Worth reading Picked by rule from 19 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
bestllmfor.com
Field report
llm-stats.com
Latest · 8 months ago
venturebeat.com 2026-02-28 461 HN pts · 272 comments about the Qwen3 family family
19 best of 19 tracked The 4 picks above, plus these 15. Top is the radar's score: discussion, source authority and the lab's own posts, fading with age.
All 15 Articles13 Code1 Papers1
Top Newest
Article Qwen3-Coder-Next qwen.ai · 2026-02-03 · official Article Qwen3-Max-Thinking qwen.ai · 2026-01-26 · official Article Qwen3-TTS family is now open sourced: Voice design, clone, and generation qwen.ai · 2026-01-22 · official Article Qwen3: Think deeper, act faster qwenlm.github.io · 2025-04-28 · 869 HN pts · official Article Qwen3-Coder: Agentic coding in the world qwenlm.github.io · 2025-07-22 · official Article Qwen3-Next qwen.ai · 2025-09-12 · official Article Qwen3-VL qwen.ai · 2025-09-23 · official Code Qwen3-Omni: Native Omni AI model for text, image and video github.com · 2025-09-22 Paper Qwen3 Technical Report - arXiv arxiv.org · undated Article Qwen3 32B (non-reasoning) - Artificial Analysis artificialanalysis.ai · undated Article Qwen3: Features, DeepSeek-R1 Comparison, Access, and More datacamp.com · undated Article Qwen3 32B - 32B model, AA Index 8.6 | Every Local AI everylocalai.com · undated Article Qwen/Qwen3-32B Hardware, VRAM, and Deployment Guide | InnoAI innoai.space · undated Article Qwen3 Released: How Does It Stack Up? runpod.io · undated Article Qwen3-32B Review — Benchmarks, Pricing & AI Panel Verdict | TopReviewed.ai topreviewed.ai · 2025-04-28 Show all 15 Where to run it Live hosted endpoints, cheapest first, USD per million tokens. Ties break on output price, then provider name.
Prices for the same weights vary 2× 4 providers with a live price, from $0.080 to $0.14 per million input tokens; the median is $0.11. One dot per provider, cheapest in teal.
$0.010 $0.10 $1.00 $10.00 Nscale: $0.080 in / $0.25 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/Qwen/Qwen3-32B?expand[]=inferenceProviderMapping DeepInfra: $0.080 in / $0.28 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/Qwen/Qwen3-32B?expand[]=inferenceProviderMapping Tenstorrent: $0.14 in / $0.40 out per 1M · observed 2026-10-24 · https://openrouter.ai/qwen/qwen3-32b SiliconFlow: $0.14 in / $0.57 out per 1M · observed 2026-10-24 · https://openrouter.ai/qwen/qwen3-32b cheapest $0.080 median $0.11
$0.010 $0.10 $1.00 $10.00 Nscale: $0.080 in / $0.25 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/Qwen/Qwen3-32B?expand[]=inferenceProviderMapping DeepInfra: $0.080 in / $0.28 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/Qwen/Qwen3-32B?expand[]=inferenceProviderMapping Tenstorrent: $0.14 in / $0.40 out per 1M · observed 2026-10-24 · https://openrouter.ai/qwen/qwen3-32b SiliconFlow: $0.14 in / $0.57 out per 1M · observed 2026-10-24 · https://openrouter.ai/qwen/qwen3-32b cheapest $0.080 · median $0.11 (white tick)
# Cheapest 5 In /1M Out /1M Context Quant Seen 1 Nscale $0.080 $0.25 40K — Oct 25 2 DeepInfra $0.080 $0.28 40K fp8 Oct 25 3 Tenstorrent $0.14 $0.40 20K fp8 Oct 24 4 SiliconFlow $0.14 $0.57 128K fp8 Oct 24 5 Featherless — — — — Oct 25
All 5 providers →
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.
Widely served (5 API providers); heavily run locally (188 quantized builds); a big base for builders (1,100 fine-tunes, adapters and merges).
