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!

Unfortunate, significant price increase for a 'lite' model: $0.25 IN / $1.50 OUT vs. Gemini 2.5 Flash-Lite $0.10 IN / $0.40 OUT.

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:

https://upmaru.com/llm-tests/simple-tama-agentic-workflow-q1...

P.S: The pricing bump is quiet significant, but still stomachable if it performs well. It is significant though.

Flash-Lite’s $0.25/$1.50 price finally lets us run the translation+compliance queue without ripping through tokens. We push 400 req/s but keep a 20-second fuzzy cache of hashed prompts and only send the de-duplicated, heuristically filtered text so the model never re-processes the same boilerplate. The thinking-level knob is huge: level 1 by default gives us sub-200ms TTFB and we only bump to level 3 for flagged QA summaries. Anyone else pairing path-specific thinking levels with caches to keep high-frequency workloads sane?

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.

You can test Gemini 3.1 Lite transcription capabilities in https://ottex.ai — the only dictation app supporting Gemini models with native audio input.

We benchmarked it for real-life voice-to-text use cases:

                <10s    10-30s   30s-1m    1-2m    2-3m
  Flash         2548     2732     3177     4583    5961
  Flash Lite    1390     1468     1772     2362    3499
  Faster by    1.83x    1.86x    1.79x   1.94x   1.70x

  (latency in ms, median over 5 runs per sample, non-streaming)

Key takeaways:

- 1.8x faster than Gemini 3 Flash on average - 1-2 sec transcription time for short recordings - ~$0.50/mo for heavy users (10h+ transcription) - Best-in-class WER and formatting instruction following - Multilingual: one model, 100+ languages

Gemini is slowly making $15/month voice apps obsolete.

That's a 150% increase in the input costs and 275% increase on output costs over the same sized previous generation (2.5-flash-lite) model

I'm still clinging to gemini-2.0-flash which I think is free free for API use(?!).

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.