Models › Qwen3 family

Qwen3-235B-A22B-Instruct-2507

Qwen Apache 2.0 out 15 months ago

235B parameters, 22B active (8 of 128 experts per token), text in, text out, 256K tokens of context. 7 providers serve it from $0.087 per million input tokens.

Verified 2026-10-25 Rank #79 in LLMs · score 103.2 method v1 Sources 5 Downloads 30d 139.4k · #280 of 619 tracked

Changed this week: 7 new quantized builds on the Hub (75 in all).2026-10-24 · 6 new quantized builds on the Hub (74 in all).2026-10-24 · 1 new provider: Nscale.2026-10-23

Can I run it?141GB at INT4Estimatefits 2×80 GBmethod
What does it cost?$0.087/1M incheapest of 6GMICloudfp82026-10-24
Can I use it?Apache 2.0Permissive; patent grantlicence file2026-10-24
Does it work?52.15on BFCL#4 of 28 measuredleaderboard2026-10-25
Do people use it?139.4kdownloads, 30 days#280 of 619 we track · top 45%+3 likes, downloads −12% this week813 likesHugging Face2026-10-24

Worth reading

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 4 picks above, plus these 14. 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 · 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. ArticleQwen/Qwen3-235B-A22B-Instruct-2507simonwillison.net · undated
  10. ArticleQwen3 235B A22B 2507 - Intelligence, Performance & Price ...artificialanalysis.ai · undated
  11. ArticleQwen3 235B A22B Instruct 2507 API - Demo - DeepInfradeepinfra.com · undated
  12. ArticleQwen3 235B A22B Instruct 2507 — specs, hardware fit & pricing | LLMapllmap.ai · undated
  13. ArticleQwen 3 235B-A22B Review: The MoE Flagship Tested | BestLLMforbestllmfor.com · undated
  14. ArticleQwen3 235B A22B Instruct (2507)opper.ai · undated

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 10×

6 providers with a live price, from $0.087 to $0.85 per million input tokens; the median is $0.15. One dot per provider, cheapest in teal.

$0.010$0.10$1.00$10.00GMICloud: $0.087 in / $0.35 out per 1M · observed 2026-10-24 · https://openrouter.ai/qwen/qwen3-235b-a22b-2507DeepInfra: $0.090 in / $0.55 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/Qwen/Qwen3-235B-A22B-Instruct-2507?expand[]=inferenceProviderMappingParasail: $0.14 in / $0.80 out per 1M · observed 2026-10-24 · https://docs.parasail.io/parasail-docs/billing/pricingVenice: $0.15 in / $0.75 out per 1M · observed 2026-10-24 · https://openrouter.ai/qwen/qwen3-235b-a22b-2507Nscale: $0.20 in / $0.60 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/Qwen/Qwen3-235B-A22B-Instruct-2507?expand[]=inferenceProviderMappingScaleway: $0.85 in / $2.56 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/Qwen/Qwen3-235B-A22B-Instruct-2507?expand[]=inferenceProviderMappingcheapest $0.087median $0.15
$0.010$0.10$1.00$10.00GMICloud: $0.087 in / $0.35 out per 1M · observed 2026-10-24 · https://openrouter.ai/qwen/qwen3-235b-a22b-2507DeepInfra: $0.090 in / $0.55 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/Qwen/Qwen3-235B-A22B-Instruct-2507?expand[]=inferenceProviderMappingParasail: $0.14 in / $0.80 out per 1M · observed 2026-10-24 · https://docs.parasail.io/parasail-docs/billing/pricingVenice: $0.15 in / $0.75 out per 1M · observed 2026-10-24 · https://openrouter.ai/qwen/qwen3-235b-a22b-2507Nscale: $0.20 in / $0.60 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/Qwen/Qwen3-235B-A22B-Instruct-2507?expand[]=inferenceProviderMappingScaleway: $0.85 in / $2.56 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/Qwen/Qwen3-235B-A22B-Instruct-2507?expand[]=inferenceProviderMappingcheapest $0.087 · median $0.15 (white tick)
#Cheapest 5In /1MOut /1MContextQuantSeen
1 GMICloud $0.087 $0.35 256K fp8 Oct 24
2 DeepInfra $0.090 $0.55 256K fp8 Oct 25
3 Parasail $0.14 $0.80 128K fp8 Oct 24
4 Venice $0.15 $0.75 125K fp8 Oct 24
5 Nscale $0.20 $0.60 32K — Oct 25

All 7 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 (7 API providers); heavily run locally (75 quantized builds).

