Qwen3.6 Plus

AlibabaKey qwen3.6-plusAlso reported as qwen/qwen3.6-plus, cmc/qwen/qwen3.6-plus, ali/qwen3.6-plus, opencode-go/qwen3.6-plus

Rank
#955
by active users
Active users
1
Down 1 from 2 active users in the previous 30 days. vs previous 30 days
Adoption
0.1%
of 958 accounts
Tokens
5.5K
cache included
Est. cost
<$1
at public list prices
First seen
Apr 3, 2026
in any submission

Oct 14 – Nov 12, 2026 (UTC)958 accounts100% of tokens from an identified modelData through Nov 12, 2026, 03:03 UTCHow we count

Daily active users

Distinct accounts that used this model on each UTC day.

0 active users on Nov 12, 2026

Daily active users for Qwen3.6 Plus, Oct 14, 2026 to Nov 12, 2026. Peak 1. Use the left and right arrow keys to read each day.110
Show daily values
Daily active users and tokens for Qwen3.6 Plus
Date (UTC)Active usersTokens
Oct 14, 202615.5K
Oct 15, 202600
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Nov 1, 202600
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Nov 12, 202600

Token mix

How the 5.5K tokens split. Cache reads are usually most of an agent's tokens and are priced far below input.

  • Input100%
  • Output<0.1%
  • Cache read0%
  • Cache write0%
  • Reasoning0%

Agents that carry Qwen3.6 Plus

Each agent's share of this model's tokens, and how many accounts used the model through it.

  1. Oh My Pi100% of this model's tokens1active users

Benchmarks

The best result for Qwen3.6 Plus in each benchmark Tokscale tracks. Each benchmark has its own unit, run conditions and source, so compare a score only with other models on the same benchmark. Rank is among the models Tokscale matched in that benchmark.

All benchmarks
  • Epoch Capabilities IndexOverall · Epoch AI
    147.3
    Rank #70 of 160 matched modelsNo conditions reportedModel released Mar 31, 2026Source: epoch.ai (opens in a new tab)
  • WebDev ArenaWeb development · LMArena
    1,461
    Rank #61 of 110 matched modelsNo conditions reportedPublished Oct 19, 2026Source: huggingface.co (opens in a new tab)
  • GPQA DiamondReasoning · Epoch AI
    88.4%
    Rank #50 of 151 matched modelsNo conditions reportedRun date Aug 7, 2026Source: epoch.ai (opens in a new tab)

    Near its ceiling: the top score is 96.4%, and Epoch AI estimates the reachable maximum at 97%. Differences between top models are small.

  • FrontierMath Tiers 1–3 (v2)Reasoning · Epoch AI
    38.2%
    Rank #59 of 86 matched modelsNo conditions reportedRun date Aug 30, 2026Source: epoch.ai (opens in a new tab)

    Near its ceiling: the top score is 93.7%, and Epoch AI estimates the reachable maximum at 95%. Differences between top models are small.

    Show 1 more result with other run conditionsHide the other results
    • Listed as qwen3.6-plus_none
      32.3%
      No reasoningRun date Aug 29, 2026Source: epoch.ai (opens in a new tab)
  • SimpleQA VerifiedKnowledge · Epoch AI
    44.1%
    Rank #41 of 80 matched modelsNo conditions reportedRun date Aug 27, 2026Source: epoch.ai (opens in a new tab)
  • SWE-bench VerifiedHistory · Epoch AI
    57.9%
    Rank #29 of 32 matched modelsNo conditions reportedRun date May 14, 2026Source: epoch.ai (opens in a new tab)

    History only. Newer agentic coding benchmarks are on the main board.

Not evaluated: Humanity's Last Exam, Terminal-Bench 4.0, CursorBench, DeepSWE, FrontierSWE, Agent Arena. A missing result is not a score of zero.

Pricing

List prices from models.dev in US dollars per 1M tokens, retrieved Oct 21, 2026. Alibaba is the lab's own API and the reference price for the estimates below.

