Models › Qwen3 family

Qwen3-Next-80B-A3B-Instruct

Qwen Apache 2.0 out 14 months ago

81.3B parameters, 3B active (10 of 512 experts per token), text in, text out, 256K tokens of context. 4 providers serve it from $0.072 per million input tokens.

Verified 2026-10-25 Rank #50 in LLMs · score 107.9 method v1 Sources 5 Downloads 30d 153.5k · #269 of 619 tracked

Changed this week: Cheapest input price fell 20%: $0.090 → $0.072 per 1M (OpenRouter).2026-10-24 · 1 new quantized build on the Hub (78 in all).2026-10-24

Can I run it?49GB at INT4Estimatefits a 64 GB Mac (unified memory)method
What does it cost?$0.072/1M incheapest of 3DeepInfrafp82026-10-25
Can I use it?Apache 2.0Permissive; patent grantlicence file2026-10-24
Do people use it?153.5kdownloads, 30 days#269 of 619 we track · top 43%+4 likes, downloads −20% this week1,069 likesHugging Face2026-10-24

Worth reading and watching

Picked by rule from 18 tracked pieces: the lab's own post, the most shared, a field report, the latest.

Most discussed · 490 HN pts

GPT-OSS vs. Qwen3 and a detailed look how things evolved since GPT-2

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

18 best of 18 tracked

The 2 picks above, plus these 16. Top is the radar's score: discussion, source authority and the lab's own posts, fading with age.

  1. ArticleQwen3-Coder-Nextqwen.ai · 2026-02-03 · official
  2. ArticleQwen3-Max-Thinkingqwen.ai · 2026-01-26 · official
  3. ArticleQwen3-TTS family is now open sourced: Voice design, clone, and generationqwen.ai · 2026-01-22 · official
  4. ArticleQwen3: Think deeper, act fasterqwenlm.github.io · 2025-04-28 · 869 HN pts · official
  5. ArticleQwen3-Coder: Agentic coding in the worldqwenlm.github.io · 2025-07-22 · official
  6. ArticleQwen3-Nextqwen.ai · 2025-09-12 · 569 HN pts · official
  7. ArticleQwen3-VLqwen.ai · 2025-09-23 · official
  8. CodeQwen3-Omni: Native Omni AI model for text, image and videogithub.com · 2025-09-22
  9. ArticleQwen3 Next 80B A3B - Intelligence, Performance & Price ...artificialanalysis.ai · undated
  10. ArticleQwen3-Next: Towards Ultimate Training & Inference Efficiencyalibabacloud.com · undated · official
  11. ArticleQwen3-Next: Towards Ultimate Training & Inference Efficiency - Alibaba Cloud Communityalibabacloud.com · 2025-10-14 · official
  12. Articleqwen / qwen3-next-80b-a3b-instructdocs.api.nvidia.com · undated
  13. VideoQwen3-Next-80B First Look & Hands-On Testing - YouTubeyoutube.com · undated
  14. ArticleQwen3 Next 80B A3B Instructopper.ai · undated
  15. Articleqwen / qwen3-next-80b-a3b-instructdocs.api.nvidia.com · undated
  16. Articleqwen3-next-80b-a3b-instruct: Full Review (80B MoE, 3B Active) - TokenMix Blogtokenmix.ai · 2026-04-25

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×

3 providers with a live price, from $0.072 to $0.15 per million input tokens; the median is $0.10. One dot per provider, cheapest in teal.

