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
#70
by active users
Active users
221
All time
Adoption
9.0%
of 2,457 accounts
Tokens
185.2B
cache included
Est. cost
$23.9K
at public list prices
First seen
Jul 29, 2026
in any submission

Oct 10, 2024 – Nov 12, 2026 (UTC)2,457 accounts100% of tokens from an identified modelData through Nov 12, 2026, 07:57 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 Gemini 3.7 Flash, Oct 10, 2024 to Nov 12, 2026. Peak 70. Use the left and right arrow keys to read each day.80400
Show daily values
Daily active users and tokens for Gemini 3.7 Flash
Date (UTC)Active usersTokens
Oct 10, 202400
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Nov 1, 202400
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Dec 1, 202400
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Jan 1, 202500
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Jul 29, 2026110M
Jul 30, 202600
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Aug 1, 2026190.1M
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Aug 13, 20266449.4M
Aug 14, 2026473.7B
Aug 15, 2026364.1B
Aug 16, 2026346.1B
Aug 17, 2026519.6B
Aug 18, 2026548.4B
Aug 19, 2026676.8B
Aug 20, 2026687.2B
Aug 21, 2026706.4B
Aug 22, 2026493B
Aug 23, 2026415.3B
Aug 24, 20265816.3B
Aug 25, 20266116.3B
Aug 26, 2026609.6B
Aug 27, 20265311.9B
Aug 28, 20266011.1B
Aug 29, 20265411.1B
Aug 30, 2026546.7B
Aug 31, 2026667.1B
Sep 1, 2026548.4B
Sep 2, 2026567.9B
Sep 3, 2026312B
Sep 4, 202612860.3M
Sep 5, 2026121.5B
Sep 6, 2026132.1B
Sep 7, 20269296.6M
Sep 8, 20267364.5M
Sep 9, 202614361.4M
Sep 10, 2026111.3B
Sep 11, 20268173.3M
Sep 12, 2026452.5M
Sep 13, 20266603.2M
Sep 14, 20267516.8M
Sep 15, 202613332.4M
Sep 16, 2026122B
Sep 17, 20264838.6M
Sep 18, 202651B
Sep 19, 202651.8B
Sep 20, 2026213.8M
Sep 21, 2026726.8M
Sep 22, 2026528.9M
Sep 23, 2026649.9M
Sep 24, 20264216.3M
Sep 25, 202656.7M
Sep 26, 2026588.3M
Sep 27, 20266204.9M
Sep 28, 2026440.4M
Sep 29, 2026762.5M
Sep 30, 20269124.2M
Oct 1, 20265108.6M
Oct 2, 2026654.2M
Oct 3, 2026520.7M
Oct 4, 2026322.2M
Oct 5, 20265236.9M
Oct 6, 202645.4M
Oct 7, 2026638M
Oct 8, 2026739.3M
Oct 9, 20263487K
Oct 10, 20261178K
Oct 11, 202600
Oct 12, 202631.1M
Oct 13, 20262408K
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
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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
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Token mix

How the 185.2B tokens split. Cache reads are usually most of an agent's tokens and are priced far below input.

  • Input7.1%
  • Output0.2%
  • Cache read93%
  • Cache write<0.1%
  • 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. Antigravity CLI46% of this model's tokens116active users
  2. Pi27% of this model's tokens25active users
  3. Oh My Pi14% of this model's tokens27active users
  4. Antigravity IDE Extension5.9% of this model's tokens43active users
  5. Antigravity3.5% of this model's tokens26active users
  6. Claude Code1.1% of this model's tokens6active users
  7. Codex CLI0.7% of this model's tokens6active users
  8. Gajae Code0.5% of this model's tokens5active users
  9. OpenCode0.4% of this model's tokens15active users
  10. Senpi (OmO Native)0.4% of this model's tokens3active users
  11. Gemini CLI0.1% of this model's tokens4active users
  12. Hermes Agent<0.1% of this model's tokens9active users
  13. Junie<0.1% of this model's tokens6active users
  14. DeepSeek Harness<0.1% of this model's tokens3active users
  15. Cline<0.1% of this model's tokens1active users
  16. ZCode<0.1% of this model's tokens1active users
  17. Command Code<0.1% of this model's tokens1active users
  18. Copilot<0.1% of this model's tokens2active users
  19. Kilo<0.1% of this model's tokens1active users
  20. OpenClaw<0.1% 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
    158.4
    Rank #25 of 158 matched modelsNo conditions reportedModel released Aug 13, 2026Source: epoch.ai (opens in a new tab)
  • Terminal-Bench 4.1Agentic coding · Terminal-Bench
    12.4%
    Rank #22 of 23 matched modelsHigh · mini-SWE-agentPublished Nov 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 #17 of 23 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 #7 of 160 matched modelsHighRun date Aug 14, 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.

  • FrontierMath Tiers 1–3 (v2)Reasoning · Epoch AI
    71.6%
    Rank #29 of 90 matched modelsHighRun date Aug 14, 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.

  • SimpleQA VerifiedKnowledge · Epoch AI
    69.2%
    Rank #12 of 86 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 Nov 12, 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
$0.13
Est. per 1M tokens
93% cache reads
At the community mix
$0.14
Est. per 1M tokens
93% cache reads
3:1 input/output blend
$1.50
Per 1M tokens at list prices
No cache

Cache reads were 93% of Gemini 3.7 Flash tokens across all time and 93% 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 across all time.
  • The community mix is the tokens of every identified model across all time.
  • 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 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
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. 1Jeff Emanuel@Dicklesworthstone64.1Btokens$7.1Kest. cost23active days$7.1K est. cost · 23 active days
  2. 2Nehr@AgileInnov8tor34.3Btokens$5.4Kest. cost29active days$5.4K est. cost · 29 active days
  3. 3Joosung Park@hyperlapse12219Btokens$2.3Kest. cost20active days$2.3K est. cost · 20 active days
  4. 4Alex Gorbatchev@alexgorbatchev10.6Btokens$1.4Kest. cost20active days$1.4K est. cost · 20 active days
  5. 5dlivxpr@dlivxpr3.8Btokens$502est. cost16active days$502 est. cost · 16 active days
  6. 6Peter Brian Gwadenya@pgwiz3Btokens$376est. cost30active days$376 est. cost · 30 active days
  7. 7Brad Lishman@bradlishman2.9Btokens$372est. cost8active days$372 est. cost · 8 active days
  8. 8WooSung Jang@Fuzen24782.2Btokens$295est. cost5active days$295 est. cost · 5 active days
  9. 9Miguel@27mfp2Btokens$206est. cost15active days$206 est. cost · 15 active days
  10. 10Haesol Shin@haesol-shin1.6Btokens$196est. cost18active days$196 est. cost · 18 active days

More from Google

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

  1. Gemini 3 Flash Preview39% adoption956active users
  2. Gemini 3 Pro Preview28% adoption676active users
  3. Gemini 3.1 Pro Preview25% adoption612active users
  4. Gemini 2.5 Pro25% adoption607active users
  5. Gemini 2.5 Flash24% adoption590active users
  6. Gemini 3.8 Flash12% adoption286active 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.
  • Terminal-BenchTerminal-Bench 4.1 official leaderboard rows (results only, no task content).Terms not stated · Retrieved Nov 12, 2026 · Data through Nov 11, 2026Terminal-Bench 4.1 leaderboard, tbench.ai. Every score links back to the leaderboard.
  • 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