  • As a service7DeepInfra, GMICloud, Google, Nscale +3
  • On own hardware75quantized builds
  • Built on2920 fine-tunes · 9 adapters · 0 merges
  • In apps100+Hugging Face Spaces

Top Spaces running it: hadadxyz/ai ♥ 819 · multimodalart/Qwen-Image-Edit-Fast ♥ 495 · AiSudo/Qwen-Image-to-LoRA ♥ 344 · burtenshaw/karpathy-llm-council ♥ 292

Will it fit?

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

Fits two 80 GB GPUs at INT4; too big for one

About 141 GB at INT4, 282 GB at FP8, 564 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 141 GB of weights (estimate; KV cache and activations extra)INT4141 GBFP8: about 282 GB of weights (estimate; KV cache and activations extra)FP8282 GBBF16: about 564 GB of weights (estimate; KV cache and activations extra)BF16564 GB
16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 141 GB of weights (estimate; KV cache and activations extra)INT4141 GBFP8: about 282 GB of weights (estimate; KV cache and activations extra)FP8282 GBBF16: about 564 GB of weights (estimate; KV cache and activations extra)BF16564 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.

#4 of 28 on BFCL so far

52.15. Each tick is a model on this site with a measured BFCL row; author-reported numbers stay in Reference. Percent (overall accuracy). Prompt

zai-org/GLM-4.6: 72.38 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlmoonshotai/Kimi-K2-Instruct: 59.06 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmldeepseek-ai/DeepSeek-V3.2-Exp: 54.12 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-32B: 48.71 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-32B: 46.78 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlSalesforce/Llama-xLAM-2-8b-fc-r: 46.68 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-8B: 42.57 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-30B-A3B-Instruct-2507: 41.39 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-14B: 41.03 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-8B: 40.43 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-14B: 37.77 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-30B-A3B-Instruct-2507: 36.7 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-4B-Instruct-2507: 35.68 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-4B-Instruct-2507: 35.52 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlmeta-llama/Llama-3.3-70B-Instruct: 31.9 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlgoogle/gemma-3-12b-it: 30.43 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlgoogle/gemma-3-27b-it: 29.47 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlmicrosoft/phi-4: 28.79 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-1.7B: 28.41 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlmeta-llama/Llama-4-Scout-17B-16E-Instruct: 28.13 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlmeta-llama/Llama-3.1-8B-Instruct: 25.83 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-0.6B: 23.93 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-0.6B: 22.38 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlmeta-llama/Llama-3.2-3B-Instruct: 21.95 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlgoogle/gemma-3-4b-it: 19.62 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlgoogle/gemma-3-1b-it: 7.17 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html010052.15 · #4 of 28
zai-org/GLM-4.6: 72.38 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlmoonshotai/Kimi-K2-Instruct: 59.06 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmldeepseek-ai/DeepSeek-V3.2-Exp: 54.12 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-32B: 48.71 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-32B: 46.78 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlSalesforce/Llama-xLAM-2-8b-fc-r: 46.68 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-8B: 42.57 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-30B-A3B-Instruct-2507: 41.39 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-14B: 41.03 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-8B: 40.43 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-14B: 37.77 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-30B-A3B-Instruct-2507: 36.7 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-4B-Instruct-2507: 35.68 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-4B-Instruct-2507: 35.52 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlmeta-llama/Llama-3.3-70B-Instruct: 31.9 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlgoogle/gemma-3-12b-it: 30.43 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlgoogle/gemma-3-27b-it: 29.47 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlmicrosoft/phi-4: 28.79 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-1.7B: 28.41 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlmeta-llama/Llama-4-Scout-17B-16E-Instruct: 28.13 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlmeta-llama/Llama-3.1-8B-Instruct: 25.83 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-0.6B: 23.93 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlQwen/Qwen3-0.6B: 22.38 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlmeta-llama/Llama-3.2-3B-Instruct: 21.95 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlgoogle/gemma-3-4b-it: 19.62 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.htmlgoogle/gemma-3-1b-it: 7.17 · observed 2026-10-25 · https://gorilla.cs.berkeley.edu/leaderboard.html010052.15 · #4 of 28

