# Datasets, Page 99 of 132
Source: https://savrn.com/datasets/page/99
Markdown alternate of the page above; the site index is https://savrn.com/llms.txt

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SAVRN Model Hub

# AI Training Datasets

Each dataset with its card, its structure (every split, row count and column type), its files, its license, and the models that disclose training on it.

11,122Models

3,164Datasets

285Papers

6,792Publishers

11,852Sourced relationships

Updated 2026-10-18 · [How the library is built](https://savrn.com/model-hub/methodology)

## Filter Datasets

Task

- [Text generation127](https://savrn.com/datasets?task=Text+generation)
- [Robotics106](https://savrn.com/datasets?task=Robotics)
- [Other48](https://savrn.com/datasets?task=Other)
- [Text classification47](https://savrn.com/datasets?task=Text+classification)
- [Text retrieval44](https://savrn.com/datasets?task=Text+retrieval)
- [Question answering36](https://savrn.com/datasets?task=Question+answering)
- [Speech recognition33](https://savrn.com/datasets?task=Speech+recognition)
- [Tabular classification21](https://savrn.com/datasets?task=Tabular+classification)
- [Time series forecasting21](https://savrn.com/datasets?task=Time+series+forecasting)
- [Image to text17](https://savrn.com/datasets?task=Image+to+text)
- [Image classification16](https://savrn.com/datasets?task=Image+classification)
- [Object detection14](https://savrn.com/datasets?task=Object+detection)
- [Text to speech14](https://savrn.com/datasets?task=Text+to+speech)
- [Visual question answering14](https://savrn.com/datasets?task=Visual+question+answering)
- [Image to image11](https://savrn.com/datasets?task=Image+to+image)
- [Image segmentation9](https://savrn.com/datasets?task=Image+segmentation)
- [Image to 3d9](https://savrn.com/datasets?task=Image+to+3d)
- [Translation9](https://savrn.com/datasets?task=Translation)
- [Video classification9](https://savrn.com/datasets?task=Video+classification)
- [Audio classification7](https://savrn.com/datasets?task=Audio+classification)
- [Feature extraction7](https://savrn.com/datasets?task=Feature+extraction)
- [Reinforcement learning7](https://savrn.com/datasets?task=Reinforcement+learning)
- [Text to image7](https://savrn.com/datasets?task=Text+to+image)
- [Text to video7](https://savrn.com/datasets?task=Text+to+video)

License

- [other250](https://savrn.com/datasets?license=other)
- [apache-2.0236](https://savrn.com/datasets?license=apache-2.0)
- [mit232](https://savrn.com/datasets?license=mit)
- [cc-by-4.0224](https://savrn.com/datasets?license=cc-by-4.0)
- [cc-by-nc-4.077](https://savrn.com/datasets?license=cc-by-nc-4.0)
- [cc0-1.030](https://savrn.com/datasets?license=cc0-1.0)
- [cc-by-sa-4.029](https://savrn.com/datasets?license=cc-by-sa-4.0)
- [odc-by24](https://savrn.com/datasets?license=odc-by)
- [cc-by-nc-sa-4.020](https://savrn.com/datasets?license=cc-by-nc-sa-4.0)
- [unknown14](https://savrn.com/datasets?license=unknown)
- [cc-by-sa-3.06](https://savrn.com/datasets?license=cc-by-sa-3.0)
- [odbl6](https://savrn.com/datasets?license=odbl)
- [gpl-3.05](https://savrn.com/datasets?license=gpl-3.0)
- [cc-by-3.04](https://savrn.com/datasets?license=cc-by-3.0)
- [cc3](https://savrn.com/datasets?license=cc)
- [cc-by-nc-nd-4.03](https://savrn.com/datasets?license=cc-by-nc-nd-4.0)
- [gpl3](https://savrn.com/datasets?license=gpl)
- [cc-by-2.02](https://savrn.com/datasets?license=cc-by-2.0)
- [cc-by-nd-4.02](https://savrn.com/datasets?license=cc-by-nd-4.0)
- [cdla-sharing-1.02](https://savrn.com/datasets?license=cdla-sharing-1.0)
- [agpl-3.01](https://savrn.com/datasets?license=agpl-3.0)
- [bsl-1.01](https://savrn.com/datasets?license=bsl-1.0)
- [c-uda1](https://savrn.com/datasets?license=c-uda)
- [cc-by-nc-3.01](https://savrn.com/datasets?license=cc-by-nc-3.0)

