MiniCPM5-2B

AA coding index 14.5 (Artificial Analysis, frozen since 2026-09-11)

Released 2026-09-06 by OpenBMB, Apache-2.0, and the most downloaded new text-generation model on the Hub the week it landed. A dense 2B built for on-device use: 42 layers, hidden size 2048, 16 attention heads over 2 key-value heads, 128K of context. The practical point is the architecture id - it declares itself LlamaForCausalLM, so every GGUF loader already knows it. No PR branch, no waiting for upstream. OpenBMB publishes the GGUF itself, in three builds: F16 at 5.0 GB, Q8_0 at 2.7 and Q4_K_M at 1.6.

BigCodeBench-Hard pass@1 20% (29/148), via official protocol · llama.cpp F16 · 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 openbmb/MiniCPM5-2B-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.

52.5% ± 3.1 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.

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 Q4_K_M scores 81.4 at 32K against 81.6 for Q8_0, a difference of 0.1 points — while on BigCodeBench-Hard the same two files score 15.5% and 21.6%. A page that read the long-context number as a general score would be telling the truth and misleading you.

Artificial Analysis scores it 14.5 on the coding index and 13.1 on intelligence, which is what a 2B looks like next to the rest of this page - it is on here because it runs anywhere, not because it codes well. Nobody serves it on OpenRouter, so there is no hosted price to compare against. The vendor claims it is the best of the 2B class and competitive with 4B models on coding, maths, long context and tool use; those are self-reported and we have not reproduced any of them. Our own number is pending: it is small enough to measure on a 16 GB card with room to spare.