Source · leaderboard · 2026-10-25

Day-one receipts

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

Speed run● pass
{
  "providers": [
    {
      "provider": "parasail",
      "endpoint_model": "parasail-qwen3-235b-a22b-instruct-2507",
      "source_url": "https://api.parasail.io/v1/chat/completions",
      "ttft_ms_median": 466,
      "ttft_any_ms_median": 466,
      "tokens_per_s_median": 40.1,
      "runs": [
        {
          "ttft_s": 0.466,
          "ttft_any_s": 0.466,
          "tokens_per_s": 42.3,
          "latency_s": 4.47,
          "tokens_out": 189,
          "reasoning_chars": 0,
          "finish": "stop"
        },
        {
          "ttft_s": 0.454,
          "ttft_any_s": 0.454,
          "tokens_per_s": 38.7,
          "latency_s": 4.85,
          "tokens_out": 188,
          "reasoning_chars": 0,
          "finish": "stop"
        },
        {
          "ttft_s": 0.507,
          "ttft_any_s": 0.507,
          "tokens_per_s": 40.1,
          "latency_s": 4.37,
          "tokens_out": 175,
          "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 successfully booked for Wednesday, 2026-10-14 at 19:30. Confirmation code: TBL-4471.",
  "fixture": "receipts.errand.v1"
}
calls in order · args 3/3 · confirmed · 2026-10-099.1 s
Messy addresses to JSON● pass
{
  "fields_matched": 80,
  "fields_total": 80,
  "cost_per_1000_usd": 0.0323,
  "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": 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
    }
  ],
  "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"
}
80/80 fields · $0.0323 per 1,000 · 2026-10-0918.9 s

The story so far

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

3 dated articles and 18 repo commits so far

Teal is the weights (2025-07-21), amber triangles are 3 dated articles (each links to it), grey ticks are repo commits.

MayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOct2025-09-17: 1 repo commit2025-08-17: 1 repo commit2025-08-08: 1 repo commit2025-07-30: 1 repo commit2025-07-29: 1 repo commit2025-07-21: 13 repo commitsQwen3.5 122B and 35B models offer Sonnet 4.5 performance on local computers · 2026-02-28Qwen3: Think deeper, act faster · 2025-04-28GPT-OSS vs. Qwen3 and a detailed look how things evolved since GPT-2 · 2025-08-10Weights 2025-07-21 · 2025-07-21Weights 2025-07-21now · 7 providers
MayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOct2025-09-17: 1 repo commit2025-08-17: 1 repo commit2025-08-08: 1 repo commit2025-07-30: 1 repo commit2025-07-29: 1 repo commit2025-07-21: 13 repo commitsQwen3.5 122B and 35B models offer Sonnet 4.5 performance on local computers · 2026-02-28Qwen3: Think deeper, act faster · 2025-04-28GPT-OSS vs. Qwen3 and a detailed look how things evolved since GPT-2 · 2025-08-10Weights 2025-07-21 · 2025-07-21Weights 2025-07-21now · 7 providers

What people built on it

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

104 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.

75 quantized (Hugging Face model tree)20 fine-tunes (Hugging Face model tree)9 adapters (Hugging Face model tree)75 quantized20 fine-tunes9 adapters
75 quantized (Hugging Face model tree)20 fine-tunes (Hugging Face model tree)9 adapters (Hugging Face model tree)75 quantized20 fine-tunes9 adapters