Access

- [Publicly accessible2,933](https://savrn.com/datasets?access=open_access)
- [Access requested at publisher231](https://savrn.com/datasets?access=gated)

Publisher

- [Benjamin J Barber35](https://savrn.com/datasets?publisher=endomorphosis)
- [Chang Yu24](https://savrn.com/datasets?publisher=g1n0st)
- [Ji Xie23](https://savrn.com/datasets?publisher=sanaka87)
- [Justicedao18](https://savrn.com/datasets?publisher=justicedao)
- [LeDuy13](https://savrn.com/datasets?publisher=duyle2408)
- [Nguyen13](https://savrn.com/datasets?publisher=icdn11)
- [Nguyen13](https://savrn.com/datasets?publisher=icdn12)
- [Nguyen13](https://savrn.com/datasets?publisher=icdn13)
- [National Engineering Research Center for Big Data Software13](https://savrn.com/datasets?publisher=thulab)
- [Nguyen12](https://savrn.com/datasets?publisher=icdn10)
- [Nguyen12](https://savrn.com/datasets?publisher=icdn14)
- [Byte I12](https://savrn.com/datasets?publisher=icdn15)
- [Rebrowser.net12](https://savrn.com/datasets?publisher=rebrowser)
- [Shufan Jiang12](https://savrn.com/datasets?publisher=tsumugii)
- [Ai210](https://savrn.com/datasets?publisher=allenai)
- [Ps Yo10](https://savrn.com/datasets?publisher=atokforps)
- [Dougall DeepMind10](https://savrn.com/datasets?publisher=dougalldeepmind)
- [Deesced9](https://savrn.com/datasets?publisher=ameluce)
- [Jiahao Zhang9](https://savrn.com/datasets?publisher=hi-todayis-jh)
- [Julia Dollis9](https://savrn.com/datasets?publisher=juliadollis)
- [Massive Text Embedding Benchmark9](https://savrn.com/datasets?publisher=mteb)
- [Pranab Pattanaik9](https://savrn.com/datasets?publisher=p2samapa)
- [Sota9](https://savrn.com/datasets?publisher=sotaagi2030)
- [Sunsetcanyon9](https://savrn.com/datasets?publisher=sunsetcanyon)

Size

- [10K<n<100K125](https://savrn.com/datasets?size=10K%3Cn%3C100K)
- [1K<n<10K106](https://savrn.com/datasets?size=1K%3Cn%3C10K)
- [n<1K100](https://savrn.com/datasets?size=n%3C1K)
- [100K<n<1M70](https://savrn.com/datasets?size=100K%3Cn%3C1M)
- [1M<n<10M51](https://savrn.com/datasets?size=1M%3Cn%3C10M)
- [10M<n<100M33](https://savrn.com/datasets?size=10M%3Cn%3C100M)
- [100M<n<1B21](https://savrn.com/datasets?size=100M%3Cn%3C1B)
- [n>1T16](https://savrn.com/datasets?size=n%3E1T)
- [1B<n<10B8](https://savrn.com/datasets?size=1B%3Cn%3C10B)
- [100B<n<1T5](https://savrn.com/datasets?size=100B%3Cn%3C1T)
- [10B<n<100B2](https://savrn.com/datasets?size=10B%3Cn%3C100B)
- [10GB<n<100GB1](https://savrn.com/datasets?size=10GB%3Cn%3C100GB)
- [120,8981](https://savrn.com/datasets?size=120%2C898)
- [1B-tokens1](https://savrn.com/datasets?size=1B-tokens)
- [1T<n<10T1](https://savrn.com/datasets?size=1T%3Cn%3C10T)
- [n1K<n10K1](https://savrn.com/datasets?size=n1K%3Cn10K)
- [n<100K1](https://savrn.com/datasets?size=n%3C100K)
- [n<10K1](https://savrn.com/datasets?size=n%3C10K)
- [n<1GB1](https://savrn.com/datasets?size=n%3C1GB)
- [n>1M1](https://savrn.com/datasets?size=n%3E1M)

