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Put two chips, runtimes, or runtime versions side by side. Everything else is held fixed or called out, so a ratio is worth exactly what the matched runs say.

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Runs compared: 156
Like-for-like pairs: 13
Chips available: 16

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Side A

Side B

Model

Runtime

Runtime version

Quantisation

Backend

Prefill size

Decode size

Cooldown

27 runs vs 129 runs · 13 like-for-like pairs

Held fixedPrefill: PP512Decode: TG128

These figures mix different models. Pick a model for a like-for-like comparison.
A side mixes quantisations. Pick a quantisation to compare the same weights.
The sides differ in decode workload. Ratios may reflect benchmark protocol rather than the chip.

Chip comparison

Apple M5 Max vs Apple M5 Pro

Ratios read side A relative to side B. Geometric mean over 13 like-for-like pairs.

Decode: 1.48×+47.6%
Prefill: 1.71×+71.1%

Side A

Apple M5 Max

27 runs

2 contributors

Latest 18 Sep 2026

Decode · TG128: 169.0tok/s median
best 708.3 · worst 15.7
Prefill · PP512: 4,821tok/s median
best 34,136 · worst 589

Models: gemma-3-1b-it, Gemma-4 12B IT (smart Q4_0, QAT-lossless), gemma-4-26B-A4B-it, gemma-4-E2B-it, gemma-4-E4B-it, gpt-oss-120b, gpt-oss-20b, Models Qwen Qwen3 0.6B, Muse-Glimmer-30B, NVIDIA-Nemotron-3-Nano-30B-A3B, Qwen3 0.6B Instruct, Qwen3-0.6B, Qwen3.6-27B, Qwen3.6-35B-A3B, Qwen3.8-27B
Runtimes: BaseRTllama.cpp
Versions: BaseRT 0.2.4, BaseRT 0.2.5, llama.cpp b10360 (48d22e295), llama.cpp b10901 (28ff09582)
Quantisations: passthrough_gguf, Q4, Q4_0, Q4_K_M, Q8, Q8_0
Backends: BLAS + Metal, Metal
Cooldown: off, on
Conditioning: idle_reset_then_warm, runtime_native_warmup, warmup_only
Decode workload: TG128
Harness schema: basert-benchmark-harness/1, computearena-measurements/1

Side B

Apple M5 Pro

129 runs

2 contributors

Latest 18 Sep 2026

Decode · TG128: 72.8tok/s median
best 2,068.0 · worst 10.6
Prefill · PP512: 1,852tok/s median
best 190,107 · worst 327

Models: gemma-3-1b-it, gemma-4-26B-A4B-it, gemma-4-E2B-it, gemma-4-E4B-it, Gemma-4-E4B-It, gpt-oss-20b, Gpt-Oss-20B, Llama 3.2 3B Instruct, Llama-3.1-8B-Instruct, Llama-3.2-1B-Instruct, Llama-3.2-3B-Instruct, Mistral-7B-Instruct-v0.3, Muse-Glimmer-30B, NVIDIA-Nemotron-3-Nano-30B-A3B, Qwen3-0.6B, Qwen3-1.7B, Qwen3-30B-A3B-Instruct-2507, Qwen3-30B-A3B-Thinking-2507, Qwen3-4B, Qwen3-4B-Instruct-2507, Qwen3-4B-Thinking-2507, Qwen3-8B, Qwen3-8B-base-q2, Qwen3-8B-base-q3, Qwen3-8B-base-q5, Qwen3-8B-base-q6, Qwen3.5-2B, Qwen3.5-2B-Base, Qwen3.5-35B-A3B, Qwen3.6-27B, Qwen3.6-35B-A3B, Qwen3.8-27B, Tinystories Lay8 Hs512 Hd8 33M, tinystories-lay8-hs512-hd8-33M
Runtimes: BaseRTllama.cpp
Versions: BaseRT 0.2.4, BaseRT 0.2.5, llama.cpp b10809 (5266f24da), llama.cpp b9960 (a935fbffe)
Quantisations: BF16, F16, mxfp4, passthrough_gguf, Q2, Q2_K, Q3, Q3_K_M, Q4, Q4_0, Q4_1, Q4_K_M, Q5, Q5_K_M, Q6, Q6_K, Q8, Q8_0
Backends: BLAS + Metal, Metal
Cooldown: off, on
Conditioning: idle_reset_then_warm, runtime_native_warmup, warmup_only
Decode workload: TG128, TG128 @ 1 ctx
Harness schema: basert-benchmark-harness/1, computearena-measurements/1

