Models › Qwen3.6 family

Qwen Apache 2.0 out 6 months ago

36.0B parameters, 3B active (8 of 256 experts per token), images and text in, text out, 256K tokens of context. 12 providers serve it from $0.050 per million input tokens.

Verified 2026-10-25 Rank #15 in LLMs · score 121.8 method v1 Sources 5 Downloads 30d 3.9M · #37 of 619 tracked

Changed this week: 32 new quantized builds on the Hub (918 in all).2026-10-24 · 29 new quantized builds on the Hub (915 in all).2026-10-24

Can I run it?22GB at INT4Estimatefits a 24 GB cardmethodCommunity: Show HN: Qwen3.6-35B-A3B on a 16 GB M1 Pro with SSD-streamed MoE →
What does it cost?$0.050/1M incheapest of 11Darkbloomfp42026-10-24
Can I use it?Apache 2.0Permissive; patent grantlicence file2026-10-24
Do people use it?3.9Mdownloads, 30 days#37 of 619 we track · top 6%+30 likes, downloads +5% this week3,028 likesHugging Face2026-10-24

The ringer: four playful tests

A pelican steals a bicycle from a supermarket

as received● 4/4 on palette
SVG reply: a pelican steals a bicycle from a supermarket
Draw a pelican stealing a bicycle from a supermarket. Reply with one complete SVG document only, no prose and no code fences. Use viewBox="0 0 200 150" and a transparent background. Use only these colours for every fill and stroke: #06e0c0 #ffc298 #cd85f9 #f1f2f2.

Finish the dad joke

as received● 1 line · 6 words

Why did the open-weights model cross the road?

To get to the other fine-tune!

Finish this dad joke. Reply with the punchline only, on one line, without quotes: Why did the open-weights model cross the road?

A parasailer, in ASCII

as received● 9×6 fits 12×32
  / \
 /___\
  | |
   |
   O
   |
  / \
  _~_
 ~ ~ ~
Draw ASCII art of a parasailer over the sea. At most 12 lines and 32 columns. Reply with the art only: no code fences, no explanation.

Animate a bouncing ball

as received✕ 8 frames · 16×16

No playable frames in the reply.

Make a 16x16 pixel sprite animation of a ball bouncing, 8 frames. Reply with JSON only, no code fences: {"palette": {"a": "#rrggbb", ...}, "fps": <number>, "frames": [[16 strings of 16 characters], ...]}. At most 4 palette colours with single-letter keys; "." means transparent; exactly 8 frames.
1 of 4 · swipe for more

The ringer: the same four playful tests for every model, replies shown as received and checked by code. A joke, not a benchmark; never ranked.3 of 4 pass · parasail-qwen3p6-35b-a3b on parasail · run 2026-10-09 · temperature 0 · cost $0.022 · 2026-10 thing: supermarket · all models →

Worth reading

Picked by rule from 37 tracked pieces: the lab's own post, the most shared, a field report, the latest.

25 best of 37 tracked

The 6 picks above, plus these 19. Top is the radar's score: discussion, source authority and the lab's own posts, fading with age.

