# StandardOne-3B by Standard Thinking: Open-Weight Model
Source: https://savrn.com/models/standardone-3b
Markdown alternate of the page above; the site index is https://savrn.com/llms.txt

---

## Runs On

What it takes to serve StandardOne-3B (3.8B parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
| --- | --- | --- | --- | --- | --- |
| 16-bit | 7.7 GB | 9.2 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 |
| 8-bit | 3.8 GB | 4.6 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 |
| 4-bit | 1.9 GB | 2.3 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 |

Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the [SAVRN Index](https://savrn.com/ai-index/pricing/gpus), read Oct 7, 2026.

[StandardOne-3B on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/standardone-3b/gpus)

## Model Card

By Standard Thinking, published under apache-2.0, revision 65f9140a1294.

Updated weights (v2, 2026-09-26). If you downloaded this model before, download it again or pin revision="v2". Earlier versions stay available under the tags v1 and v1.1.

Version: v2

Standard One scores a bounded set of answers for a supplied scenario and returns probabilities through POST /v1/systemone. It does not generate free-form response text. This repository contains the merged BF16 3B checkpoint; the server code is in [StandardOne-8B](https://savrn.com/models/standardone-8b).

| If you need | Repository |
| --- | --- |
| Merged 3B checkpoint | [StandardOne-3B](https://savrn.com/models/standardone-3b) (this repository) |
| 3B adapter weights and merge recipe | [StandardOne-3B-LoRA](https://savrn.com/models/standardone-3b-lora) |
| Larger merged checkpoint and server code | [StandardOne-8B](https://savrn.com/models/standardone-8b) |
| 8B adapter weights and merge recipe | [StandardOne-8B-LoRA](https://savrn.com/models/standardone-8b-lora) |

In the reported served evaluations, 3B has a lower median latency on the measured short-request profile; 8B scores higher on the public standard and hard tiers. See Benchmarks for the measurement conditions and limitations.

The figure combines results from different measurement paths. See Benchmarks for served versus offline conditions; measured 24–26 September 2026.

### At a glance

[Read the full model card (2,029 words)](https://savrn.com/models/standardone-3b/card)

## Configuration

Architecture

Mistral3ForConditionalGeneration

Context length (tokens)

262,144

Layers

26

Hidden size

3,072

Feed-forward size

9,216

Attention heads

32

Key/value heads

8

Head dimension

128

Vocabulary size

131,072

Model type

mistral3

## Identity and Version

Repository

StandardThinking/StandardOne-3B

Publisher

Standard Thinking

Task

Text generation

Modality

Text

Library

transformers

Parameters

3.8B parameters

Languages

en, ja, zh, es, fr, de, pt, ru

Revision

65f9140a1294c430495b306e98e2e618a21167b1

First published

2026-09-24

Last updated

2026-09-27

## Files and Weights

92 files, 7.7 GB in total. The weights are 2 files totalling 7.7 GB in safetensors.