What differs besides the chip

Facet	Apple M5 Max	Apple M5 Pro
Model	gemma-3-1b-it, Gemma-4 12B IT (smart Q4_0, QAT-lossless), gemma-4-26B-A4B-it, gemma-4-E2B-it, gemma-4-E4B-it, gpt-oss-120b, gpt-oss-20b, Models Qwen Qwen3 0.6B, Muse-Glimmer-30B, NVIDIA-Nemotron-3-Nano-30B-A3B, Qwen3 0.6B Instruct, Qwen3-0.6B, Qwen3.6-27B, Qwen3.6-35B-A3B, Qwen3.8-27B	gemma-3-1b-it, gemma-4-26B-A4B-it, gemma-4-E2B-it, gemma-4-E4B-it, Gemma-4-E4B-It, gpt-oss-20b, Gpt-Oss-20B, Llama 3.2 3B Instruct, Llama-3.1-8B-Instruct, Llama-3.2-1B-Instruct, Llama-3.2-3B-Instruct, Mistral-7B-Instruct-v0.3, Muse-Glimmer-30B, NVIDIA-Nemotron-3-Nano-30B-A3B, Qwen3-0.6B, Qwen3-1.7B, Qwen3-30B-A3B-Instruct-2507, Qwen3-30B-A3B-Thinking-2507, Qwen3-4B, Qwen3-4B-Instruct-2507, Qwen3-4B-Thinking-2507, Qwen3-8B, Qwen3-8B-base-q2, Qwen3-8B-base-q3, Qwen3-8B-base-q5, Qwen3-8B-base-q6, Qwen3.5-2B, Qwen3.5-2B-Base, Qwen3.5-35B-A3B, Qwen3.6-27B, Qwen3.6-35B-A3B, Qwen3.8-27B, Tinystories Lay8 Hs512 Hd8 33M, tinystories-lay8-hs512-hd8-33M
Runtime version	BaseRT 0.2.4, BaseRT 0.2.5, llama.cpp b10360 (48d22e295), llama.cpp b10901 (28ff09582)	BaseRT 0.2.4, BaseRT 0.2.5, llama.cpp b10809 (5266f24da), llama.cpp b9960 (a935fbffe)
Quantisation	passthrough_gguf, Q4, Q4_0, Q4_K_M, Q8, Q8_0	BF16, F16, mxfp4, passthrough_gguf, Q2, Q2_K, Q3, Q3_K_M, Q4, Q4_0, Q4_1, Q4_K_M, Q5, Q5_K_M, Q6, Q6_K, Q8, Q8_0
Decode workload	TG128	TG128, TG128 @ 1 ctx

Like-for-like pairs

Each row is a configuration present on both sides. Values are per-cell medians; ratios read Apple M5 Max relative to Apple M5 Pro.

