Nanbeige4.3 33B vs Muse Glimmer 30B

Analysis

Nanbeige4.3 33B and Muse Glimmer 30B are both dense, Apache 2.0 coding-agent models in the 30B class, released ten weeks apart (Muse Glimmer from Meta on 10 August 2026, Nanbeige on 24 October 2026). Nanbeige's card names Muse-Glimmer-30B as one of its three rivals. The pair is worth a page for two reasons: the models make opposite bets on long context, and the card re-measures Muse Glimmer instead of quoting Meta's figures, so there are two sets of numbers for the same model.

Start with memory, where they are opposites. Muse Glimmer keeps 13 of its 52 layers on full attention and caps the other 39 at a 2,048-token window, with only 2 KV heads, so its cache barely grows: 1.8 GB at its 131,072-token ceiling. Nanbeige keeps a full cache in all 52 layers (8 KV heads), which is 27.9 GB at the same length, about 15 times as much. Q4_K_M weights are 20.2 GB against 16.9 GB, so the totals at 131,072 tokens are 53.8 GB for Nanbeige and 21.0 GB for Muse Glimmer. On a 24 GB RTX 3090 or RTX 4090, Muse Glimmer runs at Q4_K_M with its whole 131,072-token window, while Nanbeige misses Q4_K_M even at an 8k context and needs Q3_K_M. Muse Glimmer's window ends at 131,072 tokens and Nanbeige's runs to 524,288. Speed follows the weights, since both are dense: this site estimates 71.5 tok/s for Muse Glimmer against 58.8 for Nanbeige on an RTX 5090 at Q4_K_M. On benchmarks, the fields this site tracks (each vendor's own figures) put Nanbeige ahead: GPQA Diamond 89.5 against 83.5, SWE-Bench Pro 60.3 against 51.2 and Terminal-Bench 2.1 80.9 against 51.7. Muse Glimmer also reports SWE-bench Verified 76.0, which Nanbeige's card does not. The second set of numbers is Nanbeige's own measurement of Muse Glimmer, and it disagrees with Meta's in both directions: Terminal-Bench 2.1 61.8, which is 10.1 points above Meta's figure, and SWE-Bench Pro 36.9, which is 14.3 below it, while GPQA Diamond matches at 83.5. The same table has Muse Glimmer far behind on long agent tasks (DeepSWE 65.5 against 4.4, Terminal-Bench 4.0 23.8 against 1.6) and close on reasoning (AIME 2026 95.2 against 94.7). A score of 4.4 on one agent suite beside Meta's 76.0 on SWE-bench Verified suggests the scaffold matters as much as the model, so treat the agentic margin as an upper bound. Muse Glimmer takes image input through a separate encoder file where Nanbeige is text only. Muse Glimmer's Ollama tag in this site's data (muse-glimmer:30b-mlx) runs on Apple's MLX engine and Nanbeige has no library tag, so llama.cpp or vLLM is the common route for both off Apple hardware.

Bottom line: Pick Nanbeige4.3 33B for agent quality: it leads on every figure both vendors report, by wide margins on long agent tasks, and it reaches 524,288 tokens, if you have a 32 GB card, a 48 GB workstation card or a 64 GB Mac. Pick Muse Glimmer 30B if you are on a 24 GB card, want its full 131,072-token window at Q4_K_M (21.0 GB in total), need image input, or want about a fifth more speed. How large Nanbeige's lead really is depends on the harness, so test both on your own repository.