All variants →

Reference

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

Benchmarks

18 rows

Benchmarks 18 rows

measured third-party leaderboard row (2)author-reported number from the model card or eval files (16)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
measured BFCL · Call tools and APIs52.15 percent (overall accuracy)Prompt
5 sub-scores
  • Non-Live AST Acc: 90.33
  • Live Acc: 78.68
  • Multi Turn Acc: 44.62
  • Web Search Acc: 50.5
  • Memory Acc: 19.35
leaderboard2026-10-25
measured BFCL · Call tools and APIs47.99 percent (overall accuracy)FC
5 sub-scores
  • Non-Live AST Acc: 37.4
  • Live Acc: 68.91
  • Multi Turn Acc: 45.38
  • Web Search Acc: 54
  • Memory Acc: 23.87
leaderboard2026-10-25
author-reported AIME 2025 · Reason through hard problems70.3 percent—model author2026-10-07
author-reported ARC-AGI · Reason through hard problems41.8 percent—model author2026-10-07
author-reported Aider Polyglot · Write code with agents57.3 percent—model author2026-10-07
author-reported Arena-Hard · Chat and follow instructions79.2 percent—model author2026-10-07
author-reported GPQA Diamond · Reason through hard problems77.5 percent—model author2026-10-07
author-reported HMMT · Reason through hard problems55.4 percent—model author2026-10-07
author-reported IFEval · Chat and follow instructions88.7 percent—model author2026-10-07
author-reported LiveBench · Reason through hard problems75.4 percent—model author2026-10-07
author-reported LiveCodeBench · Write code with agents51.8 percent—model author2026-10-07
author-reported MMLU · Reason through hard problems93.1 percent—model author2026-10-07
author-reported MMLU-Pro · Reason through hard problems83 percent—model author2026-10-07
author-reported MultiPL-E · Write code with agents87.9 percent—model author2026-10-07
author-reported SimpleQA · Reason through hard problems54.3 percent—model author2026-10-07
author-reported SimpleQA · Reason through hard problems84.3 percent—model author2026-10-07
author-reported SuperGPQA · Reason through hard problems62.6 percent—model author2026-10-07
author-reported ZebraLogic95 percent—model author2026-10-07

What the card says

1 cited sentence

Summary generated, cited

The architecture utilizes 128 experts and supports a native context length of 262,144 tokens.[1][2]

  1. num experts
  2. “262,144 natively and extendable up to 1,010,000 tokens”

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

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

24 fields

Listed in LLMs · MoE

Facts

Pipeline tagtext-generation
Librarytransformers
ArchitecturesQwen3MoeForCausalLM
Model typeqwen3_moe
Dtypebfloat16
Params by dtypeBF16: 235,093,634,560
Size bandXXL
MoEyes
Experts128
Experts per token8
Context length262,144 (config.json max_position_embeddings)
PapersarXiv:2402.17463, arXiv:2407.02490, arXiv:2501.15383, arXiv:2404.06654, arXiv:2505.09388
LicenseApache 2.0 (apache-2.0)
Hub snapshot2026-10-25

Servability

vLLM · SGLang

Servability

vLLM: supported · Qwen3MoeForCausalLM · 7050ef881ff0835f6890a47db1859a24c65481d6SGLang: supported · Qwen3MoeForCausalLM · 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 Qwen3-235B-A22B-Instruct-2507: params × bytes/param × 1.2 overhead. KV cache and activations are extra. How this is computed.

BF16 (2 bytes/param)564 GB · Multi-GPU node (<640 GB)
FP8 (1 byte/param)282 GB · Multi-GPU node (<640 GB)
INT4 (0.5 bytes/param)141 GB · Multi-GPU node (<640 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 104 derivatives on the Hub

quantized: 75finetune: 20adapter: 9

Top variants (40 shown, ranked by downloads)