$0.010$0.10$1.00$10.00DeepInfra: $0.072 in / $0.88 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/Qwen/Qwen3-Next-80B-A3B-Instruct?expand[]=inferenceProviderMappingParasail: $0.10 in / $1.10 out per 1M · observed 2026-10-24 · https://docs.parasail.io/parasail-docs/billing/pricingGoogle: $0.15 in / $1.20 out per 1M · observed 2026-10-24 · https://openrouter.ai/qwen/qwen3-next-80b-a3b-instructcheapest $0.072median $0.10
$0.010$0.10$1.00$10.00DeepInfra: $0.072 in / $0.88 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/Qwen/Qwen3-Next-80B-A3B-Instruct?expand[]=inferenceProviderMappingParasail: $0.10 in / $1.10 out per 1M · observed 2026-10-24 · https://docs.parasail.io/parasail-docs/billing/pricingGoogle: $0.15 in / $1.20 out per 1M · observed 2026-10-24 · https://openrouter.ai/qwen/qwen3-next-80b-a3b-instructcheapest $0.072 · median $0.10 (white tick)
#Cheapest 4In /1MOut /1MContextQuantSeen
1 DeepInfra $0.072 $0.88 256K fp8 Oct 25
2 Parasail $0.10 $1.10 256K fp8 Oct 24
3 Google $0.15 $1.20 256K unknown Oct 24
4 Featherless — — — — Oct 25

All 4 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.

Served by 4 API providers; heavily run locally (78 quantized builds); a big base for builders (57 fine-tunes, adapters and merges).

  • As a service4DeepInfra, Featherless, Google, Parasail
  • On own hardware78quantized builds
  • Built on5740 fine-tunes · 16 adapters · 1 merges
  • In apps100+Hugging Face Spaces

Top Spaces running it: hadadxyz/ai ♥ 819 · LPX55/Qwen-Image-Edit-2511-Turbo-Lightning ♥ 121 · jairwaal/image ♥ 16 · Gameyguy/Qwen-Image-Edit-2511-Turbo-Lightning ♥ 12

Will it fit?

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

Fits a 64 GB Mac (unified memory) at INT4

About 49 GB at INT4, 98 GB at FP8, 195 GB at BF16: weights only, so leave room for the KV cache. Every expert must be resident, so total parameters count.

16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 49 GB of weights (estimate; KV cache and activations extra)INT449 GBFP8: about 98 GB of weights (estimate; KV cache and activations extra)FP898 GBBF16: about 195 GB of weights (estimate; KV cache and activations extra)BF16195 GB
16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 49 GB of weights (estimate; KV cache and activations extra)INT449 GBFP8: about 98 GB of weights (estimate; KV cache and activations extra)FP898 GBBF16: about 195 GB of weights (estimate; KV cache and activations extra)BF16195 GB

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

Day-one receipts

Claim-matched tests, graded by code. Receipts, not a score.

