open MoE checkpoint with an Apache-licensed release.
Is Cohere Command A+ better than Mistral Large 3?
Command A+ and Mistral Large 3 are both fully open, self-hostable MoE models with no per-token API lock-in, but they target different jobs. Mistral Large 3 offers a wider 256K-token context window at a lower published API rate, while Command A+ adds native multimodal reasoning, machine translation, and support for far more languages in one checkpoint. Teams choosing between them should weigh long-document handling against unified multimodal and multilingual coverage.
How long does it take to get going with Cohere Command A+?
Getting started means downloading the weights from Hugging Face under CohereLabs/command-a-plus-05-2026 and picking a quantization: most teams start with the W4A4 4-bit build, which Cohere says keeps benchmark quality loss negligible on comparatively modest self-hosted hardware. Teams that would rather skip infrastructure entirely can request access to Cohere's managed Model Vault deployment instead.
Top Alternatives
- Mistral Large 3: Pick Mistral Large 3 for a wider context window and lower published API pricing; pick Command A+ for unified multimodal reasoning, translation, and broader language coverage in one checkpoint.
- DeepSeek V4: Pick DeepSeek V4 if agentic coding benchmark score is the deciding factor; pick Command A+ if you need native vision and translation alongside reasoning in a single model.
- Hy4 Preview: Pick Hy4 Preview for a larger open-weight checkpoint from Tencent; pick Command A+ for Cohere's enterprise-focused Model Vault deployment path and multilingual tooling.