Weights2 files · 7.7 GB

Configuration48 files · 17.3 MB

Tokenizer4 files · 17.3 MB

Documentation19 files · 139.7 KB

Other17 files · 2.0 MB

Repository2 files · 2.0 KB

Every file

| File | Type | Size | SHA-256 |
| --- | --- | --- | --- |
| model-00001-of-00002.safetensors | Weights | 5.0 GB | 7029a2415131 |
| model-00002-of-00002.safetensors | Weights | 2.7 GB | ad184b56624e |
| MERGE_REPORT.json | Configuration | 2.5 KB | — |
| config.json | Configuration | 1.7 KB | — |
| evidence/calibration-leaderboard-ablate-nosys.json | Configuration | 21.4 KB | — |
| evidence/calibration-leaderboard-merged-native.json | Configuration | 11.0 KB | — |
| evidence/calibration-per-type-served.json | Configuration | 14.2 KB | — |
| evidence/prompt-wording-choice.json | Configuration | 2.3 KB | — |
| evidence/served-nosys-easy-report.json | Configuration | 6.9 KB | — |
| evidence/served-nosys-hard-report.json | Configuration | 14.9 KB | — |
| evidence/served-nosys-latency-idle-gpu.json | Configuration | 43.6 KB | — |
| evidence/served-nosys-original-report.json | Configuration | 9.8 KB | — |
| generation_config.json | Configuration | 131 B | — |
| model.safetensors.index.json | Configuration | 45.6 KB | — |
| params.json | Configuration | 1.1 KB | — |
| processor_config.json | Configuration | 976 B | — |
| release-manifest.json | Configuration | 1.1 KB | — |
| server/benchmarks/data/jevbench-easy/public.manifest.json | Configuration | 3.3 KB | — |
| server/benchmarks/data/jevbench-hard/public.manifest.json | Configuration | 3.7 KB | — |
| server/benchmarks/data/jevbench-original/public.manifest.json | Configuration | 3.4 KB | — |
| server/benchmarks/run_matrix.py | Configuration | 9.4 KB | — |
| server/benchmarks/verify_metrics.py | Configuration | 5.1 KB | — |
| server/examples/request.json | Configuration | 542 B | — |
| server/examples/smoke.py | Configuration | 1.9 KB | — |
| server/jev_adapter/__init__.py | Configuration | 73 B | — |
| server/jev_adapter/__main__.py | Configuration | 7.5 KB | — |
| server/jev_adapter/backend.py | Configuration | 892 B | — |
| server/jev_adapter/benchmarks/__init__.py | Configuration | 81 B | — |
| server/jev_adapter/benchmarks/compare.py | Configuration | 2.8 KB | — |
| server/jev_adapter/benchmarks/data.py | Configuration | 8.5 KB | — |
| server/jev_adapter/benchmarks/jevbench.py | Configuration | 12.1 KB | — |
| server/jev_adapter/benchmarks/metrics.py | Configuration | 12.1 KB | — |
| server/jev_adapter/benchmarks/prepare.py | Configuration | 8.3 KB | — |
| server/jev_adapter/benchmarks/run.py | Configuration | 18.4 KB | — |
| server/jev_adapter/protocol.py | Configuration | 15.6 KB | — |
| server/jev_adapter/server.py | Configuration | 4.4 KB | — |
| server/jev_adapter/service.py | Configuration | 7.0 KB | — |
| server/jev_adapter/sglang.py | Configuration | 19.5 KB | — |
| server/tests/test_benchmark_data.py | Configuration | 8.7 KB | — |
| server/tests/test_benchmark_matrix.py | Configuration | 3.4 KB | — |
| server/tests/test_benchmark_run.py | Configuration | 10.0 KB | — |
| server/tests/test_disconnect_cleanup.py | Configuration | 5.1 KB | — |
| server/tests/test_http_integration.py | Configuration | 2.0 KB | — |
| server/tests/test_jevbench.py | Configuration | 11.0 KB | — |
| server/tests/test_main.py | Configuration | 11.5 KB | — |
| server/tests/test_protocol.py | Configuration | 17.8 KB | — |
| server/tests/test_service.py | Configuration | 17.1 KB | — |
| server/tests/test_sglang.py | Configuration | 16.0 KB | — |
| special_tokens_map.json | Configuration | 147.1 KB | — |
| tekken.json | Configuration | 16.8 MB | 600bb2794656 |
| LICENSE | Documentation | 11.3 KB | — |
| NOTICE | Documentation | 1.2 KB | — |
| QUICKSTART.md | Documentation | 4.5 KB | — |
| README.md | Documentation | 17.1 KB | — |
| docs/BENCHMARKS.md | Documentation | 26.0 KB | — |
| docs/public-classification-suites.md | Documentation | 3.3 KB | — |
| server/LICENSE | Documentation | 11.3 KB | — |
| server/NOTICE | Documentation | 931 B | — |
| server/README.md | Documentation | 17.5 KB | — |
| server/benchmarks/DECISION_BENCHMARK_SELECTION.md | Documentation | 5.2 KB | — |
| server/benchmarks/METRIC_VALIDATION.md | Documentation | 3.8 KB | — |
| server/benchmarks/PUBLIC_DATASETS.md | Documentation | 13.4 KB | — |
| server/benchmarks/README.md | Documentation | 12.8 KB | — |
| server/benchmarks/data/jevbench-easy/LICENSE | Documentation | 1.1 KB | — |
| server/benchmarks/data/jevbench-easy/THIRD-PARTY.md | Documentation | 2.7 KB | — |
| server/benchmarks/data/jevbench-hard/LICENSE | Documentation | 1.1 KB | — |
| server/benchmarks/data/jevbench-hard/THIRD-PARTY.md | Documentation | 2.7 KB | — |
| server/benchmarks/data/jevbench-original/LICENSE | Documentation | 1.1 KB | — |
| server/benchmarks/data/jevbench-original/THIRD-PARTY.md | Documentation | 2.7 KB | — |
| SHA256SUMS | Other | 8.8 KB | — |
| SYSTEM_PROMPT.txt | Other | 2.4 KB | — |
| chat_template.jinja | Other | 11.9 KB | — |
| docs/assets/00-benchmark-card.png | Other | 298.1 KB | 1395bca8ffbf |
| docs/assets/00-benchmark-card.svg | Other | 58.7 KB | — |
| docs/assets/02-latency-vs-qwen.png | Other | 114.5 KB | 7313f2b0645e |
| docs/assets/02-latency-vs-qwen.svg | Other | 11.3 KB | — |
| docs/assets/04-throughput.png | Other | 97.7 KB | — |
| docs/assets/04-throughput.svg | Other | 8.7 KB | — |
| docs/assets/05-gain-over-base.png | Other | 179.7 KB | 4900de357045 |
| docs/assets/05-gain-over-base.svg | Other | 16.6 KB | — |
| docs/assets/06-vs-jev.png | Other | 266.4 KB | 0274d259fe3e |
| docs/assets/06-vs-jev.svg | Other | 23.0 KB | — |
| server/benchmarks/data/jevbench-easy/public.jsonl | Other | 60.7 KB | — |
| server/benchmarks/data/jevbench-hard/public.jsonl | Other | 708.2 KB | — |
| server/benchmarks/data/jevbench-original/public.jsonl | Other | 91.9 KB | — |
| server/pyproject.toml | Other | 808 B | — |
| .gitattributes | Repository | 1.9 KB | — |
| server/.gitignore | Repository | 125 B | — |
| server/jev_adapter/native_tokenizer.py | Tokenizer | 7.5 KB | — |
| server/tests/test_native_tokenizer.py | Tokenizer | 14.1 KB | — |
| tokenizer.json | Tokenizer | 17.1 MB | d5f6046775b1 |
| tokenizer_config.json | Tokenizer | 198.1 KB | — |