Price at real agent usageEstimate at list prices

What 1M tokens cost at the reference list price when they split into input, output and cache the way Tokscale users' agents used them. It is not what anyone paid.

At this model's mix
$0.50
Est. per 1M tokens
0% cache reads
At the community mix
$0.08
Est. per 1M tokens
96% cache reads
3:1 input/output blend
$1.13
Per 1M tokens at list prices
No cache

Cache reads were 0% of Qwen3.6 Plus tokens in the last 30 days and 96% of all tokens. The 3:1 blend, (3 × input + output) ÷ 4, is the usual comparison and ignores cache.

Assumptions

  • Alibaba list prices per 1M tokens: input $0.50, output $3, cache read $0.05, cache write $0.63.
  • This model's mix is its own input, output, cache and reasoning tokens in the last 30 days.
  • The community mix is the tokens of every identified model in the last 30 days.
  • This is an estimate at list prices, not what anyone paid.
  • Reasoning tokens are priced as output.
  • Long-context tiers, batch pricing and discounts are not applied.
  • Token mixes come from public Tokscale submissions. Hidden accounts are excluded.

List prices by provider

  • AlibabaReferenceqwen3.6-plus
    $0.50 input per 1M tokens$3 output per 1M tokens$0.05 cache read per 1M tokens$0.63 cache write per 1M tokens1M tokens of context
  • OpenRouterqwen/qwen3.6-plus
    $0.33 input per 1M tokens$1.95 output per 1M tokensCache read not listed$0.41 cache write per 1M tokens1M tokens of context
  • Vercel AI Gatewayalibaba/qwen3.6-plus
    $0.50 input per 1M tokens$3 output per 1M tokens$0.05 cache read per 1M tokens$0.63 cache write per 1M tokens1M tokens of context

Specs

From models.dev (MIT), retrieved Oct 21, 2026, as the lab or the first listed provider reports them. A dash means models.dev does not list the value.

Context window
1M tokens
Max output
66K tokens
Input
Text, Image, Video
Output
Text
Knowledge cutoff
Apr 2025
Release date
Apr 2, 2026
Open weights
No
Reasoning
Yes
Tool calling
Yes

Top public profiles

Public profiles that used Qwen3.6 Plus most in this period, by tokens. Hidden accounts are never listed.

  1. 1candyandy@andredubbs5.5Ktokens<$1est. cost1active days<$1 est. cost · 1 active days

More from Alibaba

Other Alibaba models in the same period, by active users.

  1. Qwen3.8 Flash1.9% adoption18active users
  2. Qwen3.8 Max1.7% adoption16active users
  3. Qwen3.8 Flash Next0.6% adoption6active users
  4. Qwen3.8 27B0.6% adoption6active users
  5. Qwen3 Coder Next0.4% adoption4active users
  6. Qwen 3.8 27B0.3% adoption3active users
All models

Sources and attribution

Each score keeps its own source, unit and run conditions. Tokscale does not combine benchmarks into one score, and a missing score means the model was not evaluated, not that it scored zero.

  • Epoch AIEpoch Capabilities Index, Epoch's own runs and the external results it collects.CC BY 4.0 · Retrieved Oct 21, 2026 · Data through Oct 15, 2026Epoch AI, ‘Capabilities & benchmarking’. Published online at epoch.ai. Retrieved from https://epoch.ai/benchmarks.
  • LMArenaWebDev Arena and Agent Arena leaderboards.CC BY 4.0 · Retrieved Oct 21, 2026 · Data through Oct 19, 2026LMArena leaderboard dataset (lmarena-ai/leaderboard-dataset), Hugging Face.
  • models.devSpecs and list prices per provider.MIT · Retrieved Oct 21, 2026models.dev, an open catalog of AI models (github.com/anomalyco/models.dev).
  • TokscaleActive users, tokens and token mix from public Tokscale submissions. Hidden accounts are excluded.How we count