  1. ArticleQwen3.6-27B: Flagship-Level Coding in a 27B Dense Modelqwen.ai · 2026-04-22 · official
  2. ArticleQwen3.6-Max-Preview: Smarter, Sharper, Still Evolvingqwen.ai · 2026-04-20 · official
  3. ArticleQwen3.6-Plus: Towards real world agentsqwen.ai · 2026-04-02 · official
  4. CodeShow HN: Samosa Chat - Run Qwen3.6-35B-A3B Locally on a 16 GB Macgithub.com · 2026-07-15 · 6 HN pts
  5. ArticleRunning Claude Code Offline on an M3 Pro with Qwen3.6har-ki.github.io · 2026-06-11 · 18 HN pts
  6. PaperKat-Coder-v2.5 Technical Report (Qwen3.6-35B-A3B base for post-training)arxiv.org · 2026-07-24 · 2 HN pts
  7. ArticleQwen3.6-27B: Flagship-Level Coding in a 27B Dense Modelsimonwillison.net · 2026-04-27
  8. ArticleThinkingCap – we fine-tuned Qwen3.6-27B to reduce unnecessary reasoningbottlecapai.com · 2026-08-25
  9. CodeQwen3.6-35B-A3B speculative decoding is net-negative on RTX 3090github.com · 2026-04-21 · 5 HN pts
  10. CodeShow HN: Modernizing my old PhD work in an evening with little Qwen3.6 MoEgithub.com · 2026-05-21
  11. CodeGB10 Solution Atlas is now open source, <2min cold start 100 tok/s Qwen3.6-FP8github.com · 2026-05-06
  12. CodeMessy Model Bench Tests; Qwen3.6-27B vs. Coder-Nextgithub.com · 2026-05-03
  13. ArticleShow HN: Qwen3.6 27B on a DGX Spark writing code that shipsdevashish.me · 2026-07-07
  14. ArticleQwen3.6 27B (Reasoning) vs Qwen3.6 35B A3B (Reasoning): Model Comparisonartificialanalysis.ai · undated
  15. ArticleLocal LLMs on a Ryzen 8700G iGPU: 13-15 tok/s on gemma4, 9-12 on qwen3.6loomcycle.dev · 2026-06-30
  16. DiscussionNorm-preserving abliteration on Qwen3.6-35B-A3B with 0% refusalreddit.com · 2026-07-03
  17. CodeShow HN: Qwen Lens Studio – multimodal app on Qwen3.6-35B-A3B, runs on Ollamagithub.com · 2026-04-22
  18. CodeLocally Uncensored v2.3.3 – local AI with Qwen3.6 and remote accessgithub.com · 2026-04-16
  19. DiscussionShow HN: Open Access Qwen3.6-35B-A3B-UD-Q5_K_M with TurboQuantnews.ycombinator.com · 2026-04-17

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 6×

11 providers with a live price, from $0.050 to $0.28 per million input tokens; the median is $0.10. One dot per provider, cheapest in teal.