Relation
Quant
Size
40 rows
Variant Relation Quant Params Band License Author Likes Downloads
Qwen/Qwen3-235B-A22B-Instruct-2507-FP8 quantized -FP8 235B XXL apache-2.0 Qwen 161 4.6M
lmstudio-community/Qwen3-235B-A22B-Instruct-2507-MLX-4bit quantized MLX 235B XXL apache-2.0 lmstudio-community 1 277.1k
lmstudio-community/Qwen3-235B-A22B-Instruct-2507-MLX-8bit quantized MLX 235B XXL apache-2.0 lmstudio-community 0 271.9k
lmstudio-community/Qwen3-235B-A22B-Instruct-2507-MLX-6bit quantized MLX 235B XXL apache-2.0 lmstudio-community 0 270.8k
unsloth/Qwen3-235B-A22B-Instruct-2507-GGUF quantized GGUF — — apache-2.0 unsloth 120 261k
QuantTrio/Qwen3-235B-A22B-Instruct-2507-AWQ quantized AWQ 235B XXL apache-2.0 QuantTrio 13 250.4k
mlx-community/Qwen3-235B-A22B-Instruct-2507-4bit quantized -4bit 235B XXL apache-2.0 mlx-community 3 105k
mlx-community/Qwen3-235B-A22B-Instruct-2507-8bit quantized -8bit 235B XXL apache-2.0 mlx-community 2 94.9k
lmstudio-community/Qwen3-235B-A22B-Instruct-2507-GGUF quantized GGUF — — — lmstudio-community 11 48.8k
nvidia/Qwen3-235B-A22B-Instruct-2507-NVFP4 quantized NVFP4 120B XXL apache-2.0 nvidia 13 45.2k
bartowski/Qwen_Qwen3-235B-A22B-Instruct-2507-GGUF quantized GGUF — — — bartowski 9 33.9k
AIDXteam/Qwen3-235B-A22B-Instruct-2507-AWQ quantized AWQ 32.3B L — AIDXteam 0 24.4k
RedHatAI/Qwen3-235B-A22B-Instruct-2507-NVFP4 quantized NVFP4 136B XXL apache-2.0 RedHatAI 4 21.2k
NVFP4/Qwen3-235B-A22B-Instruct-2507-FP4 quantized — 118B XXL apache-2.0 NVFP4 4 19.9k
amd/Qwen3-235B-A22B-Instruct-2507-MXFP4 quantized MXFP4 118B XXL apache-2.0 amd 2 14.7k
INC4AI/Qwen3-235B-A22B-Instruct-2507-gguf-q4km-AutoRound quantized gguf — — apache-2.0 INC4AI 8 8.8k
ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF quantized GGUF — — apache-2.0 ubergarm 17 8k
QuantTrio/Qwen3-235B-A22B-Instruct-2507-GPTQ-Int4-Int8Mix quantized GPTQ 248B XXL apache-2.0 QuantTrio 4 6.1k
DevQuasar/Qwen.Qwen3-235B-A22B-Instruct-2507-GGUF quantized GGUF — — — DevQuasar 2 4.9k
Intel/Qwen3-235B-A22B-Instruct-2507-int4-mixed-AutoRound quantized int4 1.29B S apache-2.0 Intel 12 3.5k
Intel/Qwen3-235B-A22B-Instruct-2507-gguf-q2ks-mixed-AutoRound quantized gguf — — apache-2.0 Intel 15 3.2k
chriswritescode/Qwen3-235B-A22B-Instruct-2507-INT4-W4A16 quantized INT4 32.3B L apache-2.0 chriswritescode 0 3.9k
moxin-org/Qwen3-235B-A22B-Instruct-2507-GGUF quantized GGUF — — apache-2.0 moxin-org 3 3.2k
unsloth/Qwen3-235B-A22B-Instruct-2507 finetune — 235B XXL apache-2.0 unsloth 10 2.3k
John1604/Qwen3-235B-A22B-Instruct-2507-gguf quantized gguf — — apache-2.0 John1604 1 2.2k
manjunathshiva/Qwen3-235B-A22B-Instruct-2507-tq3a-tqTe-g64 quantized — 16.4B L apache-2.0 manjunathshiva 1 1.8k
INCModel/Qwen3-235B-A22B-Instruct-2507-MXFP4-CT-AutoRound quantized MXFP4 8.00B M apache-2.0 INCModel 0 1.7k
sm54/Qwen3-235B-A22B-Instruct-2507-MXFP4_MOE quantized GGUF — — — sm54 9 1.1k
manjunathshiva/Qwen3-235B-A22B-Instruct-2507-tq3a-tq2e-g32 quantized — 22.9B L apache-2.0 manjunathshiva 0 1.3k
nebius/EAGLE3-Qwen3-235B-A22B-Instruct-2507 finetune — 573M S cc-by-4.0 nebius 0 1.3k
mlx-community/Qwen3-235B-A22B-Instruct-2507-3bit quantized — 235B XXL apache-2.0 mlx-community 0 1.3k
wasmdashai/vivo-c-v1 finetune — 235B XXL apache-2.0 wasmdashai 3 843
jasonyux/Qwen3-235B-A22B-Instruct-2507-AWQ quantized AWQ 235B XXL apache-2.0 jasonyux 0 861
manjunathshiva/Qwen3-235B-A22B-Instruct-2507-tq3-g32 quantized — 31.5B L apache-2.0 manjunathshiva 0 825
derek-wh/Qwen3-235B-A22B-Instruct-2507-mlx-2Bit quantized mlx 235B XXL apache-2.0 derek-wh 0 807
mlx-community/Qwen3-235B-A22B-Instruct-2507-5bit quantized — 235B XXL apache-2.0 mlx-community 1 678
pcuenq/Qwen3-235B-A22B-Instruct-2507-mlx-4Bit quantized mlx 235B XXL apache-2.0 pcuenq 1 653
zjiayu064/Qwen3-235B-A22B-Instruct-2507-BitsMoE-1.6bit quantized — 21.5B L apache-2.0 zjiayu064 0 676
bobchenyx/Qwen3-235B-A22B-Instruct-2507-GGUF quantized GGUF — — mit bobchenyx 1 602
locailabs/locai-l1-large finetune — 235B XXL apache-2.0 locailabs 8 211

