Open Model WeightsTASK-FIRST MODEL SELECTION
Start with the task. Explore reviewed suitability, source-verified artifacts, licenses and explicit unknowns. No universal model score.
Start with a workload, select comparable checkpoint types, then inspect the weight artifacts, license, declared context, reproducible tests and realistic serving constraints. Latest checked date 2026-10-26; 765 of 765 field-verified records checked that day. Dates describe source checks, not independent inference benchmarks.
Our Fit Finder and hardware explorer add constraints, but weight-only estimates never guarantee GPU fit.
TASK PATH · 6 CURATED OPTIONS
Examine execution reliability, tool schemas and license limits before production.
Worth evaluating whenLong-context text and agent research where a verified 524,288-token configuration is relevant.
Check before choosingThe checkpoint remains a large serving target even though the publisher describes only a subset of parameters as active per token.
Worth evaluating whenHigh-complexity reasoning and coding agents with text inputs, tools and long action histories.
Check before choosingThe 560B weight tensors need substantial storage and distributed serving; 25B active does not mean a 25B-sized deployment.
Worth evaluating whenExperiments with tool-using coding agents that inspect repositories and iterate on failing tests.
Check before choosingThe 131,072-token configured context is not proof of equal accuracy or affordable memory use at its maximum length.
Worth evaluating whenNVIDIA identifies Gemma-4-E4B-it as its starting checkpoint and describes further RL post-training for structured outputs and tools.
Check before choosingAssess real improvements with matched chat template, tokenizer, permitted tools and decoding parameters against the original foundation.
Worth evaluating whenLarge open-model evaluation programs with sufficient serving resources and auditable benchmarks.
Check before choosingThe advertised single-device performance requires an explicit quantized variant and actual memory measurement.
Worth evaluating whenSelf-hosted coding assistants with sandboxed tools and test-driven evaluation.
Check before choosingPublisher benchmark numbers remain source claims, not Open Model Weights measurements.
Showing up to six alphabetically ordered, reviewed entries out of 6 matching the editorial task category. This is not a benchmark ranking; the displayed suitability and cautions come from each model’s individually reviewed guide. Weight-only BF16 arithmetic excludes KV cache, activations and runtime overhead. Format availability is not implied.
TASK PATH · 3 CURATED OPTIONS
Start with distribution, runtime and real hardware tests; size alone is not deployment proof.
Worth evaluating whenTyped classification and finite-option decision prototypes that need downloadable model artifacts.
Check before choosingReported decision benchmarks are first-party results, not an independent OMW ranking against general language models.
Worth evaluating whenDense compact-model baselines for coding, reasoning and long-context tasks.
Check before choosingPublisher/independent benchmarks do not establish that this model will win on a customer's task-specific evaluation.
Worth evaluating whenNVIDIA identifies Gemma-4-E4B-it as its starting checkpoint and describes further RL post-training for structured outputs and tools.
Check before choosingAssess real improvements with matched chat template, tokenizer, permitted tools and decoding parameters against the original foundation.
Showing up to six alphabetically ordered, reviewed entries out of 3 matching the editorial task category. This is not a benchmark ranking; the displayed suitability and cautions come from each model’s individually reviewed guide. Weight-only BF16 arithmetic excludes KV cache, activations and runtime overhead. Format availability is not implied.
TASK PATH · 2 CURATED OPTIONS
Separate transcription, synthesis, diarization and streaming requirements.
Worth evaluating whenMultilingual ASR deployments with consented voice data and reproducible quality monitoring.
Check before choosingDo not attribute speaker diarization from 4.1 2B Plus to this checkpoint.
Worth evaluating whenThe model is notable for the technical speech category and clear architecture signals, but licensing is a decisive gate. CC BY-NC 4.0 is recorded as non-commercial; a business should not deploy it commercially without separate rights.
Check before choosingThe repository uses a declared CC BY-NC-4.0 license but no separate LICENSE artifact was recorded in the checked snapshot. Do not conflate source metadata with a legal opinion.
Showing up to six alphabetically ordered, reviewed entries out of 2 matching the editorial task category. This is not a benchmark ranking; the displayed suitability and cautions come from each model’s individually reviewed guide. Weight-only BF16 arithmetic excludes KV cache, activations and runtime overhead. Format availability is not implied.
TASK PATH · 5 CURATED OPTIONS
Compare embedding, retrieval and research roles only within the same task.
Worth evaluating whenControlled research-summary pipelines that provide curated passages and require source-attributed claims.
Check before choosingNo standalone language model can verify links or retrieve current papers without explicitly integrated tools.
Worth evaluating whenThe publisher describes GLM-5.2-derived 744B-class MoE architecture, while Hugging Face reports tensor-derived total values from its files.
Check before choosingLogical parameters, activated compute and complete distributed BF16 stored weights must be separately reported to avoid incorrect memory guidance.
Worth evaluating whenOn-device or private multilingual semantic search across text and code.
Check before choosingA unified embedding space does not guarantee equal accuracy for all modalities, languages and query types.
Worth evaluating whenText-to-document and text-to-image retrieval prototypes that require fine-grained matching.
Check before choosing128 dimensions apply per token, not necessarily to a single fixed-size vector representing a whole document.
