Qwen3.7 Plus

AlibabaKey qwen3.7-plusAlso reported as cline-pass/qwen3.7-plus, qwen/qwen3.7-plus, alibaba/qwen3.7-plus, opencode-go-messages/qwen3.7-plus, ali/qwen3.7-plus

Rank: #321; by active users
Active users: 1; No change from 1 active users in the previous 7 days. vs previous 7 days
Adoption: 0.2%; of 598 accounts
Tokens: 92.1K; cache included
Est. cost: —; at public list prices
First seen: Jun 2, 2026; in any submission

Nov 7–13, 2026 (UTC)598 accounts100% of tokens from an identified modelData through Nov 13, 2026, 01:19 UTCHow we count

Daily active users

Distinct accounts that used this model on each UTC day.

0 active users on Nov 13, 2026

Daily active users for Qwen3.7 Plus, Nov 7, 2026 to Nov 13, 2026. Peak 1. Use the left and right arrow keys to read each day.110

Show daily values

Daily active users and tokens for Qwen3.7 Plus
Date (UTC)	Active users	Tokens
Nov 7, 2026	0	0
Nov 8, 2026	0	0
Nov 9, 2026	0	0
Nov 10, 2026	0	0
Nov 11, 2026	1	92.1K
Nov 12, 2026	0	0
Nov 13, 2026	0	0

Token mix

How the 92.1K tokens split. Cache reads are usually most of an agent's tokens and are priced far below input.

Input72%
Output1.2%
Cache read27%
Cache write0%
Reasoning0%

Agents that carry Qwen3.7 Plus

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

Oh My Pi100% of this model's tokens1active users

Benchmarks

The best result for Qwen3.7 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
147.9
Epoch Capabilities Index: 147.9
Model
Qwen3.7 Plus
Listed as
Qwen3.7-Plus
Conditions
Not reported
Interval
145.8–150.0
Unit
ECI
Model released
Jun 2, 2026
Retrieved
Nov 12, 2026
Source · Epoch AI, ‘Capabilities & benchmarking’, epoch.ai/benchmarks (CC BY 4.0)
Rank #75 of 158 matched modelsNo conditions reportedModel released Jun 2, 2026Source: epoch.ai (opens in a new tab)
Agent ArenaAgents · LMArena
−0.07
Agent Arena: −0.07
Model
Qwen3.7 Plus
Listed as
Qwen3.7-Plus
Conditions
Arena Agent Mode
Interval
-0.08 to -0.05
Unit
net improvement
Published
Oct 19, 2026
Retrieved
Oct 21, 2026
Source · LMArena leaderboard dataset (lmarena-ai/leaderboard-dataset on Hugging Face), CC BY 4.0
Rank #43 of 48 matched modelsArena Agent ModePublished Oct 19, 2026Source: huggingface.co (opens in a new tab)
GPQA DiamondReasoning · Epoch AI
87.9%
GPQA Diamond: 87.9%
Model
Qwen3.7 Plus
Listed as
qwen3.7-plus
Conditions
Not reported
Unit
% correct
Run date
Aug 7, 2026
Retrieved
Nov 12, 2026
Best of 2 reported results for this model.
Source · Epoch AI, ‘Capabilities & benchmarking’, epoch.ai/benchmarks (CC BY 4.0)
Rank #59 of 160 matched modelsNo conditions reportedRun date Aug 7, 2026Source: epoch.ai (opens in a new tab)
Near its ceiling: the top score is 96.5%, 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 qwen3.7-plus_none
  81.8%
  GPQA Diamond: 81.8%
  Model
  Qwen3.7 Plus
  Listed as
  qwen3.7-plus_none
  Conditions
  No reasoning
  Unit
  % correct
  Run date
  Aug 7, 2026
  Retrieved
  Nov 12, 2026
  Source · Epoch AI, ‘Capabilities & benchmarking’, epoch.ai/benchmarks (CC BY 4.0)
  No reasoningRun date Aug 7, 2026Source: epoch.ai (opens in a new tab)
FrontierMath Tiers 1–3 (v2)Reasoning · Epoch AI
34.4%
FrontierMath Tiers 1–3 (v2): 34.4%
Model
Qwen3.7 Plus
Listed as
qwen3.7-plus_none
Conditions
No reasoning
Unit
% solved
Run date
Aug 29, 2026
Retrieved
Nov 12, 2026
Source · Epoch AI, ‘Capabilities & benchmarking’, epoch.ai/benchmarks (CC BY 4.0)
Rank #67 of 90 matched modelsNo reasoningRun date Aug 29, 2026Source: epoch.ai (opens in a new tab)
Near its ceiling: the top score is 95.1%, and Epoch AI estimates the reachable maximum at 95%. Differences between top models are small.

Pricing

List prices from models.dev in US dollars per 1M tokens, retrieved Nov 12, 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: $0.33; Est. per 1M tokens
27% cache reads
At the community mix: $0.10; Est. per 1M tokens
96% cache reads
3:1 input/output blend: $0.70; Per 1M tokens at list prices
No cache

Cache reads were 27% of Qwen3.7 Plus tokens in the last 7 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 $0.40, output $1.60, cache read $0.08, cache write $0.50.
This model's mix is its own input, output, cache and reasoning tokens in the last 7 days.
The community mix is the tokens of every identified model in the last 7 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

AlibabaReferenceqwen3.7-plus
$0.40 input per 1M tokens$1.60 output per 1M tokens$0.08 cache read per 1M tokens$0.50 cache write per 1M tokens1M tokens of context
OpenRouterqwen/qwen3.7-plus
$0.32 input per 1M tokens$1.28 output per 1M tokens$0.06 cache read per 1M tokens$0.40 cache write per 1M tokens1M tokens of context
Vercel AI Gatewayalibaba/qwen3.7-plus
$0.40 input per 1M tokens$1.60 output per 1M tokens$0.08 cache read per 1M tokens$0.50 cache write per 1M tokens1M tokens of context

Specs

From models.dev (MIT), retrieved Nov 12, 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: 131K tokens
Input: Text, Image, Video
Output: Text
Knowledge cutoff: Apr 2025
Release date: Jun 2, 2026
Open weights: No
Reasoning: Yes
Tool calling: Yes

Top public profiles

Public profiles that used Qwen3.7 Plus most in this period, by tokens. Hidden accounts are never listed.

1Brad Lishman@bradlishman92.1Ktokens—est. cost1active daysno price · 1 active days

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

Qwen3.8 Flash0.8% adoption5active users
Qwen3.8 27B0.7% adoption4active users
Qwen3.8 Max0.7% adoption4active users
Qwen3.5 4B0.3% adoption2active users
Qwen4 Max Preview0.3% adoption2active users
Qwen3.5 9B0.2% adoption1active 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 Nov 12, 2026 · Data through Nov 9, 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 Nov 12, 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