Speed run● pass
{
  "providers": [
    {
      "provider": "parasail",
      "endpoint_model": "parasail-qwen-3-next-80b-instruct",
      "source_url": "https://api.parasail.io/v1/chat/completions",
      "ttft_ms_median": 562,
      "ttft_any_ms_median": 562,
      "tokens_per_s_median": 112.1,
      "runs": [
        {
          "ttft_s": 0.548,
          "ttft_any_s": 0.548,
          "tokens_per_s": 112.1,
          "latency_s": 1.62,
          "tokens_out": 182,
          "reasoning_chars": 0,
          "finish": "stop"
        },
        {
          "ttft_s": 0.562,
          "ttft_any_s": 0.562,
          "tokens_per_s": 131.5,
          "latency_s": 1.41,
          "tokens_out": 185,
          "reasoning_chars": 0,
          "finish": "stop"
        },
        {
          "ttft_s": 1.375,
          "ttft_any_s": 1.375,
          "tokens_per_s": 98.2,
          "latency_s": 2.21,
          "tokens_out": 217,
          "reasoning_chars": 0,
          "finish": "stop"
        }
      ]
    }
  ],
  "note": "Single client, wall clock; tok/s counts every output token including hidden reasoning."
}
Three-tool errand● pass
{
  "calls": [
    {
      "name": "get_weather",
      "args": {
        "city": "Lisbon",
        "date": "2026-10-14"
      }
    },
    {
      "name": "check_calendar",
      "args": {
        "date": "2026-10-14"
      }
    },
    {
      "name": "book_table",
      "args": {
        "restaurant": "Cervejaria Ramiro",
        "date": "2026-10-14",
        "time": "19:30",
        "party_size": 2
      }
    }
  ],
  "checks": {
    "get_weather": true,
    "check_calendar": true,
    "book_table": true
  },
  "final": "Your table at Cervejaria Ramiro has been confirmed for Wednesday, 2026-10-14 at 19:30 for 2 people. Confirmation code: TBL-4471. The weather in Lisbon is clear with a very low chance of rain, so your evening plans are set!",
  "fixture": "receipts.errand.v1"
}
calls in order · args 3/3 · confirmed · 2026-10-092.5 s
Messy addresses to JSON● pass
{
  "fields_matched": 79,
  "fields_total": 80,
  "cost_per_1000_usd": 0.0421,
  "parsed": true,
  "per_item": [
    {
      "street": true,
      "city": true,
      "postcode": true,
      "country": true
    },
    {
      "street": true,
      "city": true,
      "postcode": true,
      "country": true
    },
    {
      "street": true,
      "city": true,
      "postcode": true,
      "country": true
    },
    {
      "street": true,
      "city": true,
      "postcode": true,
      "country": true
    },
    {
      "street": true,
      "city": true,
      "postcode": true,
      "country": true
    },
    {
      "street": true,
      "city": true,
      "postcode": true,
      "country": true
    },
    {
      "street": true,
      "city": true,
      "postcode": true,
      "country": true
    },
    {
      "street": true,
      "city": true,
      "postcode": true,
      "country": true
    },
    {
      "street": true,
      "city": true,
      "postcode": true,
      "country": true
    },
    {
      "street": true,
      "city": true,
      "postcode": true,
      "country": true
    },
    {
      "street": true,
      "city": true,
      "postcode": true,
      "country": true
    },
    {
      "street": true,
      "city": true,
      "postcode": true,
      "country": true
    },
    {
      "street": true,
      "city": true,
      "postcode": true,
      "country": true
    },
    {
      "street": true,
      "city": true,
      "postcode": true,
      "country": true
    },
    {
      "street": true,
      "city": true,
      "postcode": true,
      "country": true
    },
    {
      "street": true,
      "city": false,
      "postcode": true,
      "country": true
    },
    {
      "street": true,
      "city": true,
      "postcode": true,
      "country": true
    },
    {
      "street": true,
      "city": true,
      "postcode": true,
      "country": true
    },
    {
      "street": true,
      "city": true,
      "postcode": true,
      "country": true
    },
    {
      "street": true,
      "city": true,
      "postcode": true,
      "country": true
    }
  ],
  "sample": [
    {
      "street": "1600 Amphitheatre Parkway",
      "unit": null,
      "city": "Mountain View",
      "region": "CA",
      "postcode": "94043",
      "country": "US"
    },
    {
      "street": "10 Downing Street",
      "unit": null,
      "city": "London",
      "region": null,
      "postcode": "SW1A 2AA",
      "country": "GB"
    },
    {
      "street": "Rua Augusta 274",
      "unit": null,
      "city": "Lisboa",
      "region": null,
      "postcode": "1100-053",
      "country": "PT"
    }
  ],
  "fixture": "receipts.addresses.v1"
}
79/80 fields · $0.0421 per 1,000 · 2026-10-094.6 s

The story so far

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

Most coverage came in the first two weeks

Teal is the weights (2025-09-09), amber triangles are 5 dated articles (each links to it), grey ticks are repo commits.

MayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOct2025-09-17: 1 repo commit2025-09-15: 2 repo commits2025-09-12: 1 repo commit2025-09-11: 7 repo commits2025-09-09: 2 repo commitsQwen3.5 122B and 35B models offer Sonnet 4.5 performance on local computers · 2026-02-28Qwen3: Think deeper, act faster · 2025-04-28Qwen3-Next · 2025-09-12GPT-OSS vs. Qwen3 and a detailed look how things evolved since GPT-2 · 2025-08-10Qwen3-Next: Towards Ultimate Training & Inference Efficiency - Alibaba Cloud Community · 2025-10-14Weights 2025-09-09 · 2025-09-09Weights 2025-09-09now · 4 providers
MayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOct2025-09-17: 1 repo commit2025-09-15: 2 repo commits2025-09-12: 1 repo commit2025-09-11: 7 repo commits2025-09-09: 2 repo commitsQwen3.5 122B and 35B models offer Sonnet 4.5 performance on local computers · 2026-02-28Qwen3: Think deeper, act faster · 2025-04-28Qwen3-Next · 2025-09-12GPT-OSS vs. Qwen3 and a detailed look how things evolved since GPT-2 · 2025-08-10Qwen3-Next: Towards Ultimate Training & Inference Efficiency - Alibaba Cloud Community · 2025-10-14Weights 2025-09-09 · 2025-09-09Weights 2025-09-09now · 4 providers

