Solar Mini 4 vs Gemma 4 12B: a Checkpoint You Download and a Model You Call

The shortest honest comparison of Solar Mini 4 and Gemma 4 12B is that one of them is a file and the other is an endpoint. Upstage's Solar Mini 4 is a 35-billion-parameter mixture-of-experts model with 3 billion parameters active per token, a 512,000-token context window, and no open weights — you reach it through the Upstage Console, the Playground, or an on-premises deployment you arrange with the vendor. Gemma 4 12B is an 11.95-billion-parameter dense checkpoint under Apache 2.0, downloadable today, running in roughly 16 GB of unified memory on a laptop. Both are built for agentic work, both take a long document as input, and they are not competing for the same slot in your stack, because one of them has a memory bill and the other has a token bill.

That distinction gets lost because the two models look similar on a feature list. Both reason, both call tools, both emit structured output, both handle English and Korean and Japanese. The differences that decide the question are elsewhere: what happens at 3 a.m. when the thing is down, what your compliance team says about where the data lives, and whether you are paying per token or per GPU-hour.

The spec contrast, dimension by dimension

• Architecture — Solar Mini 4 is sparse: 35B total parameters, 3B active per token. Gemma 4 12B is dense: every one of its 11.95B parameters runs on every token. Solar stores ten times the weights and computes with a quarter of them; Gemma stores a quarter and computes with all of it.

• Where it runs — Solar Mini 4 runs on Upstage's infrastructure (Console, Playground) or in your own environment under an on-premises arrangement. Gemma 4 12B runs on your hardware: about 16 GB of unified memory at full precision, nearer 8 GB at 4-bit, and it fits on a single consumer GPU or an M-series laptop.

• Weights — Apache 2.0 for Gemma 4 12B, meaning commercial use, modification and redistribution with no revenue threshold. Closed for Solar Mini 4. Upstage has released open weights before, but nothing about this release says it plans to here.

• Context — 512,000 tokens for Solar Mini 4, with up to 128,000 tokens of output. Gemma 4 12B is 256,000 tokens, with Artificial Analysis listing 260K on its model page. Solar is the longer of the two, and the gap is not marginal.

• Modalities — Gemma 4 12B is encoder-free multimodal: image patches and 16 kHz audio are projected directly into the language model's embedding space, so it takes text, image, audio and video and returns text. Solar Mini 4 is text in, text out. This is the one dimension where Gemma wins outright and by a lot.

• Price — Solar Mini 4 lists at $0.10 per million input tokens and $0.40 per million output, with cached input at $0.01; a 50% launch promotion runs through 22 October 2026. Gemma 4 12B costs nothing to download, and Artificial Analysis listed hosted inference at $0.10 input and $0.30 output per million when its page was captured on 22 September 2026.

• Knowledge cut-off — February 2026 for Solar Mini 4; Gemma 4 12B shipped on 3 June 2026 with a January 2025 knowledge cut-off. Both are stale enough that retrieval is not optional.

• Independent scores — Solar Mini 4 has none yet, one day after release. Gemma 4 12B carries an Artificial Analysis Intelligence Index of 14, ranked 22nd of the 142 models tracked in its class, at 112.7 output tokens per second.

What Gemma 4 12B is actually for

Gemma 4 12B exists to put a capable multimodal model somewhere you control. Google's positioning at launch was that it approaches the company's own 26B mixture-of-experts model at less than half the memory, and that it beats the previous generation's Gemma 3 27B on reasoning, science and document tasks. The vendor-reported figures behind that claim are strong for the size: 77.2% on MMLU-Pro, 78.8% on GPQA Diamond, 94.9 on DocVQA and 88.4 on InfoVQA, all Google's own numbers and not independently reproduced.

The interesting engineering decision is that Gemma 4 12B has no separate vision or audio encoders. Image patches are projected into the model's embedding space through a single matrix multiplication; raw audio goes in the same way. That removes the memory and latency cost of running two extra towers alongside the language model, and it is why a multimodal model fits in 16 GB. It also means the language model absorbs the work the encoders would have done, which is a real trade for fine visual detail.

For agent work, the practical consequence is that Gemma 4 12B is the only one of these two models that can read a screenshot, a scanned page or a voice note as part of the loop. If your agent's job involves looking at something, the comparison is over before it starts.