## License and Download

License

apache-2.0

Access

Open weights, no gate

Download size

7.7 GB

[Download from Standard Thinking](https://huggingface.co/StandardThinking/StandardOne-3B)

Released by Standard Thinking through its official repository on Hugging Face. [Read the license](https://www.apache.org/licenses/LICENSE-2.0).

## Built From

- Derived from mistralai/Ministral-3-3B-Instruct-2512-BF16

## Memory Requirements

| Precision | Weights in memory |
| --- | --- |
| As published | 7.7 GB |
| 16-bit | 7.7 GB |
| 8-bit | 3.8 GB |
| 4-bit | 1.9 GB |

Weights only, from the published parameter count; the key-value cache and runtime add to this.

## Built on This Model

- Quantized from[StandardOne-3B-GGUF](https://savrn.com/models/standardone-3b-gguf)
- Derived from[StandardOne-3B-GGUF](https://savrn.com/models/standardone-3b-gguf)
- Quantized from[StandardOne-3B-FP8](https://savrn.com/models/standardone-3b-fp8)
- Derived from[StandardOne-3B-FP8](https://savrn.com/models/standardone-3b-fp8)

## Questions About StandardOne-3B

### How much GPU memory does StandardOne-3B need?

About 9.2 GB at 16-bit and 2.3 GB at 4-bit: the weights (3.8B parameters) plus a working margin. A long context needs more.

### What is the cheapest GPU to run StandardOne-3B on?

At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

### Can I use StandardOne-3B commercially?

Yes. StandardOne-3B is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.

### What is StandardOne-3B's context length?

262,144 tokens, from the maximum position embeddings in its published configuration.

## Similar Models

Model · Text generation

### [StandardOne-3B-FP8](https://savrn.com/models/standardone-3b-fp8)

[Standard Thinking](https://savrn.com/model-publishers/standardthinking)

StandardOne-3B-FP8 is an FP8 (compressed-tensors, float8e4m3 weights, dynamic per-token activations) quantization of the released StandardOne-3B decision model. The language-model linear projections (q/k/v/o, gate/up/down) are quantized per-channel FP8 E4M3 with dynamic FP8 activations (llm-compressor's data-free FP8DYNAMIC recipe, no calibration data required); the vision tower, multi-modal projector, embeddings and lmhead are left unquantized in BF16. It was produced from source revision 68dafd17ead9b8cf6f85c4f08f7f2f3a1e7b9e5c of StandardOne-3B on 2026-09-25 using llm-compressor 0.14.0 (torch 2.14.0, transformers 5.17.0, compressed-tensors 0.19.0); results below. Served through SGLang…

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Open weights other 3.9B parameters 32,768 tokens transformers

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### [NULLXES-SHINRA-4B-INSTRUCT](https://savrn.com/models/nullxes-shinra-4b-instruct)

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[Stan Chen](https://savrn.com/model-publishers/stancsz)

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## Standard Thinking

[All models and datasets](https://savrn.com/model-publishers/standardthinking)

## Versions

- [65f9140a1294](https://savrn.com/models/standardone-3b/versions/65f9140a1294) · current 2026-09-27
- [68dafd17ead9](https://savrn.com/models/standardone-3b/versions/68dafd17ead9) 2026-09-25

## Explore More

- [All text generation models](https://savrn.com/models/tasks/text-generation)
- [All models under apache-2.0](https://savrn.com/models/licenses/apache-2-0)
- [Model comparisons](https://savrn.com/models/comparisons)
- [The model directory](https://savrn.com/models)
- [Open model prices by host](https://savrn.com/ai-index/pricing/open-models)

## Source

- Repository metadata, read 2026-09-27.
- [Hugging Face record](https://huggingface.co/StandardThinking/StandardOne-3B)
- [How the hub is built](https://savrn.com/model-hub/methodology)