Lots of comments about the price change, but Artifical Analysis reports that 3.1 Flash-Lite (reasoning) used fewer than half of the tokens of 2.5 Flash-Lite (reasoning).
This will likely bring the cost below 2.5 flash-lite for many tasks (depends on the ratio of input to output tokens).
That said, AA also reports that 3.1 FL was 20% more expensive to run for their complete Intelligence index benchmark.
The overall point is that cost is extremely task-dependent, and it doesn’t work to just measure token cost because reasoning can burn so many tokens, reasoning token usage varies by both task and model, and similarly the input/output ratios vary by task.
Do not use 3.1 Flash-Lite with HIGH reasoning, it reasons for almost max output size, you can quickly get to millions of tokens of reasoning in a few requests.
Wow, that’s very interesting. I wish more benchmarks were reported along with the total cost of running that benchmark. Dollars per token is kind of useless for the reasons you mentioned.
Yup, MiniMax M-2.5 is a standout in that aspect. It's $/token is very low, because it reasons forever (fun fact, that's also the reason why it's #1 on OpenRouter, because it simply burns through tokens, and OpenRouter ranking is based on tokens usage)...
For the last 2 years, startup wisdom has been that models will continue to get cheaper and better. Claude first, and now Gemini has shown that it's not the case.
We priced an enterprise contract using Flash 1.5 pricing last summer, and today that contract would be unit economic negative if we used Flash 3. Flash 2.5 and now Flash 3.1 Lite barely breaks even.
I predict open-source models and fine-tuning are going to make a real comeback this year for economic reasons.
Yea but there is a whole world of tasks for which Flash 2.5-lite was sufficiently intelligent. Given Google's depreciation policy, there will soon be no way to get that intelligence at that price.
I mean the same level of intelligence does get cheaper. People just care about being on the frontier. But if you track a single level of intelligence the price just drops and drops.
You know what would be great? A light weight wrapper model for voice that can use heavier ones in the background.
That much is easy but what if you could also speak to and interrupt the main voice model and keep giving it instructions? Like speaking to customer support but instead of putting you on hold you can ask them several questions and get some live updates
It's actually a nice idea - an always-on micro AI agent with voice-to-text capabilities that listens and acts on your behalf.
Actually, I'm experimenting with this kind of stuff and trying to find a nice UX to make Ottex a voice command center - to trigger AI agents like Claude, open code to work on something, execute simple commands, etc.
I speak daily in both English and Russian and have been using Gemini 3 Flash as my main transcription model for a few months. I haven't seen any model that provides better overall quality in terms of understanding, custom dictionary support, instruction following, and formatting. It's the best STT model in my experience. Gemini 3 Flash has somewhat uncomfortable latency though, and Flash Lite is much better in this regard.
This is going to be a fun one to play with. I've been conducting tests on various models for my agentic workflow.
I was just wishing they would make a new flash-lite model, these things are so fast. Unfortunately 2.5-flash and therefore 2.5-flash-lite failed some of my agentic workflows.
If 3.1-flash-lite can do the job, this solves basically all latency issues for agentic workflows.
I publish my benchmarks here in case anyone is interested:
The Gemini Pro models just don't do it for me. But I still use 2.5 Flash Lite for a lot of my non-coding jobs, super cheap but great performance. I am looking forward to this upgrade!
Are there good open models out there that beat gemini 2.5 flash on price? I often run data extraction queries ("here is this article, tell me xyz") with structured output (pydantic) and wasn't aware of any feasible (= supports pydantic) cheap enough soln :/
This will likely bring the cost below 2.5 flash-lite for many tasks (depends on the ratio of input to output tokens).
That said, AA also reports that 3.1 FL was 20% more expensive to run for their complete Intelligence index benchmark.
The overall point is that cost is extremely task-dependent, and it doesn’t work to just measure token cost because reasoning can burn so many tokens, reasoning token usage varies by both task and model, and similarly the input/output ratios vary by task.
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