What people built on it

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

135 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.

78 quantized (Hugging Face model tree)40 fine-tunes (Hugging Face model tree)1 merge (Hugging Face model tree)16 adapters (Hugging Face model tree)78 quantized40 fine-tunes1 merge16 adapters
78 quantized (Hugging Face model tree)40 fine-tunes (Hugging Face model tree)1 merge (Hugging Face model tree)16 adapters (Hugging Face model tree)78 quantized40 fine-tunes1 merge16 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 2025 · Reason through hard problems69.5—model author2026-10-07
author-reported Aider Polyglot · Write code with agents49.8—model author2026-10-07
author-reported Arena-Hard · Chat and follow instructions82.7—model author2026-10-07
author-reported BFCL · Call tools and APIs70.3—model author2026-10-07
author-reported Creative Writing · Chat and follow instructions85.3—model author2026-10-07
author-reported Creative Writing · Chat and follow instructions87.3—model author2026-10-07
author-reported GPQA Diamond · Reason through hard problems72.9—model author2026-10-07
author-reported HMMT · Reason through hard problems54.1—model author2026-10-07
author-reported IFEval · Chat and follow instructions87.6—model author2026-10-07
author-reported LiveBench · Reason through hard problems75.8—model author2026-10-07
author-reported LiveCodeBench · Write code with agents56.6—model author2026-10-07
author-reported MMLU · Reason through hard problems90.9—model author2026-10-07
author-reported MMLU-Pro · Reason through hard problems80.6—model author2026-10-07
author-reported MultiPL-E · Write code with agents87.8—model author2026-10-07
author-reported SuperGPQA · Reason through hard problems58.8—model author2026-10-07
author-reported TAU1-Airline44—model author2026-10-07
author-reported TAU1-Retail60.9—model author2026-10-07
author-reported tau-bench · Call tools and APIs57.3—model author2026-10-07
author-reported tau-bench · Call tools and APIs45.5—model author2026-10-07

What the card says

1 cited sentence

Summary generated, cited

It utilizes a hybrid attention mechanism and features 512 experts with 10 experts per token.[1][2][3]

  1. “Hybrid Attention”
  2. num experts
  3. experts per token

Generated 2026-10-07 14:59 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

24 fields

Listed in LLMs · MoE

Facts

Pipeline tagtext-generation
Librarytransformers
ArchitecturesQwen3NextForCausalLM
Model typeqwen3_next
Dtypebfloat16
Params by dtypeBF16: 81,324,862,720
Size bandXL
MoEyes
Experts512
Experts per token10
Context length262,144 (config.json max_position_embeddings)
PapersarXiv:2309.00071, arXiv:2404.06654, arXiv:2505.09388, arXiv:2501.15383
LicenseApache 2.0 (apache-2.0)
Hub snapshot2026-10-25

Servability

vLLM · SGLang

Servability

vLLM: supported · Qwen3NextForCausalLM · 7050ef881ff0835f6890a47db1859a24c65481d6SGLang: supported · Qwen3NextForCausalLM · 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 Qwen3-Next-80B-A3B-Instruct: params × bytes/param × 1.2 overhead. KV cache and activations are extra. How this is computed.

BF16 (2 bytes/param)195 GB · Multi-GPU node (<640 GB)
FP8 (1 byte/param)98 GB · Multi-GPU node (<640 GB)
INT4 (0.5 bytes/param)49 GB · Single high-end GPU (<80 GB)

Weights only (params × bytes/param × 1.2 overhead); KV cache and activations extra. MoE: all experts must be resident, so total parameters count.