Worth evaluating whenHigh-fidelity indexing of text passages, scanned pages and image-rich document corpora.
Check before choosingThe released 8.39B stored-parameter count does not establish inference speed or GPU fit.
Showing up to six alphabetically ordered, reviewed entries out of 5 matching the editorial task category. This is not a benchmark ranking; the displayed suitability and cautions come from each model’s individually reviewed guide. Weight-only BF16 arithmetic excludes KV cache, activations and runtime overhead. Format availability is not implied.
TASK PATH · 9 CURATED OPTIONS
Check modality, image or video preprocessing, checkpoint files and output limits.
Worth evaluating whenIts strength as a category candidate lies in image transformation and controlled edits, a distinct problem from text-to-image generation. Comparisons with Qwen-Image should use matched input images, edit instructions and output constraints.
Check before choosingThe checked repository includes a custom LICENSE.md. Its commercial-use status remains unknown in OMW and should not be silently treated as Apache-2.0 or unrestricted use.
Worth evaluating whenThe official architecture uses 54 dual-stream diffusion blocks and a persistent KV cache to extend generated video segments.
Check before choosingPreserved contextual frames and synchronized camera trajectories matter more here than a language model's maximum token count.
Worth evaluating whenDocument ingestion and OCR evaluation before private RAG or document-question-answering workflows.
Check before choosingThis is primarily an OCR/vision-language preprocessing component, not a verified autonomous tool-using model.
Worth evaluating whenLTX-2.5 deserves evaluation for video-generation workflows with flexible conditioning. Its operational envelope should be compared by output frame count, resolution, audio coupling and video continuity, not against text-model context lengths.
Check before choosingThe recorded license ID is custom/other and commercial use remains unknown. Model-family shorthand does not establish rights for this exact checkpoint.
Worth evaluating whenFor video generation, testing should cover temporal continuity, audio alignment, output duration and motion fidelity. The model should not be ranked by text LLM benchmarks simply because it has billion-class parameters.
Check before choosingThe official license is custom/other. Verify commercial rights and content restrictions directly, especially when generated audio and video are used for customers.
Worth evaluating whenIt is an important image-generation category representative, but leaderboard positions depend on the particular benchmark, prompt distribution and version date. Distinguish this base checkpoint from the separate Qwen-Image-2.1-Turbo accelerated release.
Check before choosingThe repository reports a nonstandard/custom license. Commercial-use permission is unknown in the OMW record until the actual license text and use case have been reviewed.
Showing up to six alphabetically ordered, reviewed entries out of 9 matching the editorial task category. This is not a benchmark ranking; the displayed suitability and cautions come from each model’s individually reviewed guide. Weight-only BF16 arithmetic excludes KV cache, activations and runtime overhead. Format availability is not implied.
TASK PATH · 16 CURATED OPTIONS
Distinguish total from active parameters, weight formats and actual context evaluation.
Worth evaluating whenThe important question is whether this exact Flash checkpoint can meet a target task at an acceptable batch size and cost. Compare source evidence, prompt format and input support before relying on anecdotal throughput or generic DeepSeek family scores.
Check before choosingThe repository has an MIT license file, but deployment claims from a model card are still publisher-supplied. A checked model API is not a substitute for a repeatable inference test.
Worth evaluating whenFor 262K-context image-text use, compare its actual processor support and throughput with Qwen3.8-27B on the same tasks. The two checkpoints may differ substantially in prompting, architecture and preprocessing.
Check before choosingApache-2.0 appears in official repository metadata; the checked record did not find a separate LICENSE file. Keep those separate when using license data for procurement.
Worth evaluating whenIts attraction is the combination of a large open-weight checkpoint and a documented long-context configuration. The primary engineering decision is whether a team can economically serve the chosen precision and whether it needs the full text-generation stack rather than a compact alternative.
Check before choosingThe recorded license ID is 'other' and the repository provides a separate LICENSE file. Do not infer MIT/Apache rights or automatic commercial permission from the availability of Safetensors.
Worth evaluating whenThis checkpoint is an interesting trade-off between very large stored weights and lower per-token active compute. Teams should compare throughput under their own server configuration rather than assume the model name 'Flash' guarantees lower latency.
Check before choosingThe text-and-image pipeline metadata and MIT license are source observations, not a demonstration that every input modality and context size works in all serving engines.
Worth evaluating whenIt is a strong comparison candidate when a team wants a named, first-party Apache-2.0 open-weight model and a reproducible serving environment. Test exact accuracy and operational costs against smaller models instead of equating model size with task quality.
Check before choosingRuntime mentions such as Ollama and vLLM are documentation signals. They are not evidence that OMW executed this checkpoint or that a chosen quantized file will satisfy a particular context window.
Worth evaluating whenEvaluation of private tool-using agents where declared Apache-2.0 model terms are an explicit requirement.
Check before choosingThe first-party 128K native context and optional 512K extension are not interchangeable for deployment sizing.
Showing up to six alphabetically ordered, reviewed entries out of 16 matching the editorial task category. This is not a benchmark ranking; the displayed suitability and cautions come from each model’s individually reviewed guide. Weight-only BF16 arithmetic excludes KV cache, activations and runtime overhead. Format availability is not implied.