Models › Nanbeige4.1 family

Nanbeige Apache 2.0 out 8 months ago

3.93B parameters, text in, text out, 256K tokens of context. 1 provider serves it.

Verified 2026-10-25 Rank #658 in LLMs · score 73 method v1 Sources 3 Downloads 30d 3.7k · #561 of 619 tracked

Changed this week: 1 new quantized build on the Hub (62 in all).2026-10-24

Can I run it?2.4GB at INT4Estimatefits a 16 GB cardmethod
Can I use it?Apache 2.0Permissive; patent grantlicence file2026-10-24
Do people use it?3.7kdownloads, 30 days#561 of 619 we track · below the median1,188 likesHugging Face2026-10-24

Worth reading and watching

Picked by rule from 1 tracked piece: the lab's own post, the most shared, a field report, the latest.

Where to run it

Live hosted endpoints, cheapest first, USD per million tokens. Ties break on output price, then provider name.

#Cheapest live rowIn /1MOut /1MContextQuantSeen
1 Featherless — — — — Oct 25

All 1 provider →

Where it's used

Hugging Face does not say who downloads a model or with what. These are the proxies: API providers listing it, quantized builds, fine-tunes/adapters/merges, and Spaces that load it.

Served by 1 API provider; heavily run locally (62 quantized builds).

  • As a service1Featherless
  • On own hardware62quantized builds
  • Built on3828 fine-tunes · 8 adapters · 2 merges
  • In apps28Hugging Face Spaces

Top Spaces running it: victor/nanbeige ♥ 35 · GenAISecurityProject/OWASP-AIBOM-Generator ♥ 30 · yaris664/Nanbeige4.1-3B-space ♥ 1

Will it fit?

Weights only, an estimate: leave room for the KV cache. Method

Fits a 16 GB card at INT4

About 2.4 GB at INT4, 4.7 GB at FP8, 9.4 GB at BF16: weights only, so leave room for the KV cache.

16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 2.4 GB of weights (estimate; KV cache and activations extra)INT42.4 GBFP8: about 4.7 GB of weights (estimate; KV cache and activations extra)FP84.7 GBBF16: about 9.4 GB of weights (estimate; KV cache and activations extra)BF169.4 GB
16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 2.4 GB of weights (estimate; KV cache and activations extra)INT42.4 GBFP8: about 4.7 GB of weights (estimate; KV cache and activations extra)FP84.7 GBBF16: about 9.4 GB of weights (estimate; KV cache and activations extra)BF169.4 GB

Estimate params × bytes per param × 1.2 · how we estimate

The story so far

Weights, every dated article, and repo commits on one rail.

1 dated article and 22 repo commits so far

Teal is the weights (2026-02-10), amber triangles are 1 dated article (each links to it), grey ticks are repo commits.

MarAprMayJunJulAugSepOct2026-03-25: 1 repo commit2026-02-26: 2 repo commits2026-02-22: 1 repo commit2026-02-19: 1 repo commit2026-02-17: 1 repo commit2026-02-13: 2 repo commits2026-02-11: 10 repo commits2026-02-10: 4 repo commitsMy Nanbeige4.1 3B chat room can now generate micro applications [video] · 2026-02-14Weights 2026-02-10 · 2026-02-10Weights 2026-02-10now · 1 provider
MarAprMayJunJulAugSepOct2026-03-25: 1 repo commit2026-02-26: 2 repo commits2026-02-22: 1 repo commit2026-02-19: 1 repo commit2026-02-17: 1 repo commit2026-02-13: 2 repo commits2026-02-11: 10 repo commits2026-02-10: 4 repo commitsMy Nanbeige4.1 3B chat room can now generate micro applications [video] · 2026-02-14Weights 2026-02-10 · 2026-02-10Weights 2026-02-10now · 1 provider

What people built on it

100 derivatives on the Hub. Which build to run, by runtime: the most downloaded in each class.

100 derivatives on the Hub, most of them quantized

From the Hugging Face model tree; quantized builds in teal, fine-tunes, merges and adapters in greys.