Configuration	Decode A	Decode B	Ratio	Prefill A	Prefill B	Ratio	Runs A / B
NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 BaseRTQ4	179.2	100.1	1.79×	4,942	2,499	1.98×	6 / 1
Qwen3.8-27B BaseRTQ4	21.5	13.5	1.59×	590	341	1.73×	5 / 2
Qwen3-0.6B BaseRTQ4	703.4	512.0	1.37×	33,557	20,603	1.63×	2 / 3
gemma-3-1b-it BaseRTQ4	414.9	281.7	1.47×	21,395	14,392	1.49×	2 / 2
Qwen3-0.6B BaseRTQ8	549.5	349.8	1.57×	33,088	20,366	1.62×	1 / 3
Qwen3-0.6B llama.cppQ4_K_M	439.1	358.1	1.23×	24,365	14,509	1.68×	1 / 3
gemma-4-E4B-it BaseRTQ4	121.1	82.3	1.47×	8,156	4,646	1.76×	1 / 2
gpt-oss-20b BaseRTQ4	169.0	110.5	1.53×	2,179	1,156	1.89×	1 / 2
Muse-Glimmer-30B BaseRTpassthrough_gguf	15.7	15.7	1.00×	830	480	1.73×	1 / 2
gemma-4-26B-A4B-it BaseRTQ4	92.0	51.7	1.78×	4,055	2,047	1.98×	1 / 1
gemma-4-E2B-it BaseRTQ4	197.5	144.6	1.37×	20,099	13,240	1.52×	1 / 1
Qwen3-0.6B llama.cppQ8_0	379.0	283.0	1.34×	24,983	14,942	1.67×	1 / 1
Qwen3.6-27B BaseRTQ4	31.3	16.1	1.94×	603	364	1.65×	1 / 1

Side A · Apple M5 Max

Top 25 of 27 runs by decode throughput.

Model / format	Chip / backend	Decode	Prefill	Contributor	Date	Report
basecompute/Qwen3-0.6B BaseRT 0.2.4Q4	Apple M5 Max Metal	708.3 TG128	34,136 PP512	basecompute	10 Sep 2026	View Benchmark
basecompute/Qwen3-0.6B BaseRT 0.2.5Q4	Apple M5 Max Metal	698.5 TG128	32,977 PP512	lukas	18 Sep 2026	View Benchmark
basecompute/Qwen3-0.6B BaseRT 0.2.4Q8	Apple M5 Max Metal	549.5 TG128	33,088 PP512	basecompute	10 Sep 2026	View Benchmark
Models Qwen Qwen3 0.6B llama.cpp b10360 (48d22e295)Q4_K_M	Apple M5 Max BLAS + Metal	439.1 TG128	24,365 PP512	basecompute	10 Sep 2026	View Benchmark
basecompute/gemma-3-1b-it BaseRT 0.2.5Q4	Apple M5 Max Metal	415.5 TG128	19,770 PP512	lukas	17 Sep 2026	View Benchmark
basecompute/gemma-3-1b-it BaseRT 0.2.5Q4	Apple M5 Max Metal	414.2 TG128	23,019 PP512	lukas	18 Sep 2026	View Benchmark
Qwen3 0.6B Instruct llama.cpp b10360 (48d22e295)Q8_0	Apple M5 Max BLAS + Metal	379.0 TG128	24,983 PP512	basecompute	10 Sep 2026	View Benchmark
basecompute/gemma-4-E2B-it BaseRT 0.2.4Q4	Apple M5 Max Metal	197.5 TG128	20,099 PP512	lukas	15 Sep 2026	View Benchmark
