Gemini 3.7 Flash
8859°
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Google | 1,048,576 token context | $0.75/M input, $3.75/M output. Gemini 3.7 Flash is a multimodal model from Google for fast agentic workflows, coding, and complex multi-step reasoning. It is designed for tasks that require responsive performance and reliable multi-step problem solving.
Guides for this model
How to choose an AI model in October 2026: the closed source news, then the open weights worth downloading
Open weights vs open source vs API-only: what you are actually allowed to do
The 10 cheapest ChatGPT alternatives in 2026: free local models, free tiers and flat-rate plans
Captain's notes
Notes from 2026-10-13
Gemini 3.7 Flash is Google's multimodal workhorse built for speed and multi-step agentic work. It handles text, vision, audio, and video inputs across a context window just over one million tokens. At under a dollar per million input tokens, it sits in the practical middle ground between throwaway-cheap and frontier-expensive, and suits developers who need reliable chained reasoning without waiting around.
Known for
- Handles vision, audio, and video inputs natively alongside text.
- One-million-token context window for long documents or extended conversations.
- Designed specifically for agentic, multi-step workflows rather than single-turn chat.
- Fast response times suited to interactive and pipeline use cases.
- Competitive input price at $0.75 per million tokens.
Watch out for
- Closed weights only — no local deployment, no offline use, no self-hosting.
- Output costs ($3.75/M) add up quickly in high-volume agentic loops that generate long responses.
- Google has not published a model card excerpt, so internal benchmarks and training details are opaque.
Prompts to try
Debug a multi-step agent
I have a Python agent that calls three tools in sequence: a web search, a data parser, and a report formatter. The agent sometimes skips the parser step and passes raw search results directly to the formatter. Here is the orchestration code: [paste your code]. Identify why the step is being skipped and rewrite the relevant section to fix it.
Reason through a build plan
I need to migrate a monolithic Flask app to a microservices architecture. The app has user auth, a payments module, and a notifications service all sharing one Postgres database. Walk me through a phased migration plan, flag the three highest-risk steps, and suggest how to test each phase before cutting over.
Analyse an uploaded image
I am attaching a screenshot of a dashboard. Identify every metric visible, note any that appear anomalous compared to the others, and suggest two follow-up queries I should run against the underlying data to investigate the anomalies.
Compare with
- Gemma 4 31BOpen-weights model262,144 token context · $0.20/M input, $0.40/M output
- Gemma 4 26B A4BOpen-weights model262,144 token context · $0.09/M input, $0.30/M output
- gemma 4 31B it turboOpen-weights model262,144 token context · $0.09/M input, $0.34/M output
- Gemini 3.8 FlashAPI-only model1,048,576 token context · $0.83/M input, $4.12/M output
More text models from google
Questions people ask
- Can I run Gemini 3.7 Flash locally?
- No. Google has not released weights for this model, so it is API-only and cannot be run offline or self-hosted.
- What does Gemini 3.7 Flash cost and how long is its context?
- Input is $0.75 per million tokens and output is $3.75 per million tokens, with a context window of 1,048,576 tokens.
- How does Gemini 3.7 Flash differ from other Gemini models?
- Flash is positioned for speed and agentic multi-step tasks rather than maximum capability; Google has not published a direct comparison to sibling models such as Gemini 3.7 Pro on this page.
Captain's verdictA practical, fast multimodal model for agentic pipelines — the million-token context is genuinely useful, and the price is honest.
Captain writes these notes from the model's own description and model card. Prices and context sizes are the provider's at the time of writing and can change.
Ranking badge
Updated Oct 22, 2026
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Also ranked: #43 Hottest Text / LLM model · #43 Hottest Text / LLM text model
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Gemini 3.7 Flash Popularity Trend
Heat score since 13 Aug 2026
Heat score
8,859°
This week
▲
26.7%
8,827° then, 8,859° now
This month
▲
254%
8,691° then, 8,859° now
Past year
n/a
Wasn't released/tracked
7,000°
Floor
The scale runs from 0° to 9,999°. Everything below 7,000° is kept for models no source reports on yet, which carry a stand-in number there so lists stay complete. 7,000° is where measured data begins: a model with real data but almost no downloads sits at the floor, and the measurements fill the degrees above it. The floor says nothing about the model; only the degrees above it do, and they are what separates one model from another.
+1,576°
OpenRouter tokens
1,793,062,343,084 tokens this model processed through OpenRouter in the last 30 days, prompts and answers together. Every 10,000,000 tokens count as one download in the download term, 179,306 in all; the last day's tokens join the adoption rate. Source: OpenRouter (openrouter.ai/rankings).
+283°
Adoption rate
About 5,896 downloads a day, measured between two readings of the sources, never guessed from a lifetime average. Speed weighs a quarter of the download term, so a fresh surge shows without letting a one-day spike outrank years of use.