62 quantized (Hugging Face model tree)28 fine-tunes (Hugging Face model tree)2 merges (Hugging Face model tree)8 adapters (Hugging Face model tree)62 quantized28 fine-tunes2 merges8 adapters
62 quantized (Hugging Face model tree)28 fine-tunes (Hugging Face model tree)2 merges (Hugging Face model tree)8 adapters (Hugging Face model tree)62 quantized28 fine-tunes2 merges8 adapters

All variants →

Reference

Everything else, folded: the full tables, every source.

Benchmarks

19 rows

Benchmarks 19 rows

measured third-party leaderboard rowauthor-reported number from the model card or eval files (19)No confidence intervals unless the source gave them; settings differ between rows, so compare within a benchmark, not across. Date = the benchmark's own date when it gives one, else when we read it.
BenchmarkValueSetting / notesReported byDate
author-reported AIME 2026 · Reason through hard problems87.4—model author2026-10-07
author-reported Arena-Hard · Chat and follow instructions73.2—model author2026-10-07
author-reported BFCL · Call tools and APIs56.5—model author2026-10-07
author-reported Browse-Comp19.12with Toolsmodel author2026-10-07
author-reported Browse-Comp-ZH31.83with Toolsmodel author2026-10-07
author-reported GAIA (Text-only)69.9with Toolsmodel author2026-10-07
author-reported GPQA Diamond · Reason through hard problems83.8—model author2026-10-07
author-reported HMMT · Reason through hard problems77.92—model author2026-10-07
author-reported Humanity's Last Exam · Reason through hard problems12.6—model author2026-10-07
author-reported Humanity's Last Exam · Reason through hard problems22.29with Toolsmodel author2026-10-07
author-reported IMO-AnswerBench · Reason through hard problems53.38—model author2026-10-07
author-reported LiveCodeBench · Write code with agents76.9—model author2026-10-07
author-reported LiveCodeBench · Write code with agents81.4—model author2026-10-07
author-reported LiveCodeBench · Write code with agents28.1—model author2026-10-07
author-reported MultiChallenge · Chat and follow instructions52.21—model author2026-10-07
author-reported SEAL-041.44with Toolsmodel author2026-10-07
author-reported tau-bench · Call tools and APIs48.57—model author2026-10-07
author-reported xBench-DeepSearch-250575with Toolsmodel author2026-10-07
author-reported xBench-DeepSearch-251039with Toolsmodel author2026-10-07

Use-case evidence

6 use cases

What it is good at 6 use cases

Labels are evidence, not verdicts: measured a third-party leaderboard row for this model; author-reported a number the model author published in the card or eval files; structural a deterministic signal from the name, config or chat template, or a decision-model estimate. Each chip links to its source. All use cases.

Facts

22 fields

Listed in LLMs

Facts

Pipeline tagtext-generation
Librarytransformers
ArchitecturesLlamaForCausalLM
Model typellama
Dtypebfloat16
Params by dtypeBF16: 3,933,637,120
Size bandM
MoEno
Context length262,144 (config.json max_position_embeddings)
Languagesen, zh
PapersarXiv:2602.13367
LicenseApache 2.0 (apache-2.0)
Hub snapshot2026-10-25

Servability

vLLM · SGLang

Servability

vLLM: supported · LlamaForCausalLM · 7050ef881ff0835f6890a47db1859a24c65481d6SGLang: supported · LlamaForCausalLM · 773cb938fe9484a8f7e7e46fb1b1fe96412c59ed

vLLM registry (7050ef881ff0835f6890a47db1859a24c65481d6) · SGLang registry (773cb938fe9484a8f7e7e46fb1b1fe96412c59ed) · Matched by architecture class name against each project's registry on main; 'supported' means the architecture is registered, not that this checkpoint was tested.

Hardware fit

3 estimates

Hardware fit ESTIMATE

Weights-only memory for Nanbeige4.1-3B: params × bytes/param × 1.2 overhead. KV cache and activations are extra. How this is computed.

BF16 (2 bytes/param)9.4 GB · Single GPU (<24 GB)
FP8 (1 byte/param)4.7 GB · Single GPU (<24 GB)
INT4 (0.5 bytes/param)2.4 GB · Edge (<4 GB)

Weights only (params × bytes/param × 1.2 overhead); KV cache and activations extra.