basecompute/NVIDIA-Nemotron-3-Nano-30B-A3B BaseRT 0.2.4Q4	Apple M5 Max Metal	184.4 TG128	4,984 PP512	lukas	11 Sep 2026	View Benchmark
basecompute/NVIDIA-Nemotron-3-Nano-30B-A3B BaseRT 0.2.4Q4	Apple M5 Max Metal	183.7 TG128	4,941 PP512	lukas	11 Sep 2026	View Benchmark
basecompute/NVIDIA-Nemotron-3-Nano-30B-A3B BaseRT 0.2.4Q4	Apple M5 Max Metal	180.8 TG128	4,951 PP512	lukas	11 Sep 2026	View Benchmark
basecompute/NVIDIA-Nemotron-3-Nano-30B-A3B BaseRT 0.2.4Q4	Apple M5 Max Metal	177.6 TG128	4,527 PP512	basecompute	10 Sep 2026	View Benchmark
basecompute/NVIDIA-Nemotron-3-Nano-30B-A3B BaseRT 0.2.4Q4	Apple M5 Max Metal	177.0 TG128	4,821 PP512	lukas	11 Sep 2026	View Benchmark
basecompute/gpt-oss-20b BaseRT 0.2.4Q4	Apple M5 Max Metal	169.0 TG128	2,179 PP512	lukas	11 Sep 2026	View Benchmark
basecompute/gemma-4-E4B-it BaseRT 0.2.5Q4	Apple M5 Max Metal	121.1 TG128	8,156 PP512	lukas	17 Sep 2026	View Benchmark
basecompute/Qwen3.6-35B-A3B BaseRT 0.2.4Q8	Apple M5 Max Metal	119.6 TG128	1,540 PP512	lukas	10 Sep 2026	View Benchmark
basecompute/gpt-oss-120b BaseRT 0.2.4Q4	Apple M5 Max Metal	114.3 TG128	1,401 PP512	lukas	11 Sep 2026	View Benchmark
basecompute/NVIDIA-Nemotron-3-Nano-30B-A3B BaseRT 0.2.4Q4	Apple M5 Max Metal	109.2 TG128	4,943 PP512	lukas	11 Sep 2026	View Benchmark
basecompute/gemma-4-26B-A4B-it BaseRT 0.2.4Q4	Apple M5 Max Metal	92.0 TG128	4,055 PP512	lukas	10 Sep 2026	View Benchmark
Gemma-4 12B IT (smart Q4_0, QAT-lossless) llama.cpp b10901 (28ff09582)Q4_0	Apple M5 Max BLAS + Metal	54.2 TG128	1,777 PP512	lukas	10 Sep 2026	View Benchmark
basecompute/Qwen3.8-27B BaseRT 0.2.4Q4	Apple M5 Max Metal	34.0 TG128	589 PP512	basecompute	10 Sep 2026	View Benchmark
basecompute/Qwen3.6-27B BaseRT 0.2.4Q4	Apple M5 Max Metal	31.3 TG128	603 PP512	basecompute	11 Sep 2026	View Benchmark
basecompute/Qwen3.8-27B BaseRT 0.2.4Q4	Apple M5 Max Metal	21.5 TG128	590 PP512	lukas	15 Sep 2026	View Benchmark
basecompute/Qwen3.8-27B BaseRT 0.2.4Q4	Apple M5 Max Metal	21.5 TG128	594 PP512	lukas	15 Sep 2026	View Benchmark
basecompute/Qwen3.8-27B BaseRT 0.2.4Q4	Apple M5 Max Metal	20.7 TG128	599 PP512	lukas	15 Sep 2026	View Benchmark

