Worth reading Picked by rule from 23 tracked pieces: the lab's own post, the most shared, a field report, the latest.
Why it matters Piece Source
Most discussed · 490 HN pts
OpenAI released gpt-oss-120b and gpt-oss-20b, their first open-weight models since GPT-2 in 2019.
A 2025 research paper confirms Dropout results in worse downstream performance in single-epoch regimes. magazine.sebastianraschka.com 2025-08-10 490 HN pts · 97 comments 5.2k words about the Qwen3 family family
Field report
artificialanalysis.ai
Field report
artificialanalysis.ai
Latest · 8 months ago
venturebeat.com 2026-02-28 461 HN pts · 272 comments about the Qwen3 family family
Where to run it Live hosted endpoints, cheapest first, USD per million tokens. Ties break on output price, then provider name.
# Cheapest live row In /1M Out /1M Context Quant Seen 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 (435 quantized builds); a big base for builders (2,135 fine-tunes, adapters and merges).
As a service 1 Featherless On own hardware 435 quantized builds Built on 2,135 1,329 fine-tunes · 800 adapters · 6 merges In apps 100+ Hugging Face Spaces Top Spaces running it: Qwen/Qwen3-Demo ♥ 868 · pliny-the-prompter/obliteratus ♥ 621 · embedl/hfviewer ♥ 181 · tomg-group-umd/lm-watermarking ♥ 100
Will it fit? Weights only, an estimate: leave room for the KV cache. Method
Fits a 16 GB card at INT4 About 1.2 GB at INT4, 2.4 GB at FP8, 4.9 GB at BF16: weights only, so leave room for the KV cache.
16 GB card 64 GB Mac 2×80 GB 8×80 GB INT4: about 1.2 GB of weights (estimate; KV cache and activations extra) INT4 1.2 GB FP8: about 2.4 GB of weights (estimate; KV cache and activations extra) FP8 2.4 GB BF16: about 4.9 GB of weights (estimate; KV cache and activations extra) BF16 4.9 GB
16 GB card 64 GB Mac 2×80 GB 8×80 GB INT4: about 1.2 GB of weights (estimate; KV cache and activations extra) INT4 1.2 GB FP8: about 2.4 GB of weights (estimate; KV cache and activations extra) FP8 2.4 GB BF16: about 4.9 GB of weights (estimate; KV cache and activations extra) BF16 4.9 GB
Estimate params × bytes per param × 1.2 · how we estimate
How it scores Third-party measurements only; author-reported numbers stay in Reference , marked as such.
#21 of 28 on BFCL so far 28.41. Each tick is a model on this site with a measured BFCL row; author-reported numbers stay in Reference. Percent (overall accuracy). FC
zai-org/GLM-4.6: 72.38 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html moonshotai/Kimi-K2-Instruct: 59.06 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html deepseek-ai/DeepSeek-V3.2-Exp: 54.12 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html Qwen/Qwen3-235B-A22B-Instruct-2507: 52.15 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html Qwen/Qwen3-32B: 48.71 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html Qwen/Qwen3-235B-A22B-Instruct-2507: 47.99 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html Qwen/Qwen3-32B: 46.78 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html Salesforce/Llama-xLAM-2-8b-fc-r: 46.68 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html Qwen/Qwen3-8B: 42.57 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html Qwen/Qwen3-30B-A3B-Instruct-2507: 41.39 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html Qwen/Qwen3-14B: 41.03 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html Qwen/Qwen3-8B: 40.43 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html Qwen/Qwen3-14B: 37.77 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html Qwen/Qwen3-30B-A3B-Instruct-2507: 36.7 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html Qwen/Qwen3-4B-Instruct-2507: 35.68 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html Qwen/Qwen3-4B-Instruct-2507: 35.52 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html meta-llama/Llama-3.3-70B-Instruct: 31.9 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html google/gemma-3-12b-it: 30.43 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html google/gemma-3-27b-it: 29.47 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html microsoft/phi-4: 28.79 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html meta-llama/Llama-4-Scout-17B-16E-Instruct: 28.13 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html meta-llama/Llama-3.1-8B-Instruct: 25.83 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html Qwen/Qwen3-0.6B: 23.93 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html Qwen/Qwen3-0.6B: 22.38 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html meta-llama/Llama-3.2-3B-Instruct: 21.95 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html google/gemma-3-4b-it: 19.62 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html google/gemma-3-1b-it: 7.17 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html 0 100 28.41 · #21 of 28
