Gemma 4 31B

AA coding index 43.4 (Artificial Analysis, frozen since 2026-09-11)

The largest dense Gemma 4 — a strong general model just inside the 32 GB tier. Gated on Hugging Face.

BigCodeBench-Hard pass@1 19% (28/148), via official protocol · llama.cpp Q8_0 · one request at a time · a shared cluster GPU · mean of 3 runs, range 0.0 pp. The brutal counterpart to HumanEval — where HumanEval saturates near the top, BCB-Hard spreads the field, so this is the number that actually separates coding ability. does not fit the token budget; repeats itself until it runs out; 14 of 148 answers hit the token limit (effective ceiling 91%)

We have not been able to tie these files back to a published repo, so take the quant names as the labels we ran them under. Every point measured on the same class of card, one pass, one request at a time. One problem is 0.68 points, so anything under ~3.4 points apart is a tie — that is the spread we measure between GPU classes, and our rows are not all on the same card. Each card column is the context left for the KV cache after the weights, KV-Q4 / KV-Q8. A dash means the file does not fit that card, or fits with no room left to work. Context is usually the real constraint, not quality. Computed for one slot (--parallel 1), which is what you get serving yourself. llama-server opens four by default, and on a hybrid that costs real context — the table below each curve shows how much. The card figures assume a dedicated card. Measured on an idle one: the driver keeps 623 MiB of 143,771, so 99.6% is usable. Compute buffers are measured too — a fixed ~130 MiB plus 1 MiB per 1K of context, the same slope on every architecture we checked. If your card also runs your desktop you get noticeably less, and we have not measured that case yet.

88.0% ± 2.1 over 1390 problems

Q4_K_M · a shared cluster GPU · KV f16 · 1 run. This is the non-agentic half of BFCL: does the model call the right function with the right arguments. It says nothing about how it behaves over a long agentic conversation, which is a separate measurement. The score is BFCL’s own: the unweighted mean of four groups (simple, multiple, parallel, parallel multiple), where simple is itself the mean of Python, Java and JavaScript. Every group counts the same whatever its size, and irrelevance detection — which we also measure, another 240 problems — is not part of it. The margin follows that same weighting.

~20 GB at Q4 — a 32 GB card, or a 24 GB one at a tighter quant / short context. Excellent on Apple Silicon via MLX.