All variants

40 variants

Lineage

Original model Family: Qwen/Qwen3 135 derivatives on the Hub

quantized: 78finetune: 40adapter: 16merge: 1

Top variants (40 shown, ranked by downloads)

Relation
Quant
Size
40 rows
Variant Relation Quant Params Band License Author Likes Downloads
Qwen/Qwen3-Next-80B-A3B-Instruct-FP8 quantized -FP8 81.3B XL apache-2.0 Qwen 98 3.3M
cyankiwi/Qwen3-Next-80B-A3B-Instruct-AWQ-4bit quantized AWQ 81.3B XL apache-2.0 cyankiwi 68 1.9M
tiny-random/qwen3-next-moe finetune — 2.84M S — tiny-random 4 676.4k
unsloth/Qwen3-Next-80B-A3B-Instruct-bnb-4bit quantized -bnb — — apache-2.0 unsloth 26 531.8k
unsloth/Qwen3-Next-80B-A3B-Instruct-GGUF quantized GGUF — — apache-2.0 unsloth 201 510.2k
lmstudio-community/Qwen3-Next-80B-A3B-Instruct-MLX-4bit quantized MLX 79.7B XL apache-2.0 lmstudio-community 7 432.9k
lmstudio-community/Qwen3-Next-80B-A3B-Instruct-MLX-8bit quantized MLX 79.7B XL apache-2.0 lmstudio-community 3 406.3k
lmstudio-community/Qwen3-Next-80B-A3B-Instruct-MLX-6bit quantized MLX 79.7B XL apache-2.0 lmstudio-community 0 395k
lmstudio-community/Qwen3-Next-80B-A3B-Instruct-MLX-5bit quantized MLX 79.7B XL apache-2.0 lmstudio-community 0 392.9k
nvidia/Qwen3-Next-80B-A3B-Instruct-NVFP4 quantized NVFP4 — — apache-2.0 nvidia 46 353.1k
TheClusterDev/Qwen3-Next-80B-A3B-Instruct-FP8-Dynamic quantized -FP8 79.8B XL — TheClusterDev 4 242.8k
cyankiwi/Qwen3-Next-80B-A3B-Instruct-AWQ-8bit quantized AWQ 81.3B XL apache-2.0 cyankiwi 4 213.4k
DevQuasar/Qwen.Qwen3-Next-80B-A3B-Instruct-FP8-Dynamic quantized -FP8 79.8B XL — DevQuasar 4 178.6k
bartowski/Qwen_Qwen3-Next-80B-A3B-Instruct-GGUF quantized GGUF — — apache-2.0 bartowski 22 144.6k
lefromage/Qwen3-Next-80B-A3B-Instruct-GGUF quantized GGUF — — apache-2.0 lefromage 47 135.5k
mlx-community/Qwen3-Next-80B-A3B-Instruct-4bit quantized -4bit 79.7B XL apache-2.0 mlx-community 25 108.9k
mlx-community/Qwen3-Next-80B-A3B-Instruct-8bit quantized -8bit 79.7B XL apache-2.0 mlx-community 10 103.6k
lmstudio-community/Qwen3-Next-80B-A3B-Instruct-GGUF quantized GGUF — — apache-2.0 lmstudio-community 1 94.7k