$0.010$0.10$1.00$10.00Darkbloom: $0.050 in / $0.70 out per 1M · observed 2026-10-24 · https://openrouter.ai/qwen/qwen3.6-35b-a3bDeepInfra: $0.10 in / $0.95 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/Qwen/Qwen3.6-35B-A3B?expand[]=inferenceProviderMappingParasail: $0.10 in / $0.95 out per 1M · observed 2026-10-24 · https://docs.parasail.io/parasail-docs/billing/pricingAkashML: $0.10 in / $0.90 out per 1M · observed 2026-10-24 · https://openrouter.ai/qwen/qwen3.6-35b-a3bDekaLLM: $0.10 in / $1.00 out per 1M · observed 2026-10-24 · https://openrouter.ai/qwen/qwen3.6-35b-a3bVenice: $0.10 in / $1.00 out per 1M · observed 2026-10-24 · https://openrouter.ai/qwen/qwen3.6-35b-a3bPhala: $0.20 in / $1.27 out per 1M · observed 2026-10-24 · https://openrouter.ai/qwen/qwen3.6-35b-a3bSiliconFlow: $0.24 in / $1.80 out per 1M · observed 2026-10-24 · https://openrouter.ai/qwen/qwen3.6-35b-a3bCoreWeave: $0.25 in / $1.25 out per 1M · observed 2026-10-24 · https://openrouter.ai/qwen/qwen3.6-35b-a3bAtlasCloud: $0.25 in / $1.25 out per 1M · observed 2026-10-24 · https://openrouter.ai/qwen/qwen3.6-35b-a3bScaleway: $0.28 in / $1.71 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/Qwen/Qwen3.6-35B-A3B?expand[]=inferenceProviderMappingcheapest $0.050median $0.10
$0.010$0.10$1.00$10.00Darkbloom: $0.050 in / $0.70 out per 1M · observed 2026-10-24 · https://openrouter.ai/qwen/qwen3.6-35b-a3bDeepInfra: $0.10 in / $0.95 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/Qwen/Qwen3.6-35B-A3B?expand[]=inferenceProviderMappingParasail: $0.10 in / $0.95 out per 1M · observed 2026-10-24 · https://docs.parasail.io/parasail-docs/billing/pricingAkashML: $0.10 in / $0.90 out per 1M · observed 2026-10-24 · https://openrouter.ai/qwen/qwen3.6-35b-a3bDekaLLM: $0.10 in / $1.00 out per 1M · observed 2026-10-24 · https://openrouter.ai/qwen/qwen3.6-35b-a3bVenice: $0.10 in / $1.00 out per 1M · observed 2026-10-24 · https://openrouter.ai/qwen/qwen3.6-35b-a3bPhala: $0.20 in / $1.27 out per 1M · observed 2026-10-24 · https://openrouter.ai/qwen/qwen3.6-35b-a3bSiliconFlow: $0.24 in / $1.80 out per 1M · observed 2026-10-24 · https://openrouter.ai/qwen/qwen3.6-35b-a3bCoreWeave: $0.25 in / $1.25 out per 1M · observed 2026-10-24 · https://openrouter.ai/qwen/qwen3.6-35b-a3bAtlasCloud: $0.25 in / $1.25 out per 1M · observed 2026-10-24 · https://openrouter.ai/qwen/qwen3.6-35b-a3bScaleway: $0.28 in / $1.71 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/Qwen/Qwen3.6-35B-A3B?expand[]=inferenceProviderMappingcheapest $0.050 · median $0.10 (white tick)
#Cheapest 5In /1MOut /1MContextQuantSeen
1 Darkbloom $0.050 $0.70 256K fp4 Oct 24
2 AkashML $0.10 $0.90 256K fp8 Oct 24
3 DeepInfra $0.10 $0.95 256K fp8 Oct 25
4 Parasail $0.10 $0.95 256K fp8 Oct 24
5 DekaLLM $0.10 $1.00 256K unknown Oct 24

All 12 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 (12 API providers); heavily run locally (918 quantized builds); a big base for builders (790 fine-tunes, adapters and merges).

  • As a service12AkashML, AtlasCloud, CoreWeave, Darkbloom +8
  • On own hardware918quantized builds
  • Built on790395 fine-tunes · 357 adapters · 38 merges
  • In apps84Hugging Face Spaces

Top Spaces running it: embedl/hfviewer ♥ 181 · KyleHessling1/qwopus36-35b-a3b-eval ♥ 12 · emanforhelp/model-pulse ♥ 5 · curtburk/vLLM_on_NVIDIA_GB10 ♥ 4

Will it fit?

