Aion 3.5 Mini vs Gemma 4 12B: One of These Has a Scoreboard and One Has a Price Tag

Aion 3.5 Mini and Gemma 4 12B are the same kind of purchase in one respect only: both are small models you can call through an API today. AionLabs released Aion 3.5 Mini on 23 September 2026 as a closed, text-only multi-model system at $0.70 per million input tokens and $1.40 output. DeepMind released Gemma 4 12B on 3 June 2026 as an 11.95B-parameter open-weights model under Apache 2.0, with an encoder-free multimodal input path and a published evaluation card. The practical difference is not the parameter count or the price line. It is that one of these models can be measured before you commit and the other cannot be measured at all.

Everything about Aion 3.5 Mini below comes from AionLabs' own published model list and the served configuration on its API. Everything about Gemma 4 12B comes from the vendor's model card, which is where its numbers originate, plus the third-party evaluation harness that has actually run it. Where a figure is vendor-reported, it is labelled as such. There is no independent evaluation of any Aion model, and that is stated as fact rather than as a caveat.

Where the two actually line up

Fewer places than the "small model" label suggests, and the ones that do line up are worth being precise about because they are the ones a buyer checks first.

• Context — Aion 3.5 Mini carries 262,144 tokens. Gemma 4 12B carries a 256K window. Nominally a wash, and in practice both are large enough that context is not the deciding line.

• Maximum output — Aion 3.5 Mini caps a single response at 32,768 tokens, a ceiling shared across the whole Aion catalogue. Gemma 4 12B publishes no equivalent single-number cap on its card, so this is a line you would have to test rather than read.

• Tool calling — both support it. Aion 3.5 Mini accepts tool definitions, JSON response format and temperature on an OpenAI-compatible endpoint. Gemma 4 12B ships function calling in its instruction-tuned form.

• Reasoning control — Aion 3.5 Mini reasons on every call and exposes three effort levels, max, high and low, with high as the default; reasoning is not optional. Gemma 4 12B ships reasoning and non-reasoning variants, and the two score differently on the same harness because they are doing different amounts of work.

• Input modality — this is the first line where they diverge hard. Aion 3.5 Mini is text in, text out, and the architecture declares no image input. Gemma 4 12B takes text, image, audio and video in, and returns text.

What Gemma 4 12B can show you

Gemma 4 12B arrives with a vendor evaluation card, and the numbers on it are vendor-reported rather than independently reproduced — but they exist, they are specific, and they cover the axes a buyer actually argues about.

• Vendor-reported reasoning and knowledge — MMLU Pro 77.2, GPQA Diamond 78.8, HLE without tools 5.2, BigBench Extra Hard 53.0, MMMLU 83.4.

• Vendor-reported coding — LiveCodeBench v6 72.0, Codeforces ELO 1659, AIME 2026 without tools 77.5.

• Vendor-reported agentic — Tau2 averaged over three runs 69.0, and Codeforces ELO again as the strongest single showing on the card.

• Vendor-reported multimodal — MMMU Pro 69.1, MATH-Vision 79.7, MedXPertQA MM 48.7. The card carries no DocVQA row; the nearest document figure is an OmniDocBench 1.5 average edit distance of 0.164.

• Long-context recall — MRCR v2, 8-needle, 128k, 43.4. That is the honest weak spot on the card and it is worth reading before you assume a 256K window behaves like a 256K window at the far end.

• Third-party measurement — Artificial Analysis lists Gemma 4 12B with a Reasoning Intelligence Index of 14 and a Non-reasoning Index of 9 on its own harness, an output speed of about 113 tokens per second in reasoning mode, and a list price of $0.10 input and $0.30 output per million tokens. Index revisions move between snapshots, so treat the index as directional and the price and speed as the durable lines.

What Aion 3.5 Mini can show you

A price, a window, an architecture description, and nothing else. AionLabs publishes no SWE-bench Verified, Terminal-Bench, GPQA or MMLU-Pro figure for any model in its catalogue, and the 3.5 launch materials carry no benchmark table at all. Artificial Analysis has no page for Aion 3.5 Mini, Aion 3.5, Aion 3.0 or Aion 3.0 Mini — none of the four appears in the model index.