All variants

40 variants

Lineage

Fine-tuned from Nanbeige/Nanbeige4-3B-Base (same organization) Family: Nanbeige/Nanbeige4.1 100 derivatives on the Hub

Base model: Nanbeige/Nanbeige4-3B-Base

quantized: 62finetune: 28adapter: 8merge: 2

Top variants (40 shown, ranked by downloads)

Relation
Quant
Size
40 rows
Variant Relation Quant Params Band License Author Likes Downloads
Edge-Quant/Nanbeige4.1-3B-Q4_K_M-GGUF quantized GGUF — — apache-2.0 Edge-Quant 40 130.4k
Edge-Quant/Nanbeige4.1-3B-Q8_0-GGUF quantized Q8_0 — — apache-2.0 Edge-Quant 6 123.5k
DevQuasar/Nanbeige.Nanbeige4.1-3B-GGUF quantized GGUF — — — DevQuasar 6 94.1k
cyankiwi/Nanbeige4.1-3B-AWQ-8bit quantized AWQ 3.93B M apache-2.0 cyankiwi 2 49.9k
mradermacher/Nanbeige4.1-3B-GGUF quantized GGUF — — apache-2.0 mradermacher 38 35.5k
tantk/Nanbeige4.1-3B-GGUF quantized GGUF — — apache-2.0 tantk 5 14.8k
mradermacher/Nanbeige4.1-3B-i1-GGUF quantized GGUF — — apache-2.0 mradermacher 6 12.7k
RoroMachine14/nanbeige-4.1-3b-deepseek-distilled quantized GGUF 3.93B M apache-2.0 RoroMachine14 4 9.7k
kaitchup/Nanbeige4.1-3B-autoround-W4A16 quantized -W4A16 1.40B S apache-2.0 kaitchup 1 8k
mlx-community/Nanbeige4.1-3B-8bit quantized -8bit 3.93B M apache-2.0 mlx-community 15 7k
heretic-org/Nanbeige4.1-3B-heretic finetune — 3.93B M apache-2.0 heretic-org 52 4.7k
Open4bits/Nanbeige4.1-3B-GGUF quantized GGUF — — apache-2.0 Open4bits 0 6.7k
deltakitsune/Nanbeige-4.1-Python-DeepThink-3B adapter GGUF 3.93B M apache-2.0 deltakitsune 8 5.4k
seanbailey518/Nanbeige4.1-3B-GGUF quantized GGUF — — other seanbailey518 0 5.1k
prithivMLmods/Nanbeige4.1-3B-f32-GGUF quantized GGUF — — llama3 prithivMLmods 4 4.7k
onnx-community/Nanbeige4.1-3B-ONNX quantized — — — apache-2.0 onnx-community 4 2.1k
Akicou/Nanbeige4.1-3B-GGUF quantized GGUF — — — Akicou 0 2.3k
cyankiwi/Nanbeige4.1-3B-AWQ-4bit quantized AWQ 3.93B M apache-2.0 cyankiwi 1 2.2k
SimplySara/Nanbeige4.1-3B-GGUF quantized GGUF — — apache-2.0 SimplySara 1 2.1k
SimplySara/Nanbeige4.1-3B-i1-GGUF quantized GGUF — — apache-2.0 SimplySara 1 2k
mlx-community/Nanbeige4.1-3B-bf16 finetune -bf16 3.93B M apache-2.0 mlx-community 3 1.5k
megabytes/Nanbeige4.1-3B-heretic finetune — 3.93B M apache-2.0 megabytes 17 661
andrevp/Nanbeige4.1-3B-MLX-4bit quantized MLX 3.93B M apache-2.0 andrevp 4 1.2k
McG-221/Nanbeige4.1-3B-mlx-8Bit quantized mlx 3.93B M apache-2.0 McG-221 0 1.4k
ThalisAI/Nanbeige4.1-3B-heretic quantized GGUF — — other ThalisAI 0 1.3k
DhruvalLabs/Nanbeige4.1-3B-GGUF quantized GGUF — — apache-2.0 DhruvalLabs 0 1.1k
NikolayKozloff/Nanbeige4.1-3B-Q8_0-GGUF quantized Q8_0 — — apache-2.0 NikolayKozloff 1 917
kaitchup/Nanbeige4.1-3B-FP8-Dynamic quantized -FP8 3.93B M apache-2.0 kaitchup 0 917
litert-community/Nanbeige4.1-3B quantized — — — apache-2.0 litert-community 2 786
mlx-community/Nanbeige4.1-3B-4bit quantized -4bit 3.93B M apache-2.0 mlx-community 1 815
Edge-Quant/Nanbeige4.1-3B-Q6_K-GGUF quantized Q6_K — — apache-2.0 Edge-Quant 2 691
NeshVerse/Uncensored_Nanbeige-4.1-3B finetune — 3.93B M apache-2.0 NeshVerse 3 457
TurkishCodeMan/Nanbeige4.1-3B-Gmail-Tool-Use finetune — 3.93B M apache-2.0 TurkishCodeMan 2 475
emee-ai/Nanbeige4.1-3B-bf16 finetune -bf16 3.93B M apache-2.0 emee-ai 0 553
alexlink/Nanbeige4.1-3B-GGUF quantized GGUF — — — alexlink 0 544
SkyAsl/Nanbeige4.1-VLM finetune — 3.94B M apache-2.0 SkyAsl 1 460
liodon-ai/Nanbeige4.1-3B-imatrix-GGUF quantized imatrix — — other liodon-ai 0 499
RepublicOfKorokke/Nanbeige4.1-3B-mlx-mxfp4 quantized mlx 3.93B M apache-2.0 RepublicOfKorokke 1 426
amoughnieh/Nanbeige4.1-3B-EE-Reasoning-SFT adapter GGUF — — mit amoughnieh 1 418
NeshVerse/Ultra_Flash_Financial_SFT_Nanbeige_4.1-3B merge — 4.03B M apache-2.0 NeshVerse 4 233