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

Fits a 64 GB Mac (unified memory) at INT4

About 22 GB at INT4, 43 GB at FP8, 86 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 22 GB of weights (estimate; KV cache and activations extra)INT422 GBFP8: about 43 GB of weights (estimate; KV cache and activations extra)FP843 GBBF16: about 86 GB of weights (estimate; KV cache and activations extra)BF1686 GB
16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 22 GB of weights (estimate; KV cache and activations extra)INT422 GBFP8: about 43 GB of weights (estimate; KV cache and activations extra)FP843 GBBF16: about 86 GB of weights (estimate; KV cache and activations extra)BF1686 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-qwen3p6-35b-a3b",
      "source_url": "https://api.parasail.io/v1/chat/completions",
      "ttft_ms_median": 13700,
      "ttft_any_ms_median": 992,
      "tokens_per_s_median": 114.4,
      "runs": [
        {
          "ttft_s": null,
          "ttft_any_s": 0.992,
          "tokens_per_s": 114.4,
          "latency_s": 13.11,
          "tokens_out": 1500,
          "reasoning_chars": 6374,
          "finish": "length"
        },
        {
          "ttft_s": 12.293,
          "ttft_any_s": 0.849,
          "tokens_per_s": 119.9,
          "latency_s": 12.51,
          "tokens_out": 1500,
          "reasoning_chars": 7042,
          "finish": "length"
        },
        {
          "ttft_s": 15.106,
          "ttft_any_s": 1.098,
          "tokens_per_s": 92.1,
          "latency_s": 16.29,
          "tokens_out": 1500,
          "reasoning_chars": 6902,
          "finish": "length"
        }
      ]
    }
  ],
  "note": "Single client, wall clock; tok/s counts every output token including hidden reasoning."
}
median TTFT 13,700 ms · 114.4 tok/s · 2026-10-09
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": "The weather in Lisbon on Wednesday, October 14, 2026, is clear with a very low chance of rain. I found a free evening slot in your calendar from 19:30 to 21:30 and successfully booked a table for 2 at Cervejaria Ramiro at 19:30.\n\nYour confirmation code is: **TBL-4471**",
  "fixture": "receipts.errand.v1"
}
calls in order · args 3/3 · confirmed · 2026-10-099.0 s

The story so far

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

10 dated articles and 3 repo commits so far

Teal is the weights (2026-04-15), amber triangles are 10 dated articles (each links to it), grey ticks are repo commits.

What people built on it

1,708 derivatives on the Hub. Which build to run, by runtime: the most downloaded in each class.

1,708 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.

918 quantized (Hugging Face model tree)395 fine-tunes (Hugging Face model tree)38 merges (Hugging Face model tree)357 adapters (Hugging Face model tree)918 quantized395 fine-tunes38 merges357 adapters
918 quantized (Hugging Face model tree)395 fine-tunes (Hugging Face model tree)38 merges (Hugging Face model tree)357 adapters (Hugging Face model tree)918 quantized395 fine-tunes38 merges357 adapters

All variants →

Reference

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

Benchmarks

9 rows

Benchmarks 9 rows

measured third-party leaderboard rowauthor-reported number from the model card or eval files (9)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 NL2Repo · Write code with agents29.4—model author2026-10-07
author-reported QwenClawBench52.6—model author2026-10-07
author-reported QwenWebBench1397—model author2026-10-07
author-reported SWE-bench Multilingual · Write code with agents67.2—model author2026-10-07
author-reported SWE-bench Pro · Write code with agents49.5—model author2026-10-07
author-reported SWE-bench Verified · Write code with agents73.4—model author2026-10-07
author-reported TAU3-Bench67.2—model author2026-10-07
author-reported Terminal-Bench · Write code with agents51.5—model author2026-10-07
author-reported VITA-Bench35.6—model author2026-10-07

Use-case evidence

6 use cases

What it is good at 6 use cases

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

Facts

23 fields

Listed in LLMs · MoE

Facts

Pipeline tagimage-text-to-text
Librarytransformers
ArchitecturesQwen3_5MoeForConditionalGeneration
Model typeqwen3_5_moe
Dtypebfloat16
Params by dtypeBF16: 35,951,822,704
Size bandXL
MoEyes
Experts256
Experts per token8
Context length262,144 (config.json max_position_embeddings)
LicenseApache 2.0 (apache-2.0)
Hub snapshot2026-10-25

Servability

vLLM · SGLang

Servability

vLLM: supported · Qwen3_5MoeForConditionalGeneration · 7050ef881ff0835f6890a47db1859a24c65481d6SGLang: supported · Qwen3_5MoeForConditionalGeneration · 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 Qwen3.6-35B-A3B: params × bytes/param × 1.2 overhead. KV cache and activations are extra. How this is computed.