What Solar Mini 4 is actually for

Solar Mini 4 is aimed at the opposite case: high-frequency, cost-sensitive work where the input is text and the volume is large. Upstage's own description is "response speed and cost matter," and the 3B active parameter count is the mechanism. A sparse model at this ratio is cheap to serve, which is why Upstage can list $0.10 per million input tokens for something with a 512K window.

The 512K context is the sharper weapon. It is the same figure Upstage ships on Solar Pro 4, its flagship, which means the compact model is not being sold short on document capacity — a 400-page contract, a full codebase, a year of support tickets. Gemma 4 12B's 256K is generous by any normal standard and half of Solar's by this one.

Reasoning is new to the Mini line. The previous generation's solar-mini models ignore a reasoning_effort parameter outright; Solar Mini 4 accepts one, and third-party agent tooling has been updated to treat it as reasoning-capable rather than deny-listing it. Upstage also shipped Solar Jev on the same day, a beta decision endpoint served on Solar Mini 4 that returns a choice, a score or a probability with no prose at all — a signal about which workloads Upstage thinks this model is for.

What Solar Mini 4 does not have, as of this writing, is a single independent number. No Intelligence Index, no Agent Arena placement, and no benchmark table from Upstage either — the changelog entry carries specs and no scores. The only third-party-ish figure in circulation is a community wrapper's self-reported test400 run, judged by another model under a stated rubric, in which Solar Mini 4 scored 98.4% field accuracy at reasoning_effort=none and averaged 1.21 seconds per call. That is one developer's comparison against their own harness, and it is not a reason to move a production decision.

The comparison that decides it

If you have GPUs and a reason to keep data in-house, Gemma 4 12B is not really competing with Solar Mini 4 at all — it is competing with your electricity bill. You download it once, you run it forever, you pay nothing per token, and you get multimodal input that Solar Mini 4 does not offer at any price. The costs that show up later are operational: someone has to own the serving stack, someone has to handle the upgrade when Google ships the next one, and your throughput is capped by whatever hardware you bought.

If you do not have GPUs, or you do and the workload is spiky, Solar Mini 4 is the one with no capital cost. You pay $0.05 per million input tokens during the launch promotion and $0.10 after, you get twice the context window, and you get a vendor SLA instead of a pager. What you give up is the weights, the multimodal input, and any ability to answer the question "what happens to my data" with "it never leaves this building."

The pricing comparison is closer than the headline suggests and worth doing carefully. At list price, Solar Mini 4 and hosted Gemma 4 12B cost the same on input ($0.10) and Gemma is cheaper on output ($0.30 against $0.40). During Upstage's promotion, Solar is half of that on both. If your workload is input-heavy — long documents, short answers — the two are effectively tied at list and Solar is cheaper today. If it is output-heavy, Gemma is cheaper unless the promotion is running.

Running either one without rebuilding your stack

The awkward part of this decision is that it is not obviously reversible. Downloading Gemma 4 12B commits you to a serving stack; adopting Solar Mini 4 commits you to an API shape. Whichever you pick, the switching cost is the thing that stops you re-evaluating in six months, and the honest reason to keep a router in the picture is to keep that cost near zero.

One thing to be accurate about: OrcaRouter does not route any Upstage model, so Solar Mini 4 is not available here — it comes from Upstage's own API and several third-party platforms. We do route Google's larger Gemma 4 sizes, the 26B-A4B and the 31B, but not the 12B checkpoint this piece is about. What the platform is actually for in this context is the comparison you make after this one: once you have decided "compact agent model, text in," the interesting question becomes which one, and that is a question you answer by putting three of them behind one key and measuring on your own traffic rather than by reading another spec sheet. A routing DSL that composes several models into a single call is how you run that test without writing three integrations.

Which one to pick

Take Gemma 4 12B if you have the hardware, if your agent needs to look at images or listen to audio, if you need the weights for fine-tuning, or if the answer to "where does the data go" has to be "nowhere." Take Solar Mini 4 if you do not want to run inference, if your inputs are long text and your outputs are short, if a 512K window matters, or if you need a Korean-first model with a vendor contract behind it.

If you are undecided, the tiebreaker is not a benchmark. It is the question you cannot answer yet about Solar Mini 4: nobody outside Upstage has measured it. Gemma 4 12B has an Intelligence Index of 14 and a public ranking; Solar Mini 4 has a spec sheet, a price and a promotion that expires on 22 October. Testing it costs you a few dollars during that window. Betting a production path on it costs you a rewrite if the numbers, when they arrive, are unkind.