Qwen3.5 9B

AlibabaKey qwen3.5-9bAlso reported as qwen3.5:9b, qwen/qwen3.5-9b, unsloth/qwen3.5-9b, lmstudio-community/qwen3.5-9b

Rank
#314
by active users
Active users
2
Down 1 from 3 active users in the previous 30 days. vs previous 30 days
Adoption
0.2%
of 960 accounts
Tokens
28.4M
cache included
Est. cost
—
at public list prices
First seen
Mar 2, 2026
in any submission

Oct 14 – Nov 12, 2026 (UTC)960 accounts100% of tokens from an identified modelData through Nov 12, 2026, 04:57 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 Qwen3.5 9B, 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 Qwen3.5 9B
Date (UTC)Active usersTokens
Oct 14, 202600
Oct 15, 202600
Oct 16, 202600
Oct 17, 202600
Oct 18, 202600
Oct 19, 202600
Oct 20, 202600
Oct 21, 202600
Oct 22, 20261329K
Oct 23, 20261336K
Oct 24, 202600
Oct 25, 202611.3M
Oct 26, 202600
Oct 27, 202600
Oct 28, 202600
Oct 29, 202600
Oct 30, 202600
Oct 31, 20261944K
Nov 1, 202600
Nov 2, 202600
Nov 3, 202600
Nov 4, 202600
Nov 5, 20261224
Nov 6, 2026125.5M
Nov 7, 202600
Nov 8, 202600
Nov 9, 202600
Nov 10, 202600
Nov 11, 202600
Nov 12, 202600

Token mix

How the 28.4M tokens split. Cache reads are usually most of an agent's tokens and are priced far below input.

  • Input89%
  • Output2.4%
  • Cache read9.1%
  • Cache write0%
  • Reasoning<0.1%

Agents that carry Qwen3.5 9B

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

  1. LM Studio90% of this model's tokens1active users
  2. OpenCode10% of this model's tokens1active users

Benchmarks

The best result for Qwen3.5 9B 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
    139.3
    Rank #118 of 160 matched modelsNo conditions reportedModel released Feb 24, 2026Source: epoch.ai (opens in a new tab)
  • GPQA DiamondReasoning · Epoch AI
    79.0%
    Rank #87 of 151 matched modelsNo conditions reportedRun date Aug 27, 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.

    Show 1 more result with other run conditionsHide the other results
    • Listed as qwen3.5-9B_none
      78.9%
      No reasoningRun date Aug 27, 2026Source: epoch.ai (opens in a new tab)

Not evaluated: Humanity's Last Exam, FrontierMath Tiers 1–3 (v2), SimpleQA Verified, Terminal-Bench 4.0, CursorBench, DeepSWE, FrontierSWE, Agent Arena, WebDev 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. OpenRouter is the reference price for the estimates below. It is the first provider listed; models.dev lists no price from the lab's own API.

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
$0.10
Est. per 1M tokens
9.1% cache reads
At the community mix
$0.10
Est. per 1M tokens
96% cache reads
3:1 input/output blend
$0.11
Per 1M tokens at list prices
No cache

Cache reads were 9.1% of Qwen3.5 9B 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

  • OpenRouter list prices per 1M tokens: input $0.10, output $0.15.
  • 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.
  • No cache-read price is listed, so cache reads are priced as input.
  • No cache-write price is listed, so cache writes are priced as input.

List prices by provider

  • OpenRouterReferenceqwen/qwen3.5-9b
    $0.10 input per 1M tokens$0.15 output per 1M tokensCache read not listedCache write not listed262K tokens of context
  • Deep InfraQwen/Qwen3.5-9B
    $0.10 input per 1M tokens$0.15 output per 1M tokensCache read not listedCache write not listed262K 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
262K tokens
Max output
66K tokens
Input
Text, Image, Video
Output
Text
Knowledge cutoff
— not listed
Release date
Feb 23, 2026
Open weights
Yes
Reasoning
Yes
Tool calling
Yes

Top public profiles

Public profiles that used Qwen3.5 9B most in this period, by tokens. Hidden accounts are never listed.

  1. 1Minjun Kim (Lapis0875)@Lapis087525.5Mtokens—est. cost2active daysno price · 2 active days
  2. 2Roei Boribor@roeiboribor2.9Mtokens—est. cost4active daysno price · 4 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.
  • 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