That absence is not automatically damning, and it would be wrong to present it as one. AionLabs describes its models as multi-model systems built for narrative and storytelling work, with a collaborative generation process in which several specialised models each contribute to a response. The composite indices above are assembled from mathematics, code and economic tasks — the wrong instrument for judging prose. A model can be genuinely good at the job it was built for and score badly on a coding leaderboard, or never be entered into one.

What the absence does mean is narrower and harder: you cannot compute a cost per completed task. Aion 3.5 Mini's $0.70 and $1.40 are list prices with no measured denominator. Gemma 4 12B's $0.10 and $0.30 have a measured denominator attached, and the same harness that measured it also reports a cost per task. That is the asymmetry, and it survives every argument about whether the benchmarks are the right ones.

The price gap is bigger than the capability gap is likely to be

Set the two rate cards side by side and the shape of the decision changes. Aion 3.5 Mini charges $0.70 per million input tokens and $1.40 per million output. Gemma 4 12B is listed at $0.10 input and $0.30 output on the third-party harness that measures it. That is seven times the input rate and roughly four and a half times the output rate, for a model whose own vendor publishes no score you can compare against.

Two things complicate a straight multiplication, and both favour Gemma 4 12B further. First, Aion 3.5 Mini reasons on every call, so the output line carries tokens you did not ask for and cannot bound — AionLabs publishes no statement about how many tokens the model spends thinking at any of its three effort levels. Gemma 4 12B lets you switch the thinking step off, which changes both the bill and the latency. Second, Gemma 4 12B is open weights under Apache 2.0, so the $0.10 and $0.30 are one option among several rather than the only way to run it.

None of that settles which model writes better, because nobody has measured either on that axis. It does settle which one you can put in front of a stakeholder.

Running the two of them together

The useful move here is not to pick one. Aion 3.5 Mini and Gemma 4 12B sit behind different interfaces but the same calling convention — AionLabs exposes an OpenAI-compatible endpoint, and Gemma 4 12B is served by the usual OpenAI-compatible runtimes. That makes them interchangeable at the base-URL level, which makes a chain cheap to build: Aion 3.5 Mini first for the prompts where the ensemble is the reason you are calling it, Gemma 4 12B behind it for everything where you want a measured model, a switchable thinking step and a rate card a quarter the size.

A gateway that fronts several hundred models on one key is what makes that chain a config line rather than a second integration, and it is also where a failover rule earns its keep — a 429 or a provider outage gets absorbed before the response starts instead of surfacing as a failed job. Where a vendor moves its rate card, a pass-through router bills the provider's list price with nothing added per token, so the change lands the same day rather than at the next billing cycle. One detail worth checking rather than assuming: neither Aion 3.5 Mini nor Gemma 4 12B is served on every platform that hosts models of this size, and Gemma 4 12B in particular is absent from some catalogues that carry its larger siblings. Verify the identifier resolves before you wire it in.

How to decide

• If you need a number before you commit — Gemma 4 12B. It is the only one of the two with a published evaluation card and a third-party index, and the evaluation predates your decision rather than following it.

• If you need image, audio or video input — Gemma 4 12B. Aion 3.5 Mini declares text to text and nothing else.

• If you need to own the thing — Gemma 4 12B. Apache 2.0 weights mean you can fine-tune, quantise or self-host it; Aion 3.5 Mini is a closed system with no weights to download.

• If narrative and long-form prose is the entire job — this is the one case where the comparison fails on your behalf. Aion 3.5 Mini was built for that work and Gemma 4 12B was built as a generalist, and no published number tells you which writes better. Try it on a path you can afford to lose.

• If cost per finished job is the deciding line — Gemma 4 12B, on the arithmetic alone, and the gap widens the more the task depends on output tokens.

Both models are recent, both are live, and only one of them is asking you to take its word for it. The defensible summary is that Gemma 4 12B is the measurable default and Aion 3.5 Mini is a specialist you adopt on purpose, not by comparison — and if you do adopt it, adopt it beside a fallback rather than instead of one.