RedHatAI/Qwen3-Next-80B-A3B-Instruct-NVFP4 quantized NVFP4 — — apache-2.0 RedHatAI 5 82.6k
RESMP-DEV/Qwen3-Next-80B-A3B-Instruct-NVFP4 quantized NVFP4 — — apache-2.0 RESMP-DEV 8 42.9k
Intel/Qwen3-Next-80B-A3B-Instruct-int4-mixed-AutoRound quantized int4 11.5B L apache-2.0 Intel 24 38.1k
RedHatAI/Qwen3-Next-80B-A3B-Instruct-quantized.w4a16 quantized -quant 12.2B L apache-2.0 RedHatAI 4 35.9k
yujiepan/qwen3-next-moe-tiny-random finetune — 2.84M S — yujiepan 0 33.3k
RedHatAI/Qwen3-Next-80B-A3B-Instruct-FP8 quantized -FP8 81.3B XL apache-2.0 RedHatAI 0 25.9k
TheClusterDev/Qwen3-Next-80B-A3B-Instruct-FP8 quantized -FP8 79.8B XL — TheClusterDev 1 23k
unsloth/Qwen3-Next-80B-A3B-Instruct finetune — 81.3B XL apache-2.0 unsloth 88 10.7k
dazipe/Qwen3-Next-80B-A3B-Instruct-GPTQ-Int4A16 quantized GPTQ 14.4B L — dazipe 5 12.8k
mradermacher/Qwen3-Next-80B-A3B-Instruct-i1-GGUF quantized GGUF — — apache-2.0 mradermacher 2 12.6k
Intel/Qwen3-Next-80B-A3B-Instruct-int4-AutoRound quantized int4 11.2B L apache-2.0 Intel 10 7.6k
lovedheart/Qwen3-Next-REAP-40B-A3B-Instruct-GGUF quantized GGUF — — apache-2.0 lovedheart 1 8.1k
ilintar/Qwen3-Next-80B-A3B-Instruct-GGUF quantized GGUF — — apache-2.0 ilintar 11 7.5k
jart25/Qwen3-Next-80B-A3B-Instruct-Int4-GPTQ quantized Int4 — — — jart25 3 6.1k
mradermacher/Qwen3-Next-80B-A3B-Instruct-GGUF quantized GGUF — — apache-2.0 mradermacher 2 5.5k
noctrex/Qwen3-Next-80B-A3B-Instruct-MXFP4_MOE-GGUF quantized GGUF — — — noctrex 7 4.6k
lovedheart/Qwen3-Next-REAP-25B-A3B-Instruct-GGUF quantized GGUF — — apache-2.0 lovedheart 2 4.6k
lovedheart/Qwen3-Next-REAP-30B-A3B-Instruct-GGUF quantized GGUF — — apache-2.0 lovedheart 4 3.8k
John1604/Qwen3-Next-80B-A3B-Instruct-gguf quantized gguf — — apache-2.0 John1604 0 3k
mlx-community/Qwen3-Next-80B-A3B-Instruct-6bit quantized — 79.7B XL apache-2.0 mlx-community 4 2.2k
RedHatAI/Qwen3-Next-80B-A3B-Instruct-FP8-dynamic quantized -FP8 79.8B XL apache-2.0 RedHatAI 1 2.4k
LibraxisAI/Qwen3-Next-80B-A3B-Instruct-MLX-MXFP4 quantized MLX 79.7B XL apache-2.0 LibraxisAI 2 2.2k

