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    Models/Captioning/CohereLabs/command-a-plus-05-2026-bf16
    HFScene CaptioningApache-2.0

    command-a-plus-05-2026-bf16

    by CohereLabs

    218B MoE multimodal model with native citation generation for document understanding

    145Kdl/month
    218B total / 25B activeparams
    Identifiers
    Model ID
    CohereLabs/command-a-plus-05-2026-bf16
    Feature URI
    mixpeek://image_extractor@v1/cohere_command_a_plus_v1

    Overview

    Command A+ is Cohere's flagship open-weight multimodal model with 218B total parameters and 25B active per token. It processes scanned documents, charts, and technical manuals while generating structured output with native citations back to source material.

    The model handles 128K context across 48 languages, making it particularly valuable for enterprise document understanding where provenance and citations are critical. The w4a4 quantized variant runs on a single B200 or two H100s.

    Architecture

    Mixture-of-Experts transformer with 218B total parameters, 25B active per token. Multimodal encoder handles interleaved text and image inputs. Native citation and grounding head produces source references alongside generated text. 128K context window.

    Mixpeek SDK Integration

    from mixpeek import Mixpeek
    mx = Mixpeek(api_key="YOUR_KEY")
    mx.ingest(
    collection_id="legal-docs",
    source="s3://contracts/",
    extractors=[
    {
    "type": "scene_caption",
    "model": "CohereLabs/command-a-plus-05-2026-bf16",
    "output_feature": "caption"
    },
    {
    "type": "text_embedding",
    "model": "BAAI/bge-m3",
    "input_field": "caption",
    "output_feature": "caption_embedding"
    }
    ]
    )

    Capabilities

    • Native citation generation with source grounding
    • 128K context, 48 languages
    • Document understanding (scans, charts, manuals)
    • MoE efficiency: 25B active / 218B total
    • Apache 2.0 license

    Use Cases on Mixpeek

    Cited document extraction where provenance matters
    Multilingual document understanding pipelines
    Chart and technical manual analysis with structured output

    Benchmarks

    DatasetMetricScoreSource
    MathVistaAccuracy80.6%Cohere, May 2026
    CharXiv ReasoningAccuracy52.7%Cohere, May 2026

    Performance

    Input SizeUp to 128K tokens (text + images)
    GPU Latency~350ms / image (H100, 25B active)
    GPU Throughput~22 images/sec (H100, batch 4)
    GPU Memory~48 GB (w4a4 quantized)

    Specification

    FrameworkHF
    OrganizationCohereLabs
    FeatureScene Captioning
    Outputtext
    Modalitiesvideo, image
    RetrieverSemantic Search
    Parameters218B total / 25B active
    LicenseApache-2.0
    Downloads/mo145K

    Research Paper

    Introducing Command A+

    arxiv.org

    Build a pipeline with command-a-plus-05-2026-bf16

    Add this model to a processing pipeline alongside other extractors. Combine with retrieval stages for end-to-end search.

    Open Studio