Show detailed breakdown
Every number on this site can be checked. This model's heat score is 8,859°, and here it is built one ingredient at a time, with the running total after each: the floor, the degrees each source adds, the bonus for the kind of model and what the age of its weights takes away. The colors are the bands of the chart above. The glossary at the end defines every term.
- 1Start at the floor.Why 7,000° and not zero: the scale runs from 0° to 9,999°, and the part below 7,000° is reserved for models no source reports on yet, which carry a stand-in number there so the lists stay complete and never outrank a model with data. 7,000° is where measured data begins. A model with real data but almost no downloads sits here; everything that follows is measurement, and the room above the floor holds ten tenfold steps in downloads at 300° each. The floor is the same for every model, so it decides nothing; the degrees above it do.7,000°running total
- 2Add the downloads.179,306 download-equivalents in all, from OpenRouter tokens. Ten times the downloads adds the same 300°, so size matters but never runs away with the score.+1,576°8,576°running total
- 3Add the adoption rate.About 5,896 downloads a day right now, measured between two readings, at a quarter of the weight.+283°8,859°running total
- =The heat score.Where this model stands on every list of this site. Nothing else adjusts it.8,859°
Glossary: the terms used here
- Heat score
- One number for how much a model is used and wanted right now, from 0° to 9,999°. Every list on this site that says hottest is sorted by it and nothing else.
- Floor
- The 7,000° every model with real data starts from. The scale runs from 0° to 9,999°; the part below the floor is reserved for stand-ins (models without data), so that a model with data always ranks above one without. The floor is the same for everyone and decides nothing; the degrees above it are the measurement.
- Download-equivalents
- Everything the score treats as a download, added up: downloads themselves, each torrent seeder as 1,000, each AI Horde image and each run on a generation site as one.
- Log scale
- Counting by tens, not by ones: going from 1,000 to 10,000 downloads adds the same degrees as going from 10,000 to 100,000. It keeps giants from crowding out everything else.
- Likes
- Thumbs up and likes from every source, worth half a download each on the log scale.
- Adoption rate
- How many downloads a day a model gets right now, from the difference between two readings of a source. A quarter of the weight of the download term.
- Measured and estimated
- Measured means two readings of a source exist and the difference is real. Estimated means only today's total is known and the past is spread over the model's life; the site says which one it is showing.
- Seeder
- Someone who keeps a copy of the weights online for others to download over a torrent, or a hosted mirror doing the same. Each one counts as 1,000 downloads.
- AI Horde
- A volunteer network that generates images for free on donated computers. The number of images it has made with a model is public, and each counts as one download.
- Generation sites
- Sites that host their own copies of a model and let people generate with it, such as PixAI, SeaArt and Yodayo. Their downloads count like Civitai's and each generation run as one download.
- OpenRouter tokens
- A text model served through an API, such as Claude or GPT, has no weights to download, so its usage is measured in tokens: the pieces of text it read and wrote through OpenRouter, a gateway many apps use, over the last 30 days. Every 10,000,000 tokens count as one download. Source: OpenRouter (openrouter.ai/rankings), CC BY 4.0.
- Full model (checkpoint)
- The weights themselves, the file that generates on its own. It gets a bonus because add-ons depend on it.
- LoRA and embedding
- Small add-on files that steer someone else's weights toward a style, a character or a concept. They cannot run alone.
- Family and weights
- The base a model is built on, such as SD 1.5, SDXL, Flux or Wan. Every model inherits the release date of its family's weights.
- Age
- Degrees taken off for how long ago the family's weights came out: 150° a year, up to 750°. It keeps old downloads that had years to pile up from freezing the top of the list.
- Family boost
- A small, bounded nudge for LoRAs of a family this site is pushing forward right now, always listed in the breakdown.
- Stand-in score
- A stable number below the floor given to a model no source reports on yet, so lists stay complete. It never ranks above a model with data.
- New! (grace period)
- A model less than 7 days old shows New! instead of a score and holds no place in the rankings. Its score is computed from day one, but a model one day old grows faster than anything, so without the wait the week's #1 would always be whatever came out yesterday rather than what held up for a week.
- Trend
- How a model's growth over a period compares with the typical model's growth over the same period. Faster than typical reads as a rise, slower as a fall.
- Typical model
- The middle of the catalog: half the models with a figure grew faster over the period, half slower.
How this model's popularity has moved against the rest of the catalog: its growth in downloads, likes and usage over each period, measured against the typical model's growth over the same period, with its heat score at the time. Above the typical growth reads as a rise, below it as a fall. The past is reconstructed from today's totals, the model's first day and its measured download speed where two readings exist; a model that did not exist at the start of a period shows n/a.
Gemini 3.7 Flash is a text model for the Text / LLM family listed on ModelTrends.ai, a read-only catalog of open source AI image, video and text models. Compare it with other Text / LLM models and check its heat score to see how it is trending.
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