zai-org/GLM-4.6: 72.38 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html moonshotai/Kimi-K2-Instruct: 59.06 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html deepseek-ai/DeepSeek-V3.2-Exp: 54.12 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html Qwen/Qwen3-235B-A22B-Instruct-2507: 52.15 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html Qwen/Qwen3-32B: 48.71 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html Qwen/Qwen3-235B-A22B-Instruct-2507: 47.99 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html Qwen/Qwen3-32B: 46.78 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html Salesforce/Llama-xLAM-2-8b-fc-r: 46.68 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html Qwen/Qwen3-8B: 42.57 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html Qwen/Qwen3-30B-A3B-Instruct-2507: 41.39 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html Qwen/Qwen3-14B: 41.03 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html Qwen/Qwen3-8B: 40.43 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html Qwen/Qwen3-14B: 37.77 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html Qwen/Qwen3-30B-A3B-Instruct-2507: 36.7 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html Qwen/Qwen3-4B-Instruct-2507: 35.68 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html Qwen/Qwen3-4B-Instruct-2507: 35.52 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html meta-llama/Llama-3.3-70B-Instruct: 31.9 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html google/gemma-3-12b-it: 30.43 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html google/gemma-3-27b-it: 29.47 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html microsoft/phi-4: 28.79 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html meta-llama/Llama-4-Scout-17B-16E-Instruct: 28.13 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html meta-llama/Llama-3.1-8B-Instruct: 25.83 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html Qwen/Qwen3-0.6B: 23.93 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html Qwen/Qwen3-0.6B: 22.38 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html meta-llama/Llama-3.2-3B-Instruct: 21.95 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html google/gemma-3-4b-it: 19.62 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html google/gemma-3-1b-it: 7.17 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html 0 100 28.41 · #21 of 28
Source · leaderboard · 2026-10-25
The story so far Weights, every dated article, and repo commits on one rail.
4 dated articles and 18 repo commits so far Teal is the weights (2025-04-27), amber triangles are 4 dated articles (each links to it), grey ticks are repo commits.
What people built on it 2,570 derivatives on the Hub. Which build to run, by runtime: the most downloaded in each class.
2,570 derivatives on the Hub, most of them fine-tunes From the Hugging Face model tree; quantized builds in teal, fine-tunes, merges and adapters in greys.
435 quantized (Hugging Face model tree) 1,329 fine-tunes (Hugging Face model tree) 6 merges (Hugging Face model tree) 800 adapters (Hugging Face model tree) 435 quantized1,329 fine-tunes6 merges800 adapters
435 quantized (Hugging Face model tree) 1,329 fine-tunes (Hugging Face model tree) 6 merges (Hugging Face model tree) 800 adapters (Hugging Face model tree) 435 quantized1,329 fine-tunes6 merges800 adapters
All variants →
Reference Everything else, folded: the full tables, every source.
Benchmarks 1 row Benchmarks 1 row measured third-party leaderboard row (1)author-reported number from the model card or eval filesNo 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.
Benchmark Value Setting / notes Reported by Date measured BFCL · Call tools and APIs 28.41 percent (overall accuracy) FC5 sub-scores Non-Live AST Acc : 82.92Live Acc : 74.61Multi Turn Acc : 11Web Search Acc : 2.5Memory Acc : 6.02 leaderboard 2026-10-25
What the card says 1 cited sentence Summary generated, cited The model architecture features 28 layers and a context length of 32,768.[1] [2]
“Number of Layers: 28” “Context Length: 32,768” Generated 2026-10-07 15:00 UTC by parasail-gemma-4-26b-a4b-it ; each numeral points at the fact it rests on. Not a verdict.
Use-case evidence 5 use cases What it is good at 5 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 21 fields Listed in LLMs
Facts Pipeline tag text-generation Library transformers Architectures Qwen3ForCausalLM Model type qwen3 Dtype bfloat16 Params by dtype BF16: 2,031,739,904 Size band M MoE no Context length 40,960 (config.json max_position_embeddings) Papers arXiv:2505.09388 License Apache 2.0 (apache-2.0) Hub snapshot 2026-10-25
Servability vLLM · SGLang Hardware fit 3 estimates Hardware fit ESTIMATE Weights-only memory for Qwen3-1.7B: params × bytes/param × 1.2 overhead. KV cache and activations are extra. How this is computed .
