Gemini 3.7 Flash

GoogleKey gemini-3.7-flashAlso reported as google-antigravity/gemini-3.7-flash, google/gemini-3.7-flash, hr-gl/gemini-3.7-flash, gemini/gemini-3.7-flash, gh/gemini-3.7-flash, models/gemini-3.7-flash, google-vertex-ai/google/gemini-3.7-flash

Rank
#207
by active users
Active users
4
Down 38 from 42 active users in the previous 30 days. vs previous 30 days
Adoption
0.4%
of 958 accounts
Tokens
59M
cache included
Est. cost
$6
at public list prices
First seen
Jul 29, 2026
in any submission

Oct 14 – Nov 12, 2026 (UTC)958 accounts100% of tokens from an identified modelData through Nov 12, 2026, 01:30 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 Gemini 3.7 Flash, Oct 14, 2026 to Nov 12, 2026. Peak 2. Use the left and right arrow keys to read each day.210
Show daily values
Daily active users and tokens for Gemini 3.7 Flash
Date (UTC)Active usersTokens
Oct 14, 202621.2M
Oct 15, 20261267K
Oct 16, 20261371K
Oct 17, 20261900K
Oct 18, 20261483K
Oct 19, 20261398K
Oct 20, 20261119K
Oct 21, 202600
Oct 22, 202600
Oct 23, 202600
Oct 24, 202614.8M
Oct 25, 202612.5M
Oct 26, 202600
Oct 27, 2026119.5M
Oct 28, 202600
Oct 29, 202600
Oct 30, 2026123.5M
Oct 31, 202614.9M
Nov 1, 202600
Nov 2, 202600
Nov 3, 202600
Nov 4, 202600
Nov 5, 202600
Nov 6, 202600
Nov 7, 202600
Nov 8, 202600
Nov 9, 202600
Nov 10, 202600
Nov 11, 202600
Nov 12, 202600

Token mix

How the 59M tokens split. Cache reads are usually most of an agent's tokens and are priced far below input.

  • Input14%
  • Output0.7%
  • Cache read86%
  • Cache write0%
  • Reasoning<0.1%

Agents that carry Gemini 3.7 Flash

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

  1. Junie98% of this model's tokens3active users
  2. OpenCode2.1% of this model's tokens1active users

Benchmarks

The best result for Gemini 3.7 Flash 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
    158.0
    Rank #18 of 160 matched modelsNo conditions reportedModel released Aug 13, 2026Source: epoch.ai (opens in a new tab)
  • Terminal-Bench 4.0Agentic coding · Terminal-Bench
    11.2%
    Rank #23 of 23 matched modelsHigh · mini-SWE-agentPublished Sep 3, 2026Source: tbench.ai (opens in a new tab)
  • DeepSWEAgentic coding · Datacurve, collected by Epoch AI
    65.5%
    Rank #12 of 26 matched modelsMedium · mini-swe-agentModel released Aug 13, 2026Source: deepswe.datacurve.ai (opens in a new tab)
    Show 2 more results with other run conditionsHide the other results
    • Listed as gemini-3-7-flash (high)
      65.3%
      High · mini-swe-agentModel released Aug 13, 2026Source: deepswe.datacurve.ai (opens in a new tab)
    • Listed as gemini-3-7-flash (low)
      53.8%
      Low · mini-swe-agentModel released Aug 13, 2026Source: deepswe.datacurve.ai (opens in a new tab)
  • FrontierSWEAgentic coding · FrontierSWE, collected by Epoch AI
    20.3%
    Rank #15 of 21 matched modelsHigh · proximusModel released Aug 13, 2026Source: frontierswe.com (opens in a new tab)
  • WebDev ArenaWeb development · LMArena
    1,591
    Rank #28 of 110 matched modelsHighPublished Oct 19, 2026Source: huggingface.co (opens in a new tab)
  • Agent ArenaAgents · LMArena
    −0.01
    Rank #31 of 48 matched modelsHigh · Arena Agent ModePublished Oct 19, 2026Source: huggingface.co (opens in a new tab)
  • GPQA DiamondReasoning · Epoch AI
    94.8%
    Rank #6 of 151 matched modelsHighRun date Aug 14, 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
    71.6%
    Rank #25 of 86 matched modelsHighRun date Aug 14, 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.

  • SimpleQA VerifiedKnowledge · Epoch AI
    69.2%
    Rank #10 of 80 matched modelsHighRun date Aug 27, 2026Source: epoch.ai (opens in a new tab)

Not evaluated: Humanity's Last Exam, CursorBench. 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. Google 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
$0.19
Est. per 1M tokens
86% cache reads
At the community mix
$0.11
Est. per 1M tokens
96% cache reads
3:1 input/output blend
$1.50
Per 1M tokens at list prices
No cache

Cache reads were 86% of Gemini 3.7 Flash 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

  • Google list prices per 1M tokens: input $0.75, output $3.75, cache read $0.07.
  • 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-write price is listed, so cache writes are priced as input.

List prices by provider

  • GoogleReferencegemini-3.7-flash
    $0.75 input per 1M tokens$3.75 output per 1M tokens$0.07 cache read per 1M tokensCache write not listed1.05M tokens of context
  • Vertexgemini-3.7-flash
    $0.75 input per 1M tokens$3.75 output per 1M tokens$0.07 cache read per 1M tokensCache write not listed1.05M tokens of context
  • OpenRoutergoogle/gemini-3.7-flash
    $0.75 input per 1M tokens$3.75 output per 1M tokens$0.07 cache read per 1M tokens$0.04 cache write per 1M tokens1.05M tokens of context
  • Vercel AI Gatewaygoogle/gemini-3.7-flash
    $0.75 input per 1M tokens$3.75 output per 1M tokens$0.07 cache read per 1M tokensCache write not listed1M 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
1.05M tokens
Max output
66K tokens
Input
Text, Image, Video, Audio, PDF
Output
Text
Knowledge cutoff
Mar 2026
Release date
Aug 13, 2026
Open weights
No
Reasoning
Yes
Tool calling
Yes

Top public profiles

Public profiles that used Gemini 3.7 Flash most in this period, by tokens. Hidden accounts are never listed.

  1. 1James W. Lane III@jameswlane55.2Mtokens$5est. cost5active days$5 est. cost · 5 active days
  2. 2Tan Jeong Shiun@jeshtan2.5Mtokens<$1est. cost6active days<$1 est. cost · 6 active days
  3. 3Sachee@comicsansbestfont1.2Mtokens<$1est. cost1active days<$1 est. cost · 1 active days
  4. 4Tobias Steinhäusler@tobytaken15.1Ktokens<$1est. cost1active days<$1 est. cost · 1 active days

More from Google

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

  1. Gemini 3.8 Flash N14% adoption138active users
  2. Gemini 4 Argon8.6% adoption82active users
  3. Gemini 4 Argon Trial7.7% adoption74active users
  4. Gemini 3.8 Flash6.9% adoption66active users
  5. Gemini 3.8 Flash Tiered3.7% adoption35active users
  6. Gemini 4 Argon High1.8% adoption17active 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.
  • Terminal-BenchTerminal-Bench 4.0 official leaderboard rows (results only, no task content).Terms not stated · Retrieved Oct 21, 2026 · Data through Oct 6, 2026Terminal-Bench 4.0 leaderboard, tbench.ai. Every score links back to the leaderboard.
  • 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