Side B · Apple M5 Pro

Top 25 of 129 runs by decode throughput.

Model / format	Chip / backend	Decode	Prefill	Contributor	Date	Report
ivnle/tinystories-lay8-hs512-hd8-33M BaseRT 0.2.4Q4	Apple M5 Pro Metal	2,068.0 TG128	190,107 PP512	arki05	17 Sep 2026	View Benchmark
Tinystories Lay8 Hs512 Hd8 33M llama.cpp b10809 (5266f24da)Q4_0	Apple M5 Pro BLAS + Metal	1,443.3 TG128	124,181 PP512	arki05	17 Sep 2026	View Benchmark
basecompute/Qwen3-0.6B BaseRT 0.2.4Q4	Apple M5 Pro Metal	516.8 TG128	20,778 PP512	arki05	11 Sep 2026	View Benchmark
basecompute/Qwen3-0.6B BaseRT 0.2.4Q4	Apple M5 Pro Metal	512.0 TG128	20,603 PP512	arki05	11 Sep 2026	View Benchmark
basecompute/Qwen3-0.6B BaseRT 0.2.5Q4	Apple M5 Pro Metal	481.5 TG128 @ 1 ctx	19,595 PP512	isu	18 Sep 2026	View Benchmark
Qwen3-0.6B llama.cpp b10809 (5266f24da)Q4_K_M	Apple M5 Pro BLAS + Metal	358.3 TG128	14,509 PP512	arki05	11 Sep 2026	View Benchmark
Qwen3-0.6B llama.cpp b10809 (5266f24da)Q4_K_M	Apple M5 Pro BLAS + Metal	358.1 TG128	14,344 PP512	arki05	11 Sep 2026	View Benchmark
Qwen3-0.6B llama.cpp b10809 (5266f24da)Q4_K_M	Apple M5 Pro BLAS + Metal	356.2 TG128	14,552 PP512	arki05	11 Sep 2026	View Benchmark
basecompute/Qwen3-0.6B BaseRT 0.2.4Q8	Apple M5 Pro Metal	350.2 TG128	20,537 PP512	arki05	11 Sep 2026	View Benchmark
basecompute/Qwen3-0.6B BaseRT 0.2.4Q8	Apple M5 Pro Metal	349.8 TG128	20,366 PP512	arki05	11 Sep 2026	View Benchmark
basecompute/Qwen3-0.6B BaseRT 0.2.4Q8	Apple M5 Pro Metal	311.0 TG128	18,474 PP512	arki05	11 Sep 2026	View Benchmark
basecompute/gemma-3-1b-it BaseRT 0.2.4Q4	Apple M5 Pro Metal	293.1 TG128	15,066 PP512	arki05	11 Sep 2026	View Benchmark
Qwen3-0.6B llama.cpp b10809 (5266f24da)Q8_0	Apple M5 Pro BLAS + Metal	283.0 TG128	14,942 PP512	arki05	11 Sep 2026	View Benchmark
basecompute/gemma-3-1b-it BaseRT 0.2.4Q4	Apple M5 Pro Metal	270.4 TG128	13,717 PP512	arki05	11 Sep 2026	View Benchmark
basecompute/Qwen3-1.7B BaseRT 0.2.4Q4	Apple M5 Pro Metal	235.4 TG128	8,040 PP512	arki05	11 Sep 2026	View Benchmark
basecompute/Llama-3.2-1B-Instruct BaseRT 0.2.4Q4	Apple M5 Pro Metal	229.1 TG128	11,673 PP512	arki05	11 Sep 2026	View Benchmark
basecompute/Qwen3.5-2B-Base BaseRT 0.2.4Q4	Apple M5 Pro Metal	218.2 TG128	2,645 PP512	arki05	11 Sep 2026	View Benchmark
basecompute/Qwen3.5-2B BaseRT 0.2.4Q4	Apple M5 Pro Metal	217.7 TG128	2,641 PP512	arki05	11 Sep 2026	View Benchmark
basecompute/Qwen3-1.7B BaseRT 0.2.4Q4	Apple M5 Pro Metal	210.3 TG128	7,268 PP512	arki05	11 Sep 2026	View Benchmark
basecompute/gemma-3-1b-it BaseRT 0.2.4Q8	Apple M5 Pro Metal	210.3 TG128	15,061 PP512	arki05	11 Sep 2026	View Benchmark
basecompute/Llama-3.2-1B-Instruct BaseRT 0.2.4Q4	Apple M5 Pro Metal	206.3 TG128	10,528 PP512	arki05	11 Sep 2026	View Benchmark
basecompute/Llama-3.2-1B-Instruct BaseRT 0.2.4Q4	Apple M5 Pro Metal	205.3 TG128	12,067 PP512	arki05	11 Sep 2026	View Benchmark
basecompute/gemma-4-E2B-it BaseRT 0.2.4Q4	Apple M5 Pro Metal	144.6 TG128	13,240 PP512	arki05	11 Sep 2026	View Benchmark
basecompute/Qwen3-1.7B BaseRT 0.2.4Q8	Apple M5 Pro Metal	139.7 TG128	8,043 PP512	arki05	11 Sep 2026	View Benchmark
basecompute/Qwen3.5-2B-Base BaseRT 0.2.4Q8	Apple M5 Pro Metal	131.4 TG128	2,642 PP512	arki05	11 Sep 2026	View Benchmark