Solar Mini 4 vs Granite 4.2 3B: a Model You Call and a Checkpoint You Download

Put Solar Mini 4 next to Granite 4.2 3B and the interesting number is not the benchmark gap. It is that Granite 4.2 3B, IBM's Apache-2.0 reasoning model released 25 August 2026, will run on a laptop tonight for nothing, while Solar Mini 4, Upstage's proprietary model released 22 September 2026, will not run anywhere you own and bills $0.10 per million input tokens and $0.40 per million output tokens to answer a question. Both activate roughly three billion parameters of compute per token. One is a file. The other is a meter.

That difference decides almost everything else about this comparison, including which benchmarks are even worth putting side by side. Granite 4.2 3B had an independent evaluation long before Solar Mini 4 did — Artificial Analysis scores it 9 on the Intelligence Index v4.3.2, from the same evaluation suite and the same organisation that scores Solar Mini 4 at 24. Which means the fifteen-point gap here is unusually well-founded: it is one lab, one methodology, and two models that nobody had to take on faith.

The specification, on the dimensions that actually separate them

• Architecture — Solar Mini 4 is a sparse mixture-of-experts: 35B total parameters, 3B active per token, per Upstage. Granite 4.2 3B is dense, about 3B parameters with roughly 4B total including embeddings per the safetensors metadata. Same activation budget, two different ways of spending it.

• Weights — Solar Mini 4 is proprietary and closed. Granite 4.2 3B ships under Apache 2.0 with a documented training recipe down to the data proportions.

• Context — Upstage documents 512K tokens with up to 128K output; Artificial Analysis lists 1.0M for the same model, so treat the ceiling as unsettled. Granite 4.2 3B runs 131,072 tokens natively, extended to 512K by a fifth pre-training phase.

• Price — Solar Mini 4 at $0.10 / $0.01 cached / $0.40 per million tokens, currently 50% off through 22 October 2026. Granite 4.2 3B has no vendor rate card because there is nothing to rent; the hosted endpoints Artificial Analysis tracks price it around $0.03 / $0.12, and self-hosting costs electricity.

• Intelligence Index — 24 for Solar Mini 4, 9 for Granite 4.2 3B, both from Artificial Analysis v4.3.2.

• Speed — 204 output tokens per second for Solar Mini 4 and 223 for Granite 4.2 3B, both measured by the same lab, with time-to-first-chunk of 1.5 s and 0.5 s respectively. The dense model is the faster one on both counts.

What the fifteen-point gap is actually made of

Individual evaluations tell a less flattering story than the headline for Granite 4.2 3B, and a more complicated one for Solar Mini 4. On AA-LCR v1.1, the long-context reasoning benchmark, Solar Mini 4 scores 83% and Granite 4.2 3B scores 24%. That single evaluation accounts for an enormous share of the index difference, and it is not an accident of scale — it is what you buy with 512K to 1M tokens of context and the training to use it. Granite 4.2 3B's window tops out at 131K natively; the extended 512K phase exists, but the model was not tuned to reason across it the way Solar Mini 4 was.

On general knowledge the gap narrows and then inverts in character. Granite 4.2 3B scores 55.9% on GPQA, and Solar Mini 4 has no GPQA figure at all; on Humanity's Last Exam the two are 6.6% and 25.8% respectively, which is the more direct comparison and the one the size difference predicts. What Granite 4.2 3B does not do, according to Artificial Analysis, is fabricate: its AA-Omniscience non-hallucination rate is 73.7%, higher than Solar Mini 4's 64.2%. The smaller model is less likely to invent an answer it does not have.

Agentic coding is where both models fall down, and where reading the numbers matters. Solar Mini 4 scores 1% on Terminal-Bench 4.0 and 22.3% on AutomationBench-AA. Granite 4.2 3B scores 0% on Terminal-Bench 4.0, 13.9% on the older Terminal-Bench 2.1 and 5.6% on τ³-Banking. Neither model is the right instrument for autonomous terminal work. The 8B and 30B members of the Granite 4.2 family got IBM's agentic reinforcement learning and carry SWE-Bench and Terminal-Bench results; the 3B explicitly skipped that training block, and Upstage's model is priced for retrieval, structuring and policy application rather than for driving a shell.

Where Granite 4.2 3B is still the correct answer

Seven and a third gigabytes of weights in bfloat16, under four gigabytes at a practical quantization, and an Apache 2.0 licence that lets you fine-tune it on your own data and ship the result without asking anyone. A student with one consumer GPU, a hospital that cannot send patient text to a third party, a product that needs to work on an aircraft or in a factory basement, a team that wants to own a model rather than rent an endpoint — every one of those cases picks Granite 4.2 3B and does not look back. The engine runs through vLLM, SGLang, Transformers and GGUF, and Ollama lists a granite4.2:3b build.

Its vendor-reported numbers deserve the usual caution. IBM's card claims 78.33 on AIME 2025, 66.67 on HMMT February 2025 and 69.71 on LiveCodeBench v6 — all unreproduced by any third party, and for a dense 3B model the AIME figure sits far above the historical range. The Artificial Analysis index of 9 registers the model as genuinely useful and nowhere near the frontier, which is the more trustworthy signal precisely because IBM did not publish it.

Where Solar Mini 4 is the correct answer

Anything where the work is a long document, a repeated structured extraction, or a policy that has to be applied consistently at volume — and where ninety seconds of latency is acceptable. Solar Mini 4's profile is a specialist's profile: 83% on long-context reasoning, 48% on scientific coding, 64% non-hallucination, and a documented tendency to abstain rather than guess. It also speaks Korean as a first-class language alongside English and Japanese, which for a Korean-market deployment is not a footnote.

What buyers should not do is compare the two token prices and conclude that Solar Mini 4 is three times more expensive. Artificial Analysis measured Solar Mini 4 at $0.36 per completed Intelligence Index task — and, on the same suite, Granite 4.2 3B at $0.006. That is a factor of sixty, driven by the same things that inflate Solar Mini 4 against GPT-6 Luna (max): 88,300 output tokens per task against Granite's 18,600, and a cost structure in which prompt-cache writes account for $0.30 of Solar Mini 4's $0.36 against $0.0006 of Granite's $0.006. A cheap output price on a verbose model is not a cheap task.

Neither model is on OrcaRouter — we route Granite and Upstage neither — so if you want Granite 4.2 3B it comes from Hugging Face, and if you want Solar Mini 4 it comes from Upstage's own API and third-party platforms. But the two-model pattern this comparison suggests is one routers are built for, and it is the reason OrcaRouter puts more than 200 models behind a single key at 0% markup over provider list price: run the long-document and Korean-language work against whatever model actually earns its cost, keep the deterministic extraction steps on something you host, and let routing and failover move a request between them per call rather than per contract.

One final note on the numbers above. Every Solar Mini 4 figure that comes from Artificial Analysis is an independent measurement; every Granite 4.2 3B figure from IBM's model card is a vendor claim that no third party has reproduced. The Artificial Analysis index scores of 24 and 9 are the only two numbers here that were produced by the same lab under the same conditions — and they are the two to build a decision on.