Nanbeige4.3-33B vs Gemma 4 26B-A4B: A Teacher With No Endpoint Against a Workhorse at Six Cents

Comparing Nanbeige4.3-33B with Gemma 4 26B-A4B on a benchmark is the wrong first move, and the reason is not that the numbers are close or far apart. It is that only one of these two models has a price. Gemma 4 26B-A4B is a hosted model on OrcaRouter at $0.06 per million input and $0.33 per million output tokens, with a 262,144-token window and a p50 time-to-first-token of 826 milliseconds. Nanbeige4.3-33B has no endpoint, no rate card and no inference provider listed on its repository — its cost is four GPUs and your electricity bill.

So the real question is not which model scores higher. It is whether the gap in capability between a 33-billion-parameter dense teacher and a 26-billion-parameter mixture-of-experts that activates 3.8 billion parameters per token is worth moving from a metered line item to a capital one. That is a question you can answer with arithmetic, and the arithmetic is less flattering to the bigger model than the benchmark table suggests.

The index score does not have a partner number

Nanbeige4.3-33B sits at 20 on the Artificial Analysis Intelligence Index v5.0.1. Gemma 4 26B-A4B has no v5 score at all: its Artificial Analysis page lists the composite as N/A, states "Unknown out of 4 units for Intelligence," and notes there is no publicly available API to run the current harness against. Whatever it scored on an earlier index belongs to a rubric that no longer exists.

Those two facts cannot be subtracted from each other. The v5 index is a different, rebased evaluation set from its predecessors, and a model that scored well under the old methodology would not necessarily land anywhere near the same place under the new one. Anyone showing you a "20 versus whatever Gemma scored last year" comparison is comparing two different exams. The honest statement is that Gemma 4 26B-A4B is unmeasured on the current composite and Nanbeige4.3-33B is measured at 20, and until both appear on v5 there is no index delta to quote.

What does exist on a shared harness is Terminal-Bench. Artificial Analysis ran Gemma 4 26B-A4B at 39.0% on Terminal-Bench 2.1. It ran Nanbeige4.3-33B at 29.8% on Terminal-Bench 4.0 — a different and harder benchmark, so again not a delta. Nanbeige's own card claims 80.9 on Terminal-Bench 2.1 for Nanbeige4.3-33B against 73.0 for Qwen3.8-27B, but that row is vendor-reported with a Terminus 2 harness and has not been independently reproduced. Read it as "Nanbeige believes it is strong here," not as a number.

What each model is actually shaped to do

The architecture tells you more than the benchmarks do, because the two designs are optimising for opposite constraints.

• Shape — Nanbeige4.3-33B is 33.1B parameters, all of them dense, activated on every token. Gemma 4 26B-A4B is 25.2B total with roughly 3.8B active per token, a 6.6:1 ratio.

• Memory for weights — 66.3 GB at bfloat16 for Nanbeige4.3-33B across fourteen shards. Gemma 4 26B-A4B fits in roughly 18 GB at 4-bit, which is a single consumer card.

• Context — 524,288 tokens native for Nanbeige4.3-33B, no scaling applied, versus 262,144 for Gemma 4 26B-A4B. Both are served at 262K on OrcaRouter for the Gemma model.

• Modality — text in, text out, English and Chinese, for Nanbeige4.3-33B. Gemma 4 26B-A4B takes text, image and video in and returns text, which ends the comparison for any pipeline that reads screenshots.

• Licence — Apache 2.0 on both, so the legal position is identical and the decision is purely technical and economic.

• Reasoning — both ship with thinking on by default and both expose a way to turn it down. Nanbeige4.3-33B recommends a 262,144-token ceiling for reasoning and 65,536 for agentic work; Gemma 4 26B-A4B caps output far lower in practice, around 33K tokens on most routes.

That last pair is the crux. The Nanbeige model is built to run long — long terminal sessions, long agent trajectories, long reasoning chains — and every one of those tokens costs you GPU time at dense-model rates. The Gemma model is built to be cheap per token, which is why Google can ship it at 3.8B active and why it costs six cents to start a conversation.

A worked example, because this is a cost decision

Take a task that reads 20,000 tokens of input and produces 3,000 tokens of output, and run it a thousand times a day.

On Gemma 4 26B-A4B at OrcaRouter list, each call costs 20,000 × $0.06 per million for input — $0.0012 — plus 3,000 × $0.33 per million for output — $0.00099. That is $0.00219 per call, or $2.19 per thousand calls, or about $66 a month at that volume.

On Nanbeige4.3-33B there is no per-call price to compute, because there is no call. You need hardware. At bfloat16 the weights alone are 66.3 GB, which does not fit on a single 80 GB card alongside a useful KV cache once the context grows: each token in the cache costs about 208 KiB, so a 32K-token conversation adds roughly 6.5 GiB per sequence. Four-way tensor parallelism is the configuration Nanbeige's own card assumes, and that is the floor for a serious deployment, before you count the second node you need for redundancy.

Put those side by side and the honest conclusion is that the two models are not substitutes at low volume. Below a few million tokens a day, Gemma 4 26B-A4B is cheaper by an order of magnitude, and the agentic gap would have to be enormous to close it. Above that volume, the fixed cost of self-hosting amortises and the comparison becomes real — which is exactly when the 33B's long-horizon strength starts to matter.

Where the dense model earns its keep

The case for Nanbeige4.3-33B is not "it is better." It is "it does a specific thing that the cheap model does badly," and the independent numbers point at which thing.

Terminal-Bench 4.0 at 29.8% is, per Artificial Analysis's own board, the 56th-best result of any configuration on that benchmark. That is not a headline number. It is, however, roughly five times what Qwen3.8-27B manages at its highest reasoning setting on the same harness, and it is a benchmark built around long-horizon terminal work — the kind of task where a model that gives up early scores zero regardless of how well it reasons.

The same shape shows up in the calibration data. Nanbeige4.3-33B scores 11.7% accuracy on AA-Omniscience but 81.8% non-hallucination, seventh on the entire site. In an agent loop, a model that says "I don't know" instead of inventing a path is worth more than one that is five points better on trivia and fabricates when it runs out of road. Gemma 4 26B-A4B's equivalent calibration figures are not published on the current composite, so this is a claim about Nanbeige, not a comparison.

The part that decides it for most teams

Both models can be reached through one key if you want to test the argument rather than read it. Gemma 4 26B-A4B is in the OrcaRouter catalogue at the price above, with a 262K window and zero-data-retention routing available on the same endpoint. Nanbeige4.3-33B is not in the catalogue — we do not host it, and nothing here should be read as a claim that we do.

What that asymmetry means in practice is that the cheap side of this comparison is a five-minute experiment and the expensive side is a procurement. If your workload is multimodal, or your volume is under a few million tokens a day, or you need an answer this week, Gemma 4 26B-A4B wins on availability alone and the benchmark question never gets asked.

If your workload is long-horizon terminal automation, you already know that a 4B-active model is going to drop the thread somewhere in hour two, and the 29.8% is the number you care about. Then the question becomes whether you can afford four GPUs, and no benchmark can answer that for you.