BF16 (2 bytes/param) 4.9 GB · Single GPU (<24 GB) FP8 (1 byte/param) 2.4 GB · Edge (<4 GB) INT4 (0.5 bytes/param) 1.2 GB · Edge (<4 GB)
Weights only (params × bytes/param × 1.2 overhead); KV cache and activations extra.
All variants 40 variants Lineage Base model: Qwen/Qwen3-1.7B-Base
quantized: 435 finetune: 1,329 merge: 6 adapter: 800
Top variants (40 shown, ranked by downloads) Repository changelog 18 commits Repository changelog 18 commits Date Commit SHA 2025-07-26 Create LICENSE 70d244c 2025-05-21 Update README.md 0060bc5 2025-05-19 update tokenizer_config.json 0ecc654 2025-04-29 Update README.md d3e2589 2025-04-28 Update README.md bf6c947 2025-04-28 Update README.md 44c8955 2025-04-28 Update README.md 33c2236 2025-04-28 Update README.md d725c74 2025-04-28 Delete special_tokens_map.json b9352fb 2025-04-28 Delete added_tokens.json 7794bc7 2025-04-28 Update README.md 9cbdf16 2025-04-28 Update README.md 026ef89 2025-04-28 Update README.md 0fa42c2 2025-04-28 Update README.md 2fe16ea 2025-04-28 Update README.md 442b4a6 2025-04-28 Update README.md 3f1b00a 2025-04-28 Upload folder using huggingface_hub 8d4744f 2025-04-27 initial commit c52c071
All providers 1 provider · 1 endpoint Where to run 1 providers · 1 endpoints Hosted endpoints observed for Qwen3-1.7B, sorted by input price (USD per million tokens). Each row names its observer and date; prices change daily, so check the source before quoting.
Provider Model ref Context Quant $/M in $/M out tok/s TTFT Tools Status Observed Featherlesshf
Qwen/Qwen3-1.7B — — — — — — — live 2026-10-25
Articles and coverage 23 pieces Ranked by source signals (HN, Lobsters, authority domains, official posts) and recency. Summaries are generated from a stored copy of the article text; every sentence cites a span of that text, and nothing is shown that failed the citation check.
Official1 qwenlm.github.io 2025-04-28 official authority HN front page about the Qwen3 family HN 869 pts · 388 comments 2,139 words
Qwen3 introduces Hybrid Thinking Modes, allowing users to toggle between step-by-step reasoning and rapid responses via computational budget control. The open-weight MoE model Qwen3-235B-A22B achieves performance similar to dense models while using only 10% of the active parameters. Pre-training utilized approximately 36 trillion tokens across 119 languages, with deployment supported by SGLang and vLLM.written, reviewed, cited
Qwen3-8B and Qwen3-14B models do not use tied embeddings, unlike smaller variants. The pre-training pipeline includes a final stage extending context length to 32K tokens. Analysis9 venturebeat.com 2026-02-28 HN front page about the Qwen3 family HN 461 pts · 272 comments
magazine.sebastianraschka.com 2025-08-10 authority HN front page about the Qwen3 family HN 490 pts · 97 comments 5,195 words
OpenAI released gpt-oss-120b and gpt-oss-20b, their first open-weight models since GPT-2 in 2019. The 20B model can run on a consumer GPU with up to 16 GB of RAM, and the 120B model can run on a single H100 with 80 GB of RAM or newer hardware. The models utilize MXFP4 optimization to fit onto single GPUs.written, reviewed, cited
A 2025 research paper confirms Dropout results in worse downstream performance in single-epoch regimes. arxiv.org authority about the Qwen3 family
artificialanalysis.ai authority
artificialanalysis.ai authority
magazine.sebastianraschka.com authority about the Qwen3 family
artificialanalysis.ai authority
artificialanalysis.ai authority
All coverage tracked for this model (23) — append-only, nothing is ever removed 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).
Similar models 3 models Sources 3 sources Sources Hub snapshot: 2026-10-25 · HF API: 2026-10-24 21:33 UTC · Providers: 2026-10-24 21:44 UTC · Articles: 2026-10-24 21:45 UTC · Summary: 2026-10-07 15:00 UTC · Last verified 2026-10-25
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