Granite 4.0 Micro

Not independently scored by Artificial Analysis

IBM's smallest Granite 4, Apache-2.0, with 128K of context and attention in all 40 layers over 8 key-value heads. That last detail is what decides whether it fits: a dense attention stack makes the KV cache grow fast, so the context you can actually serve is smaller than the headline suggests.

BigCodeBench-Hard pass@1 16% (24/148), via official protocol · llama.cpp BF16 · 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.

Each quant links to the exact file it was measured from, in unsloth/granite-4.0-micro-GGUF. 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.

87.6% ± 2.2 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.

All 13 RULER tasks, one request at a time, KV cache f16, bench commit c3f5e3b4, seed 42. The score is the mean of the 13; the margin is two standard deviations of the sampling, so it grows as the sample shrinks and as the score drops.

This is not a coding score and must not be read as one. RULER asks whether the model finds and follows a fact buried in a long text. On this model Q2_K scores 55.7 at 32K against 71.0 for Q8_0, a difference of 15.2 points — while on BigCodeBench-Hard the same two files score 3.4% and 16.2%. A page that read the long-context number as a general score would be telling the truth and misleading you.

Measured across its curve on BigCodeBench-Hard, and it holds up better than its size suggests: 16.2% at BF16, 18.2% at Q6_K, still 16.2% at Q4_K_M. The cliff is not where you would guess - Q4_0 drops to 9.5%, roughly half, while the K-quant at the same bit width keeps its score. That is a packaging difference, not a precision one, and it is the practical argument for preferring K-quants over the legacy Q4_0 build. A permissive licence makes it a sensible on-prem option where the model licence matters.