BF16 (2 bytes/param)86 GB · Multi-GPU node (<640 GB)
FP8 (1 byte/param)43 GB · Single high-end GPU (<80 GB)
INT4 (0.5 bytes/param)22 GB · Single GPU (<24 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/Qwen3.6 1,708 derivatives on the Hub

quantized: 918finetune: 395adapter: 357merge: 38

Top variants (40 shown, ranked by downloads)

Relation
Quant
Size
40 rows
Variant Relation Quant Params Band License Author Likes Downloads
Qwen/Qwen3.6-35B-A3B-FP8 quantized -FP8 36.0B XL apache-2.0 Qwen 438 47.2M
nvidia/Qwen3.6-35B-A3B-NVFP4 quantized NVFP4 18.7B L apache-2.0 nvidia 670 39.6M
HauhauCS/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive quantized GGUF — — apache-2.0 HauhauCS 4k 12.4M
RedHatAI/Qwen3.6-35B-A3B-NVFP4 quantized NVFP4 34.7B L apache-2.0 RedHatAI 193 11.5M
unsloth/Qwen3.6-35B-A3B-GGUF quantized GGUF — — apache-2.0 unsloth 1.7k 9.7M
cyankiwi/Qwen3.6-35B-A3B-AWQ-4bit quantized AWQ 36.0B XL apache-2.0 cyankiwi 96 6.2M
unsloth/Qwen3.6-35B-A3B-NVFP4 quantized NVFP4 23.0B L apache-2.0 unsloth 123 5.7M
unsloth/Qwen3.6-35B-A3B-MTP-GGUF quantized GGUF — — apache-2.0 unsloth 1k 5M
QuantTrio/Qwen3.6-35B-A3B-AWQ quantized AWQ 36.0B XL apache-2.0 QuantTrio 37 4.9M
Bahushruth/Qwen3.6-35B-A3B-abliterated-v4 finetune — 34.7B L apache-2.0 Bahushruth 13 3M
lmstudio-community/Qwen3.6-35B-A3B-MLX-4bit quantized MLX 35.1B XL apache-2.0 lmstudio-community 17 2.2M
lmstudio-community/Qwen3.6-35B-A3B-GGUF quantized GGUF — — apache-2.0 lmstudio-community 33 2.1M
lmstudio-community/Qwen3.6-35B-A3B-MLX-8bit quantized MLX 35.1B XL apache-2.0 lmstudio-community 5 2.1M
lmstudio-community/Qwen3.6-35B-A3B-MLX-6bit quantized MLX 35.1B XL apache-2.0 lmstudio-community 4 2M
unsloth/Qwen3.6-35B-A3B-NVFP4-Fast quantized NVFP4 19.8B L apache-2.0 unsloth 129 2M
palmfuture/Qwen3.6-35B-A3B-GPTQ-Int4 quantized GPTQ 36.0B XL apache-2.0 palmfuture 31 1.8M
bartowski/Qwen_Qwen3.6-35B-A3B-GGUF quantized GGUF — — apache-2.0 bartowski 167 1.5M
michaelw9999/Qwen3.6-35B-A3B-NVFP4-MTP-GGUF quantized NVFP4 — — — michaelw9999 11 1.1M
mudler/Qwen3.6-35B-A3B-APEX-GGUF quantized GGUF — — apache-2.0 mudler 205 926.1k
z-lab/Qwen3.6-35B-A3B-DFlash finetune — 386M S apache-2.0 z-lab 295 867.6k
ykarout/Qwen3.6-35B-A3B-NVFP4 quantized NVFP4 36.0B XL apache-2.0 ykarout 1 774.6k
ggml-org/Qwen3.6-35B-A3B-GGUF quantized GGUF — — apache-2.0 ggml-org 28 732.9k
huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated-MTP-GGUF quantized GGUF — — apache-2.0 huihui-ai 57 708.8k
Intel/Qwen3.6-35B-A3B-int4-mixed-AutoRound quantized int4 6.33B M — Intel 13 553.2k
mlx-community/Qwen3.6-35B-A3B-4bit quantized -4bit 35.1B XL apache-2.0 mlx-community 118 417.5k
rdtand/Qwen3.6-35B-A3B-PrismaQuant-4.75bit-vllm quantized — 21.1B L apache-2.0 rdtand 35 421.2k
Ex0bit/Qwen3.6-35B-A3B-PRISM-NVFP4 quantized NVFP4 — — apache-2.0 Ex0bit 22 360.7k
unsloth/Qwen3.6-35B-A3B-UD-MLX-4bit quantized MLX 35.1B XL apache-2.0 unsloth 124 327.6k
flywheel-ai/automotive quantized GGUF 34.7B L apache-2.0 flywheel-ai 0 324.1k
unsloth/Qwen3.6-35B-A3B finetune — 36.0B XL apache-2.0 unsloth 36 281.8k
mlx-community/Qwen3.6-35B-A3B-8bit quantized -8bit 35.1B XL apache-2.0 mlx-community 33 268.3k
lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled adapter — 36.0B XL apache-2.0 lordx64 204 234.1k
mudler/Qwen3.6-35B-A3B-APEX-MTP-GGUF quantized GGUF — — apache-2.0 mudler 76 206.1k
sakamakismile/Qwen3.6-35B-A3B-NVFP4 quantized NVFP4 — — apache-2.0 sakamakismile 14 205.3k
Edge0/Edge0-35B-A3B-preview adapter — 34.7B L apache-2.0 Edge0 1.1k 84.2k
selode-ai/Qwen-3.6-35B-A3B-VRAP-4-bit-AWQ-21.2GB quantized AWQ 28.7B L apache-2.0 selode-ai 17 136.2k
lokeshe09/Qwen3.6-35B-A3B-INT8 quantized INT8 34.7B L apache-2.0 lokeshe09 0 110.6k
prithivMLmods/Qwen3.6-35B-A3B-MTP-GGUF quantized GGUF — — apache-2.0 prithivMLmods 3 106.9k
huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated finetune — 36.0B XL apache-2.0 huihui-ai 101 88.4k
llmfan46/Qwen3.6-35B-A3B-uncensored-heretic-Native-MTP-Preserved finetune — 35.1B XL apache-2.0 llmfan46 47 90.8k