Repository changelog

22 commits
changed in the last 7 daysOther: 2026-10-24all changes

Repository changelog 22 commits

DateCommitSHA
2026-03-25 Update tokenizer_config.json 9c4555c
2026-02-26 Add syntax highlight in python markdown code snippets (#34) 6f3b2c3
2026-02-26 increase the max_new_tokens in the demo (#33) 971753c
2026-02-22 Update README.md 2d78edc
2026-02-19 Update README.md 7e64fe9
2026-02-17 Update README.md 988060c
2026-02-13 Update README.md b32903a
2026-02-13 Upload Nanbeige4.1-3B-Report.pdf 57b5051
2026-02-11 Update config.json 0133423
2026-02-11 Upload model_performance_comparison.png 5fa91af
2026-02-11 Delete figures/model_performance_comparison.png b7eedac
2026-02-11 Update README.md da13918
2026-02-11 Upload model_performance_comparison.png 28f4b2e
2026-02-11 Update README.md d02c34c
2026-02-11 Delete figures/1 8109f12
2026-02-11 Upload nbg.png 9c1da7f
2026-02-11 Create figures/1 810318d
2026-02-11 Upload tokenizer_config_search.json 253dcb4
2026-02-10 Upload 2 files 665db78
2026-02-10 Upload 2 files 1f158ff
2026-02-10 Upload 6 files 346a63d
2026-02-10 initial commit 7d222b4

All providers

1 provider · 1 endpoint

Where to run 1 providers · 1 endpoints

Hosted endpoints observed for Nanbeige4.1-3B, sorted by input price (USD per million tokens). Each row names its observer and date; prices change daily, so check the source before quoting.

ProviderModel refContextQuant$/M in$/M outtok/sTTFTToolsStatusObserved
Featherless
hf
Nanbeige/Nanbeige4.1-3B — — — — — — — live 2026-10-25

Articles and coverage

1 piece

Ranked by source signals (HN, Lobsters, authority domains, official posts) and recency.

Analysis1

youtube.com2026-02-14HN 1 pts · 0 comments

Papers

1 paper

Papers 1

arXiv links found in the model card and its tags. "Peer-reviewed" is shown only with evidence: a published venue in OpenAlex (measured) or the authors' own journal reference on arXiv (reported).

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Sources

3 sources

Sources

Hub snapshot: 2026-10-25 · HF API: 2026-10-24 21:42 UTC · Providers: 2026-10-24 21:44 UTC · Articles: 2026-10-24 21:46 UTC · Summary: 2026-10-07 15:14 UTC · Last verified 2026-10-25

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