Repository changelog

13 commits
changed in the last 7 daysPrice moves: 2026-10-24Other: 2026-10-24all changes

Repository changelog 13 commits

DateCommitSHA
2025-09-17 Update tokenizer_config.json 9c7f2fb
2025-09-15 Update README.md 3eb90af
2025-09-15 Update README.md 92d3dc0
2025-09-12 Update README.md 609718e
2025-09-11 Update README.md cfdff41
2025-09-11 Upload tokenizer.json c7646fb
2025-09-11 Delete tokenizer.json 7cec4bd
2025-09-11 Create LICENSE e62a222
2025-09-11 Update generation_config.json 9aeffe7
2025-09-11 Update config.json 1f3f9fc
2025-09-11 Update README.md f7597ba
2025-09-09 Add files using upload-large-folder tool 21dee27
2025-09-09 initial commit 219812d

All providers

4 providers · 4 endpoints

Where to run 4 providers · 4 endpoints

Hosted endpoints observed for Qwen3-Next-80B-A3B-Instruct, 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: $0.07/M.

ProviderModel refContextQuant$/M in$/M outtok/sTTFTToolsStatusObserved
DeepInfra
hf, deepinfra, openrouter
Qwen/Qwen3-Next-80B-A3B-Instruct 256K fp8 $0.07 $0.88 120.5 545 ms yes live 2026-10-25
Parasail
parasail, openrouter
parasail-qwen-3-next-80b-instruct 256K fp8 $0.10 $1.10 — — yes serverless 2026-10-24
Google
openrouter
qwen/qwen3-next-80b-a3b-instruct 256K unknown $0.15 $1.20 — — yes live 2026-10-24
Featherless
hf
Qwen/Qwen3-Next-80B-A3B-Instruct — — — — — — — live 2026-10-25

Articles and coverage

18 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.

Official4

qwenlm.github.io2025-04-28officialauthorityHN front pageabout the Qwen3 familyHN 869 pts · 388 comments2,139 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.
qwen.ai2025-09-12officialauthorityHN front pageabout the Qwen3 familyHN 569 pts · 230 comments2,208 words
alibabacloud.comofficialabout the Qwen3 family
alibabacloud.com2025-10-14officialabout the Qwen3 family

Analysis6

venturebeat.com2026-02-28HN front pageabout the Qwen3 familyHN 461 pts · 272 comments
magazine.sebastianraschka.com2025-08-10authorityHN front pageabout the Qwen3 familyHN 490 pts · 97 comments5,195 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.
artificialanalysis.aiauthorityabout the Qwen3 family
docs.api.nvidia.com
youtube.comabout the Qwen3 family
opper.ai
All coverage tracked for this model (18) — append-only, nothing is ever removed
TitleSourcePublishedFirst seenSignals
Qwen3 Next 80B A3B - Intelligence, Performance & Price ...artificialanalysis.ai—2026-10-07exa
Qwen3-Next: Towards Ultimate Training & Inference Efficiencyalibabacloud.com official—2026-10-07exa
qwen / qwen3-next-80b-a3b-instructdocs.api.nvidia.com—2026-10-07exa
Qwen3-Next-80B First Look & Hands-On Testing - YouTubeyoutube.com—2026-10-07exa
Qwen3 Next 80B A3B Instructopper.ai—2026-10-07exa
qwen / qwen3-next-80b-a3b-instructdocs.api.nvidia.com—2026-10-07exa
qwen3-next-80b-a3b-instruct: Full Review (80B MoE, 3B Active) - TokenMix Blogtokenmix.ai2026-04-252026-10-07exa
Qwen3.5 122B and 35B models offer Sonnet 4.5 performance on local computersventurebeat.com2026-02-282026-10-07hn
Qwen3-Coder-Nextqwen.ai official2026-02-032026-10-07hn
Qwen3-Max-Thinkingqwen.ai official2026-01-262026-10-07hn
Qwen3-TTS family is now open sourced: Voice design, clone, and generationqwen.ai official2026-01-222026-10-07hn
Qwen3-Next: Towards Ultimate Training & Inference Efficiency - Alibaba Cloud Communityalibabacloud.com official2025-10-142026-10-07exa
Qwen3-VLqwen.ai official2025-09-232026-10-07hn
Qwen3-Omni: Native Omni AI model for text, image and videogithub.com2025-09-222026-10-07hn
Qwen3-Nextqwen.ai official2025-09-122026-10-07hn
GPT-OSS vs. Qwen3 and a detailed look how things evolved since GPT-2magazine.sebastianraschka.com2025-08-102026-10-07hn
Qwen3-Coder: Agentic coding in the worldqwenlm.github.io official2025-07-222026-10-07hn
Qwen3: Think deeper, act fasterqwenlm.github.io official2025-04-282026-10-07hn

Papers

4 papers

Papers 4

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

Similar models

  • Hunyuan-A13B-Instruct · 80.4B
    same size band (XL), also MoE, same task (text-generation), fewer providers
  • gpt-oss-120b · 117B
    also MoE, same task (text-generation), more providers, permissive license
  • GLM-4.5-Air · 110B
    also MoE, same task (text-generation), permissive license

Sources

5 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

Follow Qwen3-Next-80B-A3B-Instruct

Watch this model

Email alerts are not open yet; the feeds above are the same data. See feeds for the item schema.