Repository changelog

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

Repository changelog 3 commits

DateCommitSHA
2026-04-24 Upload README.md with huggingface_hub 995ad96
2026-04-22 Upload LICENSE 53c4317
2026-04-15 Initial public release 7da1103

All providers

12 providers · 12 endpoints

Where to run 12 providers · 12 endpoints

Hosted endpoints observed for Qwen3.6-35B-A3B, 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: $0.05/M.

ProviderModel refContextQuant$/M in$/M outtok/sTTFTToolsStatusObserved
Darkbloom
openrouter
qwen/qwen3.6-35b-a3b 256K fp4 $0.05 $0.70 — — yes live 2026-10-24
DeepInfra
hf, deepinfra, openrouter
Qwen/Qwen3.6-35B-A3B 256K fp8 $0.10 $0.95 60.1 265 ms yes live 2026-10-25
Parasail
parasail, openrouter
parasail-qwen3p6-35b-a3b 256K fp8 $0.10 $0.95 — — yes serverless 2026-10-24
AkashML
openrouter
qwen/qwen3.6-35b-a3b 256K fp8 $0.10 $0.90 — — yes live 2026-10-24
DekaLLM
openrouter
qwen/qwen3.6-35b-a3b 256K unknown $0.10 $1.00 — — yes live 2026-10-24
Venice
openrouter
qwen/qwen3.6-35b-a3b 250K fp8 $0.10 $1.00 — — yes live 2026-10-24
Phala
openrouter
qwen/qwen3.6-35b-a3b 256K unknown $0.20 $1.27 — — yes live 2026-10-24
SiliconFlow
openrouter
qwen/qwen3.6-35b-a3b 256K fp8 $0.24 $1.80 — — — live 2026-10-24
CoreWeave
openrouter
qwen/qwen3.6-35b-a3b 256K fp8 $0.25 $1.25 — — — live 2026-10-24
AtlasCloud
openrouter
qwen/qwen3.6-35b-a3b 256K fp8 $0.25 $1.25 — — yes live 2026-10-24
Scaleway
hf
qwen3.6-35b-a3b — — $0.28 $1.71 105.7 547 ms yes live 2026-10-25
Featherless
hf
Qwen/Qwen3.6-35B-A3B — — — — — — — live 2026-10-25