As a service 5 DeepInfra, Featherless, Nscale, SiliconFlow +1 On own hardware 188 quantized builds Built on 1,100 593 fine-tunes · 488 adapters · 19 merges In apps 100+ Hugging Face Spaces Top Spaces running it: Qwen/Qwen3-Demo ♥ 868 · pliny-the-prompter/obliteratus ♥ 621 · embedl/hfviewer ♥ 181 · fdaudens/podcast-jobs ♥ 81
Will it fit? Weights only, an estimate: leave room for the KV cache. Method
Fits a 64 GB Mac (unified memory) at INT4 About 20 GB at INT4, 39 GB at FP8, 79 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 20 GB of weights (estimate; KV cache and activations extra) INT4 20 GB FP8: about 39 GB of weights (estimate; KV cache and activations extra) FP8 39 GB BF16: about 79 GB of weights (estimate; KV cache and activations extra) BF16 79 GB
16 GB card 64 GB Mac 2×80 GB 8×80 GB INT4: about 20 GB of weights (estimate; KV cache and activations extra) INT4 20 GB FP8: about 39 GB of weights (estimate; KV cache and activations extra) FP8 39 GB BF16: about 79 GB of weights (estimate; KV cache and activations extra) BF16 79 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.
#5 of 28 on BFCL so far 48.71. 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/Qwen3-235B-A22B-Instruct-2507: 47.99 · 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 Qwen/Qwen3-1.7B: 28.41 · 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 48.71 · #5 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/Qwen3-235B-A22B-Instruct-2507: 47.99 · 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 Qwen/Qwen3-1.7B: 28.41 · 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 48.71 · #5 of 28
Source · leaderboard · 2026-10-25
The story so far Weights, every dated article, and repo commits on one rail.
3 dated articles and 19 repo commits so far Teal is the weights (2025-04-27), amber triangles are 3 dated articles (each links to it), grey ticks are repo commits.
What people built on it 1,288 derivatives on the Hub. Which build to run, by runtime: the most downloaded in each class.
1,288 derivatives on the Hub, led by 593 fine-tunes From the Hugging Face model tree; quantized builds in teal, fine-tunes, merges and adapters in greys.
188 quantized (Hugging Face model tree) 593 fine-tunes (Hugging Face model tree) 19 merges (Hugging Face model tree) 488 adapters (Hugging Face model tree) 188 quantized593 fine-tunes19 merges488 adapters
188 quantized (Hugging Face model tree) 593 fine-tunes (Hugging Face model tree) 19 merges (Hugging Face model tree) 488 adapters (Hugging Face model tree) 188 quantized593 fine-tunes19 merges488 adapters
All variants →
Reference Everything else, folded: the full tables, every source.
Benchmarks 3 rows Benchmarks 3 rows measured third-party leaderboard row (3)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.
What the card says 1 cited sentence 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: 32,762,123,264 Size band L MoE no Context length 40,960 (config.json max_position_embeddings) Papers arXiv:2309.00071 , 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 Qwen3-32B: params × bytes/param × 1.2 overhead. KV cache and activations are extra. How this is computed .
BF16 (2 bytes/param) 79 GB · Single high-end GPU (<80 GB) FP8 (1 byte/param) 39 GB · Single high-end GPU (<80 GB) INT4 (0.5 bytes/param) 20 GB · Single GPU (<24 GB)
Weights only (params × bytes/param × 1.2 overhead); KV cache and activations extra.
All variants 40 variants Lineage quantized: 188 merge: 19 finetune: 593 adapter: 488
Top variants (40 shown, ranked by downloads) Repository changelog 19 commits Repository changelog 19 commits Date Commit SHA 2025-07-26 Create LICENSE 9216db5 2025-05-21 Update README.md d47b0d4 2025-05-19 update tokenizer_config.json a09d5cc 2025-04-29 Update README.md 30b8421 2025-04-28 Update README.md ba1f828 2025-04-28 Update README.md c37ea5f 2025-04-28 Update README.md c36b753 2025-04-28 Delete special_tokens_map.json be2f114 2025-04-28 Delete added_tokens.json e3a3f59 2025-04-28 Update README.md 4d178d9 2025-04-28 Update README.md 395feeb 2025-04-28 Update README.md b1f6143 2025-04-28 Update README.md c141ee0 2025-04-28 Update README.md 817577e 2025-04-28 Update README.md 940b12c 2025-04-28 Update README.md c6740cd 2025-04-28 Update README.md 88f1f1f 2025-04-28 Upload folder using huggingface_hub 1aecd22 2025-04-27 initial commit 4c0e0ce
All providers 5 providers · 5 endpoints Where to run 5 providers · 5 endpoints Hosted endpoints observed for Qwen3-32B, sorted by input price (USD per million tokens). Each row names its observer and date; prices change daily, so check the source before quoting. Cheapest observed input price: $0.08/M .