Repository changelog

18 commits
changed in the last 7 daysNew providers: 2026-10-23Other: 2026-10-24, 2026-10-24all changes

Repository changelog 18 commits

DateCommitSHA
2025-09-17 Update tokenizer_config.json ac9c66c
2025-08-17 Update README.md 97d5e9a
2025-08-08 Add 1M support (#30) 8994a5c
2025-07-30 Update README.md 9fe72e5
2025-07-29 Update README.md 8b0a014
2025-07-21 Update README.md 56e16a6
2025-07-21 Update README.md 61e22c6
2025-07-21 Update README.md 8a39f5b
2025-07-21 Update README.md bbd8c67
2025-07-21 Update README.md 8399681
2025-07-21 Create LICENSE 585555b
2025-07-21 Update tokenizer_config.json 3d2db25
2025-07-21 Update README.md 0f2e333
2025-07-21 Delete configuration.json a46ff03
2025-07-21 Delete .mv eea9174
2025-07-21 Delete .msc 4ee6ddd
2025-07-21 Add files using upload-large-folder tool d4dc8b0
2025-07-21 initial commit a1c9a1b

All providers

7 providers · 7 endpoints

Where to run 7 providers · 7 endpoints

Hosted endpoints observed for Qwen3-235B-A22B-Instruct-2507, 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: $0.09/M.

ProviderModel refContextQuant$/M in$/M outtok/sTTFTToolsStatusObserved
GMICloud
openrouter
qwen/qwen3-235b-a22b-2507 256K fp8 $0.09 $0.35 — — yes live 2026-10-24
DeepInfra
hf, deepinfra, openrouter
Qwen/Qwen3-235B-A22B-Instruct-2507 256K fp8 $0.09 $0.55 35 200 ms yes live 2026-10-25
Parasail
parasail, openrouter
parasail-qwen3-235b-a22b-instruct-2507 128K fp8 $0.14 $0.80 — — yes serverless 2026-10-24
Venice
openrouter
qwen/qwen3-235b-a22b-2507 125K fp8 $0.15 $0.75 — — yes live 2026-10-24
Nscale
hf
Qwen/Qwen3-235B-A22B-Instruct-2507 32K — $0.20 $0.60 16.3 433 ms yes live 2026-10-25
Google
openrouter
qwen/qwen3-235b-a22b-2507 256K unknown $0.22 $0.88 — — yes status -5 2026-10-24
Scaleway
hf
qwen3-235b-a22b-instruct-2507 — — $0.85 $2.56 30.4 1011 ms yes 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.

Official1

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.

Analysis9

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.
simonwillison.netauthority
artificialanalysis.aiauthority
artificialanalysis.aiauthority
benchgen.com
deepinfra.com
llmap.ai
bestllmfor.com
All coverage tracked for this model (18) — append-only, nothing is ever removed
TitleSourcePublishedFirst seenSignals
Qwen/Qwen3-235B-A22B-Instruct-2507simonwillison.net—2026-10-07exa
Qwen3 235B A22B 2507 Models - Intelligence, Performance & Price Comparison | Artificial Analysisartificialanalysis.ai—2026-10-07exa
Qwen3 235B A22B 2507 - Intelligence, Performance & Price ...artificialanalysis.ai—2026-10-07exa
Qwen3-235B-A22B-Instruct-2507: Benchmarks, Pricing & Context Window — Benchgenbenchgen.com—2026-10-07exa
Qwen3 235B A22B Instruct 2507 API - Demo - DeepInfradeepinfra.com—2026-10-07exa
Qwen3 235B A22B Instruct 2507 — specs, hardware fit & pricing | LLMapllmap.ai—2026-10-07exa
Qwen 3 235B-A22B Review: The MoE Flagship Tested | BestLLMforbestllmfor.com—2026-10-07exa
Qwen3 235B A22B Instruct (2507)opper.ai—2026-10-07exa
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-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

5 papers

Papers 5

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

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 15:00 UTC · Last verified 2026-10-25

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