Articles and coverage

37 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

qwen.ai2026-04-16officialauthorityHN front pageHN 1,274 pts · 532 comments2,176 words
Qwen3.6-35B-A3B is a sparse mixture-of-experts model with 35 billion total parameters and only 3 billion active parameters. The model achieves a SWE-bench Verified score of 73.4, surpassing its predecessor Qwen3.5-35B-A3B which scored 70.0 on the same benchmark.generated, cited

Analysis9

simonwillison.net2026-04-16authorityHN front pageHN 463 pts · 97 comments442 words
Qwen3.6-35B-A3B running on a laptop generated a better pelican illustration than Claude Opus 4.7. The author tested the 20.9GB Qwen3.6-35B-A3B-UD-Q4_K_S.gguf quantized model on a MacBook Pro M5 via LM Studio against Anthropic's release. A 21GB quantized version of the local model was deemed a better bet for SVG illustrations than the proprietary service.written, reviewed, cited
github.com2026-07-17authorityHN 24 pts · 4 comments1,404 words
github.com2026-07-15authorityHN 6 pts · 9 comments1,571 words
github.com2026-10-03authorityHN 2 pts · 0 comments1,364 words
har-ki.github.io2026-06-11about the Qwen3.6 familyHN 18 pts · 10 comments2,250 words
github.com2026-09-13authorityHN 2 pts · 0 comments688 words
arxiv.org2026-07-24authorityHN 2 pts · 0 comments
github.com2026-07-23authorityHN 2 pts · 0 comments
github.com2026-04-21authorityHN 5 pts · 2 comments
All coverage tracked for this model (37) — append-only, nothing is ever removed
TitleSourcePublishedFirst seenSignals
Qwen3.6 27B (Reasoning) vs Qwen3.6 35B A3B (Reasoning): Model Comparisonartificialanalysis.ai—2026-10-07exa
Qwen3.6-35B-A3B - QwenCloudqwencloud.com—2026-10-07exa
Qwen: Qwen3.6 35B A3B Review | Pricing, Benchmarks & Capabilities (2026) | Design for Onlinedesignforonline.com—2026-10-07exa
Gufo-Qwen3.6-35B-A3B-Q6dense – 3095tok/s prefill; 190 tok/s decode on Strix Halogithub.com2026-10-032026-10-07hn
Iris-mini – open search agent on Qwen3.6-35B-A3Bgithub.com2026-09-132026-10-07hn
ThinkingCap – we fine-tuned Qwen3.6-27B to reduce unnecessary reasoningbottlecapai.com2026-08-252026-10-07hn
Kat-Coder-v2.5 Technical Report (Qwen3.6-35B-A3B base for post-training)arxiv.org2026-07-242026-10-07hn
NInfer: 271 tok/s (543 with speculative) for Qwen3.6-35B-A3B on one RTX 5090github.com2026-07-232026-10-07hn
Show HN: Qwen3.6-35B-A3B on a 16 GB M1 Pro with SSD-streamed MoEgithub.com2026-07-172026-10-07hn
Show HN: Samosa Chat - Run Qwen3.6-35B-A3B Locally on a 16 GB Macgithub.com2026-07-152026-10-07hn
Show HN: Qwen3.6 27B on a DGX Spark writing code that shipsdevashish.me2026-07-072026-10-07hn
Norm-preserving abliteration on Qwen3.6-35B-A3B with 0% refusalreddit.com2026-07-032026-10-07hn
Local LLMs on a Ryzen 8700G iGPU: 13-15 tok/s on gemma4, 9-12 on qwen3.6loomcycle.dev2026-06-302026-10-07hn
Qwen3.6-35B-A3B: Community Reviews, Uncensored Variants, and MTP Benchmarksblog.hakhub.net2026-06-292026-10-07exa