Domain

- [LeRobot31](https://savrn.com/datasets?domain=LeRobot)
- [legal, law, graphrag12](https://savrn.com/datasets?domain=legal%2C+law%2C+graphrag)
- [tsfile, timeseries, time-series12](https://savrn.com/datasets?domain=tsfile%2C+timeseries%2C+time-series)
- [open-data, data-science, web-scraping9](https://savrn.com/datasets?domain=open-data%2C+data-science%2C+web-scraping)
- [seo-cluster, web-nodes9](https://savrn.com/datasets?domain=seo-cluster%2C+web-nodes)
- [dpo, rlhf, ultrafeedback8](https://savrn.com/datasets?domain=dpo%2C+rlhf%2C+ultrafeedback)
- [mteb, text, image7](https://savrn.com/datasets?domain=mteb%2C+text%2C+image)
- [nepali, instruction-tuning, agriculture7](https://savrn.com/datasets?domain=nepali%2C+instruction-tuning%2C+agriculture)
- [science, stackexchange, provenance5](https://savrn.com/datasets?domain=science%2C+stackexchange%2C+provenance)
- [LeRobot, robotics, lerobot4](https://savrn.com/datasets?domain=LeRobot%2C+robotics%2C+lerobot)
- [ml-intern4](https://savrn.com/datasets?domain=ml-intern)
- [AMASS, Retarget, Robotics3](https://savrn.com/datasets?domain=AMASS%2C+Retarget%2C+Robotics)
- [benchmark, agents, terminal3](https://savrn.com/datasets?domain=benchmark%2C+agents%2C+terminal)
- [climate3](https://savrn.com/datasets?domain=climate)
- [code3](https://savrn.com/datasets?domain=code)
- [crypto, cryptocurrency, finance3](https://savrn.com/datasets?domain=crypto%2C+cryptocurrency%2C+finance)
- [data-attribution, influence-functions, interpretability3](https://savrn.com/datasets?domain=data-attribution%2C+influence-functions%2C+interpretability)
- [fireviewer, fire, smoke3](https://savrn.com/datasets?domain=fireviewer%2C+fire%2C+smoke)
- [video3](https://savrn.com/datasets?domain=video)
- [LeRobot, lerobot, grabette2](https://savrn.com/datasets?domain=LeRobot%2C+lerobot%2C+grabette)
- [LeRobot, ur7e, robotiq2](https://savrn.com/datasets?domain=LeRobot%2C+ur7e%2C+robotiq)
- [agentenv, rl-environment, agents2](https://savrn.com/datasets?domain=agentenv%2C+rl-environment%2C+agents)
- [ai-governance, measurement2](https://savrn.com/datasets?domain=ai-governance%2C+measurement)
- [autonomous, agents, evolution2](https://savrn.com/datasets?domain=autonomous%2C+agents%2C+evolution)

[Most downloaded](https://savrn.com/datasets?sort=downloads)[Most liked](https://savrn.com/datasets?sort=likes)[Recently updated](https://savrn.com/datasets?sort=updated)[Name](https://savrn.com/datasets?sort=name)

3,164 datasets, sorted by most downloaded.

Dataset · Image segmentation

### [GTCrack](https://savrn.com/datasets/gtcrack)

[Guoxi Liu](https://savrn.com/model-publishers/hf-gx)

Automated detection of expressway pavement cracks is critically important for infrastructure inspection and maintenance. In this task, range information provides a key geometric cue for distinguishing genuine cracks from pavement textures, shadows, stains, and other visual interference. However, industrial-grade pavement data acquisition equipment is expensive, and pixel-wise annotation is labor intensive. Consequently, most existing pavement crack datasets contain only visual imagery and cannot precisely represent the geometric morphology of the pavement surface. GTCrack was developed to alleviate this data bottleneck. A survey vehicle equipped with two laser line profilers was used to…

Publicly accessible

[View dataset](https://savrn.com/datasets/gtcrack)

Dataset

### [GWM-Zero](https://savrn.com/datasets/gwm-zero)

[Sanxun7](https://savrn.com/model-publishers/sanxun7)

Publicly accessible

[View dataset](https://savrn.com/datasets/gwm-zero)

Dataset

### [GameCrafter-Parkour](https://savrn.com/datasets/gamecrafter-parkour)

[White Tea](https://savrn.com/model-publishers/teawhite)

First-person rooftop parkour captured in Unreal Engine 5.8, made to train a whitebox → RGB video-to-video world model: the model takes a cheap, engine-free whitebox render of the scene as its control video and acts as the renderer. The whitebox is not rendered by Unreal. The courses are procedurally generated by a small Python package (pk, included under code/pk) that only uses boxes, cylinders and capsules. The same package renders the whitebox control frames with a lightweight OpenGL renderer, both for this dataset and at inference time. That lets you generate new worlds and control videos in milliseconds per frame without a game engine. Unreal is only used here to produce the paired RGB…