Provider Model ref Context Quant $/M in $/M out tok/s TTFT Tools Status Observed Nscalehf
Qwen/Qwen3-32B 40K — $0.08 $0.25 45.9 421 ms yes live 2026-10-25 DeepInfrahf, deepinfra, openrouter
Qwen/Qwen3-32B 40K fp8 $0.08 $0.28 32.2 611 ms yes live 2026-10-25 Tenstorrentopenrouter
qwen/qwen3-32b 20K fp8 $0.14 $0.40 — — yes live 2026-10-24 SiliconFlowopenrouter
qwen/qwen3-32b 128K fp8 $0.14 $0.57 — — yes live 2026-10-24 Featherlesshf
Qwen/Qwen3-32B — — — — — — — live 2026-10-25
Articles and coverage 19 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
datacamp.com about the Qwen3 family
All coverage tracked for this model (19) — append-only, nothing is ever removed Title Source Published First seen Signals Qwen3 Technical Report - arXiv arxiv.org — 2026-10-07 exa Qwen3 32B (non-reasoning) - Artificial Analysis artificialanalysis.ai — 2026-10-07 exa Qwen 3 32B Review: 30 Days On Local Hardware | BestLLMfor bestllmfor.com — 2026-10-07 exa Qwen3 32B Benchmarks, Pricing & Context Window - LLM Stats llm-stats.com — 2026-10-07 exa Qwen3: Features, DeepSeek-R1 Comparison, Access, and More datacamp.com — 2026-10-07 exa Qwen3 32B - 32B model, AA Index 8.6 | Every Local AI everylocalai.com — 2026-10-07 exa Qwen/Qwen3-32B Hardware, VRAM, and Deployment Guide | InnoAI innoai.space — 2026-10-07 exa Qwen3 Released: How Does It Stack Up? runpod.io — 2026-10-07 exa Qwen3.5 122B and 35B models offer Sonnet 4.5 performance on local computers venturebeat.com 2026-02-28 2026-10-07 hn Qwen3-Coder-Next qwen.ai official 2026-02-03 2026-10-07 hn Qwen3-Max-Thinking qwen.ai official 2026-01-26 2026-10-07 hn Qwen3-TTS family is now open sourced: Voice design, clone, and generation qwen.ai official 2026-01-22 2026-10-07 hn Qwen3-VL qwen.ai official 2025-09-23 2026-10-07 hn Qwen3-Omni: Native Omni AI model for text, image and video github.com 2025-09-22 2026-10-07 hn Qwen3-Next qwen.ai official 2025-09-12 2026-10-07 hn GPT-OSS vs. Qwen3 and a detailed look how things evolved since GPT-2 magazine.sebastianraschka.com 2025-08-10 2026-10-07 hn Qwen3-Coder: Agentic coding in the world qwenlm.github.io official 2025-07-22 2026-10-07 hn Qwen3: Think deeper, act faster qwenlm.github.io official 2025-04-28 2026-10-07 hn, exa Qwen3-32B Review — Benchmarks, Pricing & AI Panel Verdict | TopReviewed.ai topreviewed.ai 2025-04-28 2026-10-07 exa
Papers 2 papers Papers 2 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 4 sources Sources Hub snapshot: 2026-10-25 · HF API: 2026-10-24 21:32 UTC · Providers: 2026-10-24 21:44 UTC · Articles: 2026-10-24 21:45 UTC · Summary: 2026-10-07 14:59 UTC · Last verified 2026-10-25
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