From Qwen3-32B to Qwen3.6-35B-A3B: Upgrading a Local Inference Stack on 2× RTX 5060 Ti | Tom Seidelremus-software.org2026-06-132026-10-07exa
Running Claude Code Offline on an M3 Pro with Qwen3.6har-ki.github.io2026-06-112026-10-07hn
A Comprehensive Analysis of the Open-Weight Frontier (May 2026)deepresearch.ninja2026-05-282026-10-07hn
Show HN: Modernizing my old PhD work in an evening with little Qwen3.6 MoEgithub.com2026-05-212026-10-07hn
Qwen3.6 35B A3B Review: Strong Specs, Real Deployment Gapsnowadais.com2026-05-142026-10-07exa
GB10 Solution Atlas is now open source, <2min cold start 100 tok/s Qwen3.6-FP8github.com2026-05-062026-10-07hn
Local Harness Benchmark: Pi Coding Agent vs. OpenCode with Qwen3.6 35B A3Bgrigio.org2026-05-042026-10-07hn
Messy Model Bench Tests; Qwen3.6-27B vs. Coder-Nextgithub.com2026-05-032026-10-07hn
Qwen3.6-27B: Flagship-Level Coding in a 27B Dense Modelsimonwillison.net2026-04-272026-10-07hn
Show HN: Qwen Lens Studio – multimodal app on Qwen3.6-35B-A3B, runs on Ollamagithub.com2026-04-222026-10-07hn
Qwen3.6-27B: Flagship-Level Coding in a 27B Dense Modelqwen.ai official2026-04-222026-10-07hn
Qwen3.6-35B-A3B speculative decoding is net-negative on RTX 3090github.com2026-04-212026-10-07hn
Qwen3.6-Max-Preview: Smarter, Sharper, Still Evolvingqwen.ai official2026-04-202026-10-07hn
Can I code on my PC? Gemma4 26B A4B vs. Qwen3.6 35B A3B Coding benchmarkgrigio.org2026-04-202026-10-07hn
Qwen3.6-35B-A3B: What the Numbers Actually Show | AI | Tobias Weisstobias-weiss.org2026-04-182026-10-07exa
Show HN: Open Access Qwen3.6-35B-A3B-UD-Q5_K_M with TurboQuantnews.ycombinator.com2026-04-172026-10-07hn
Qwen3.6 35B A3B is THE ONE The Local LLM Champ on OpenCode benchmark dashboard [video]youtube.com2026-04-172026-10-07hn
Qwen3.6-35B-A3B draws a better pelican than Opus 4.7twitter.com2026-04-162026-10-07hn
Qwen3.6-35B-A3B on my laptop drew me a better pelican than Claude Opus 4.7simonwillison.net2026-04-162026-10-07hn
Locally Uncensored v2.3.3 – local AI with Qwen3.6 and remote accessgithub.com2026-04-162026-10-07hn
Qwen3.6-35B-A3B:Now Open-Sourcetwitter.com2026-04-162026-10-07hn
Qwen3.6-35B-A3B: Agentic coding power, now open to allqwen.ai official2026-04-162026-10-07hn
Qwen3.6-Plus: Towards real world agentsqwen.ai official2026-04-022026-10-07hn

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  • Qwen3.5-35B-A3B · 36.0B
    same size band (XL), also MoE, same task (image-text-to-text), fewer providers, permissive license
  • Qwen3-VL-30B-A3B-Instruct · 31.1B
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  • Qwen3.5-35B-A3B-Base · 36.0B
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Sources

5 sources

Sources

Hub snapshot: 2026-10-25 · HF API: 2026-10-24 21:31 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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