Publicly accessible 100K<n<1M

[View dataset](https://savrn.com/datasets/gamecrafter-parkour)

Dataset · Image to image

### [GenLit_ICLight_Relighting](https://savrn.com/datasets/genlit-iclight-relighting)

[World Snapshot](https://savrn.com/model-publishers/world-snapshot)

Raw (pre-VAE) samples for the WSM v1 IC-Light relighting pilot (iclightrelightingv3). Each sample is a self-describing named-key.npz: a source appearance, an IBL light-probe condition, and the relit target. The matching FLUX.2 latents live in the companion latent repo World-Snapshot/WSMv1LatentPTWSGv1.8forFlux2 (custompilots/iclightrelightingv3/cachenamedv2/). Derived from the public GenLit dataset (gbmpi/genlitdataset): Objaverse objects rendered under Poly Haven HDRI environments with a single moving point light. Within a GenLit scene the point-light direction is fixed while its intensity ramps 0 → 30 → 60 → 90 → 100 → 120 and then plateaus at 120 (as the point light rises the environment…

Publicly accessible mit

[View dataset](https://savrn.com/datasets/genlit-iclight-relighting)

Dataset · Text to video

### [GroOT-MOT17](https://savrn.com/datasets/groot-mot17)

[Mohamed Bassat](https://savrn.com/model-publishers/bassatbassat)

A text-to-tracklet retrieval benchmark for MOT17 pedestrians. Each captioned MOT17 track is cropped into its own short mp4. A text query must retrieve the matching track from a gallery of 454 tracks. The layout is the same as bassatbassat/TVPReid, so the same evaluation code runs on both. This is an unofficial derivative. The videos come from MOT17 and the captions come from GroOT. The credit for both belongs to their authors (see below). What this repository adds is listed under Our contribution. All configs share the same 454-track gallery. Only the queries differ. Tracks without a caption of the requested type stay in the gallery as distractors. Read the action numbers with care. GroOT…

Publicly accessible cc-by-nc-sa-3.0

[View dataset](https://savrn.com/datasets/groot-mot17)

Dataset

### [HEC3R-Sim-KalibEval](https://savrn.com/datasets/hec3r-sim-kalibeval)

[SHEC3R](https://savrn.com/model-publishers/shec3r)

Publicly accessible

[View dataset](https://savrn.com/datasets/hec3r-sim-kalibeval)

Dataset

### [HF001](https://savrn.com/datasets/hf001)

[GMES](https://savrn.com/model-publishers/sstore)

Publicly accessible

[View dataset](https://savrn.com/datasets/hf001)

Dataset

### [HWuhan](https://savrn.com/datasets/hwuhan)

[LanJie](https://savrn.com/model-publishers/lanjie001)

Publicly accessible

[View dataset](https://savrn.com/datasets/hwuhan)

Dataset · Text generation

### [HanIFEval](https://savrn.com/datasets/hanifeval)

[Yonghye Kwon](https://savrn.com/model-publishers/developer0hye)

HanIFEval is a Korean translation of 429 items of google/IFEval (revision 966cd89). Every Korean prompt is translated together with the arguments of the rule-based checker that scores it, and the result is verified by code. as the v1 revision tag. Wherever the checker scores a constraint, the Korean prompt states the - the same string (keyword, forbidden word, end phrase, repeat text); - the same number; - the same relation ("N 이상", "N 미만", and "N개 이상" wherever the checker scores ≥ N). It does not mean that every natural reading of a prompt is scored. - Keyword rules match surface substrings. '청사진' contains '사진', and a spacing variant escapes a forbidden compound. - Some constraints are…

Publicly accessible apache-2.0 n<1K

[View dataset](https://savrn.com/datasets/hanifeval)

Dataset

### [HarborWatch-TideBriefs](https://savrn.com/datasets/harborwatch-tidebriefs)

[Sota](https://savrn.com/model-publishers/sotaagi2030)

This companion release provides short plain-language tide summaries for the cleaned station observations. The published summary field was generated directly with mistralai/Mistral-7B-Instruct-v0.3. The model card states that Mistral-7B-Instruct-v0.3 is released under the Apache License 2.0. No other model produced the summary field.

