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Hi, I’m the author of this blog post. I wrote it about 4 weeks ago, and the VLM world is moving so fast that it’s already kinda outdated. I think Gemini 3.7 Flash might be a better choice now, especially when you factor in the price.

Here’s a comparison of the best low-cost models I put together last week. What’s crazy is that Gemini 3.7 Flash is now 50% off on OpenRouter, and this chart doesn’t even account for that discount. https://x.com/skalskip92/status/2088032652301304121?s=20

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Curious why you didn't try Gemini 3 pro? That is the model I've been using for OCR entry of handwritten datasheets (JPGS of datasheets, structured JSON output). At my scale, the cost of 3 pro is basically not an issue, but if there are improvements in quality, I'd definitely be willing to explore other models
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In my experience starting with Gemini 2.5 Pro, moving to 3 and 3.1, 3.5 Flash, 3.6 Flash, and finally 3.7 Flash, 3.7 Flash is just as good if not better than 3 especially on high resolution mode (same token count per page as 3.1).

I run complicated, messy PDFs through these models. 2.5 Pro required a lot of kludgy hacks to get it to fully "see," but from 3.1 pro on I've removed many of them and haven't spotted problems.

3.7 Flash scores better than 3.1 pro on most benchmarks, leading me to believe that even if your OCR requires reasoning to interpret text or data, 3.7 Flash is probably going to be better.

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3 Pro is quickly approaching one year old. There's almost no reason to benchmark it, especially since a new version of Gemini Pro was supposed to be released mid 2026 and hasn't seen the light of day.
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That would make sense if we already knew that, for these kinds of tasks it was significantly worse. The tests that I'm aware of for these tasks show it as still performing near the top.
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I think it definitely makes sense since it's still the best Google has to offer in the "pro" tier.
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3 and 3.1 Pro are both marked as deprecated by Google. Even if they're the best Google offers, it would be foolish to choose a model that's explicitly deprecated.

It's not a technical problem, it's a commercial one. If Google can't ship a model to replace the one they deprecated, that tells you everything you need to know about choosing a Gemini model for whatever you're trying to do.

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3.1 Pro is not deprecated!
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https://ai.google.dev/gemini-api/docs/deprecations

That link shows 3.1 pro listed as deprecated with no replacement model.

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No, that's the "preview" version (gemini-3.1-pro-preview) aka the beta/early version before the official release of 3.1 Pro.
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The “pro” moniker means nothing

these models aren’t successors and barely have a common ancestor, they are independently baked in the training oven and assigned a semantic version randomly by someone trying to show initiative but not trying to do on the toes of the last guy who got promoted first

So 3 pro is outdated and will likely never exit preview

The “flash” and “lite” models are the real “pro” in colloquial ideas of fleshed out and capability, at this point.

they’re better, faster and cheaper, larger context windows keeping up with the industry and more

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They are smaller models, and you can tell. Small models make dumb common-sense mistakes that big models never do. This is the "smell" many talk about.
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Do you have cases where you still see 3.1 pro outperforming 3.7 flash?
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Yes, for complex questions of biology, physics, and analysis of anomalies.

3.7 Flash is better at coding, sure, but AI is not just for coding.

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hasn't been an issue since 3.5 for me, what have you seen, say, in the last two months
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For complex questions of biology, physics, and analysis of anomalies, 3.1 Pro is still better than 3.7 Flash for me.

3.7 Flash is better at coding, sure, but AI is not just for coding.

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At this point, VLM benchmarks should probably come with an expiration date. A four-week-old leaderboard can already be measuring a different market.
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What about Gemma ?
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Gemini tops their vision evals [0] by a mile, with 4/5 top spots going to variants of it. Qwen is the only other contender, likely due to how good it is for object detection, where it crushes the competition [1].

[0] https://playground.roboflow.com/evals

[1] https://playground.roboflow.com/evals/object-detection

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Yeah I was thinking about giving Luna a go with my PDF data extraction, but I think I‘ll stay on Gemini. It does a very good job.
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Gemini is still my top choice within production software for typical data extraction from unstructured data. Gemini Flash Lite feels like a cheat code for speed, and it's really cheap.

Some other Chinese models are also fast and cheap, but a harder sell in a U.S. production environment.

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Speaking from experience here, flash lite models have amazing price, speed, and perform far above their size, but are susceptible to very bad instruction following and recall when either complexity or context size inch up. They’ll just forget to apply your instructions to portions of the input, and repeat parts of the input that should be returned verbatim as direct quotes but with subtle changes (breaking urls, for example).
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Yes you have to continuously tune the prompts ever so subtly. 3.5 is a lot better than than 3.1 tho.

Important to remember that json schema instructions take precedence over the normal prompt, so move as much into property descriptions as possible.

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This was 3.5 flash lite, actually, and after prompt tuning. It was very clearly an issue that correlated with input (JSON array) size, the more elements in the batch, the higher the error rate.

3.0 flash (not lite) handled it like a champ though, fwiw.

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Yeah Gemini 3.5 Flash Lite is really good. Which Chinese models can you recommend?
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Hi, I’m the author of this blog. It depends on how strong of a model you need, but in general, Qwen is easily the best among the Chinese models right now.

Over the last two weeks, Qwen released two new models. Qwen3.8-Max is totally insane, but it’s only available through the Alibaba Cloud API. I wrote a similar blog covering Qwen3.8-Max: [https://blog.roboflow.com/qwen3-8-max/](https://blog.roboflow.com/qwen3-8-max/)

If you’re looking for something you can run locally, Qwen3.8-27B might be a great option. On Friday, I did a quick comparison between Qwen3.8-Max and Qwen3.8-27B: [https://x.com/skalskip92/status/2088411215441621469?s=20](https://x.com/skalskip92/status/2088411215441621469?s=20)

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Googles local gemma models which target roughly the same parameter count range, are known for being a lot better at vision tasks than qwen, no idea if 3.8 has changed that though
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Really? Gemma4-31B should be better than Qwen3.8-27B? I'm happy to test that.
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I've been using Qwen3.5-9B, hosted locally for PDF data extraction and it performs pretty well when extracting data from tables and infographics
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Gemini is honestly an excellent LLM with many capability strengths.

For example, 3.7 Flash is #1 on MMLU Pro and AA’s agentic spreadsheets/docs benchmark, etc. Yes, beating Fable.

Agentic coding is only one dimension.

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Anecdotally, Gemini Flash is the leader for a particular use case of mine and has been since at least version 2.5. But now there's also Luna as the first real competitor thanks to the price cut.

My worry is that this is a zero-sum game and when Gemini catches up on coding, it'll regress to the mean in other areas.

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thats so helpful - tysm
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