Publicly accessible

[View dataset](https://savrn.com/datasets/harborwatch-tidebriefs)

Dataset

### [HarborWatch-TideLogs-Raw](https://savrn.com/datasets/harborwatch-tidelogs-raw)

[Sota](https://savrn.com/model-publishers/sotaagi2030)

This archive contains cleaned hourly shoreline observations imported directly from the Coastal Signals tidal-stations-open collection. Station identifiers and observation timestamps are retained to support traceable coastal analysis.

Publicly accessible

[View dataset](https://savrn.com/datasets/harborwatch-tidelogs-raw)

Dataset

### [Hecate-Omni](https://savrn.com/datasets/hecate-omni)

[James](https://savrn.com/model-publishers/harry-1234)

1. We develop and release Hecate-Omni, a model for multimodal social intelligence reasoning, designed to handle diverse social behavior tasks spanning emotion recognition, mental-state analysis, sentiment understanding, and pathological behavior recognition. Hecate-Omni achieves SOTA on the evaluated benchmarks, demonstrating effective and robust reasoning across different social behavior scenarios. 2. We introduce Hecate-PO. Hecate-PO adaptively identifies latent reasoning units using predictive entropy and establishes correspondences among equivalent reasoning states through internal state representations, enabling reasoning decisions to be evaluated under semantically comparable…

Publicly accessible mit

[View dataset](https://savrn.com/datasets/hecate-omni)

Dataset

### [Hindi-Sentiment-30K](https://savrn.com/datasets/hindi-sentiment-30k)

[Satyapal Singh](https://savrn.com/model-publishers/satyapal2240)

Publicly accessible

[View dataset](https://savrn.com/datasets/hindi-sentiment-30k)

Dataset

### [Hypa-Keyboard-English](https://savrn.com/datasets/hypa-keyboard-english)

[Hypa-Intelligence](https://savrn.com/model-publishers/hypaai)

Publicly accessible

[View dataset](https://savrn.com/datasets/hypa-keyboard-english)

Dataset

### [ICLR2027-duplexsafety-exp](https://savrn.com/datasets/iclr2027-duplexsafety-exp)

[Haolong Zheng](https://savrn.com/model-publishers/magicluke)

Raw outputs of full-duplex spoken-dialogue safety experiments: simulated conversations (audio and transcripts), judge verdicts and system traces. Research use only; access on request. Layout: studies/ /{REPORT.md, manifest.json, tables/, shared/, runs/, audio/}.

Access requested at publisher cc-by-nc-4.0

[View dataset](https://savrn.com/datasets/iclr2027-duplexsafety-exp)

Dataset

### [IEEE2026_BigData_MAS-4-Science-Matching](https://savrn.com/datasets/ieee2026-bigdata-mas-4-science-matching)

[AgentsSci — Agents for Science Research Group](https://savrn.com/model-publishers/agentssci)

Publicly accessible

[View dataset](https://savrn.com/datasets/ieee2026-bigdata-mas-4-science-matching)

Dataset · Speech recognition

### [IELTS-Dataset-WAV-and-Trasnscription-P3](https://savrn.com/datasets/ielts-dataset-wav-and-trasnscription-p3)

[Mohammed Aman](https://savrn.com/model-publishers/amanafif554)

IELTS Speaking test recordings from Amanafif554/IELTS-WAV-FILE-P3 (revision 8c68bdf8), each with an automatic Examiner/Candidate transcription and the band score stated in the video title. data/.wav + data/metadata.jsonl – the dataset itself transcriptions/.txt – the transcription of each WAV transcriptions/.json – per-file details: duration, sample rate, channels, size, sha256, band score and how it was found, title, YouTube URL, word counts, candidate word share, speaker check, ASR settings and time-stamped segments with speaker labels WhisperX with whisper-large-v3 (English, float16, a prompt containing fillers so that "um"/"uh" are kept), wav2vec2 word alignment and pyannote speaker…

Publicly accessible n<1K

[View dataset](https://savrn.com/datasets/ielts-dataset-wav-and-trasnscription-p3)

Dataset

### [IPEC-COMMUNITY_FastUMI_100k_lerobot_pantheon_annotated](https://savrn.com/datasets/ipec-community-fastumi-100k-lerobot-pantheon-annotated)

[Sambhav Gupta](https://savrn.com/model-publishers/sambhav-pantheon)

This dataset is Pantheon's derived flags and annotations for the FastUMI 100k dataset. See./{version}/README.md (currently v1) for each version's annotation schema. You will need to merge the annotations with the original dataset. QC flags and processed actions may contain errors, false positives, and false negatives, and are provided as-is. Pantheon is not affiliated with the source dataset.

Publicly accessible apache-2.0

[View dataset](https://savrn.com/datasets/ipec-community-fastumi-100k-lerobot-pantheon-annotated)

Dataset

### [Image-Caption](https://savrn.com/datasets/image-caption)

[Duy](https://savrn.com/model-publishers/tran1312)

Publicly accessible

[View dataset](https://savrn.com/datasets/image-caption)

Dataset

### [Instruct-Multilingual-v1-fix](https://savrn.com/datasets/instruct-multilingual-v1-fix)

[Bilal Cckfdn](https://savrn.com/model-publishers/bcckfdn)

Publicly accessible

[View dataset](https://savrn.com/datasets/instruct-multilingual-v1-fix)

Dataset · Video text to text

### [InteractionBench](https://savrn.com/datasets/interactionbench)

[InteractionBench](https://savrn.com/model-publishers/interactionbench)

InteractionBench: A Real-Time Interaction Benchmark for Streaming Video Systems A real-time interaction system watches a video stream and decides, at every moment, whether to speak or to stay silent. Each item of InteractionBench pairs a video with a question or a standing instruction and with timed ground-truth responses: what should be said, and the earliest moment at which it should be said. Items that require silence for the whole stream are included. This repository contains the videos, the questions and the ground truth. The evaluation code, the protocols and the baseline runners are in the is the collection directory of the video. An item is identified by #, for example…

Publicly accessible 1K<n<10K

[View dataset](https://savrn.com/datasets/interactionbench)

Dataset

### [JIMTOF_1](https://savrn.com/datasets/jimtof-1)

[Rainbow Robotics](https://savrn.com/model-publishers/rainbowrobotics)

Publicly accessible

[View dataset](https://savrn.com/datasets/jimtof-1)

Dataset

### [Kamsa-K1-Dataset](https://savrn.com/datasets/kamsa-k1-dataset)

[Haijun AI](https://savrn.com/model-publishers/haijun-ai)

Publicly accessible

[View dataset](https://savrn.com/datasets/kamsa-k1-dataset)

Dataset

### [Kestrel-Arch-Online](https://savrn.com/datasets/kestrel-arch-online)

[Eadxm](https://savrn.com/model-publishers/eadxm)

Publicly accessible

[View dataset](https://savrn.com/datasets/kestrel-arch-online)

## Model Collections

Hand-picked starting points, each with the reason it exists.

Collection · 4 entries

### [Embedding models for retrieval](https://savrn.com/collections/embedding-models-for-retrieval)

Sentence and document embedding models used to build retrieval systems. Dimension and sequence length matter more than size here, and both come from the publisher.

[View collection](https://savrn.com/collections/embedding-models-for-retrieval)

Collection · 4 entries

### [Models that fit on one accelerator](https://savrn.com/collections/models-that-run-on-one-accelerator)

Models whose publisher-reported parameter count puts them within reach of a single accelerator at common precisions. Memory needed depends on precision and serving configuration, so treat the parameter count as the starting point, not the answer.

[View collection](https://savrn.com/collections/models-that-run-on-one-accelerator)

Collection · 6 entries

### [Open-weight text models worth knowing](https://savrn.com/collections/open-weight-text-models-worth-knowing)

Widely used open-weight language models, chosen because each one is a distinct family rather than a variant of the one above it. Selection, not a ranking.

[View collection](https://savrn.com/collections/open-weight-text-models-worth-knowing)

Collection · 3 entries

### [Speech and audio models](https://savrn.com/collections/speech-and-audio)

Recognition and synthesis models, grouped so the two directions are easy to compare.

[View collection](https://savrn.com/collections/speech-and-audio)

## Related SAVRN Research

The hub sits beside SAVRN's market data and infrastructure research: what models cost to run, and what it takes to run them.

SAVRN Index

### [What open models cost to run](https://savrn.com/ai-index/pricing/open-models)

The same open-weight model priced by every host that serves it, per million tokens.

Research Hub

### [Data center trackers and maps](https://savrn.com/data-center-research)

Moratoriums, permits, power, water and capital behind the facilities that run these models.

Method

### [How the Model Hub is built](https://savrn.com/model-hub/methodology)

Sources, evidence labels, refresh behaviour, and the limits of every comparison here.
