Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5
77
Ling 2.6 Flash
44
Verified leaderboard positions: GLM-5 #13 · Ling 2.6 Flash unranked
Pick GLM-5 if you want the stronger benchmark profile. Ling 2.6 Flash only becomes the better choice if you need the larger 262K context window.
Coding
+36.2 difference
Knowledge
+11.7 difference
Inst. Following
+35.6 difference
GLM-5
Ling 2.6 Flash
$0 / $0
$0.1 / $0.3
74 t/s
209.5 t/s
1.64s
1.07s
200K
262K
Pick GLM-5 if you want the stronger benchmark profile. Ling 2.6 Flash only becomes the better choice if you need the larger 262K context window.
GLM-5 is clearly ahead on the provisional aggregate, 77 to 44. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5's sharpest advantage is in coding, where it averages 63.2 against 27. The single biggest benchmark swing on the page is GPQA, 86% to 59%.
Ling 2.6 Flash is also the more expensive model on tokens at $0.10 input / $0.30 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for GLM-5. That is roughly Infinityx on output cost alone. Ling 2.6 Flash gives you the larger context window at 262K, compared with 200K for GLM-5.
GLM-5 is ahead on BenchLM's provisional leaderboard, 77 to 44. The biggest single separator in this matchup is GPQA, where the scores are 86% and 59%.
GLM-5 has the edge for knowledge tasks in this comparison, averaging 70.7 versus 59. Inside this category, GPQA is the benchmark that creates the most daylight between them.
GLM-5 has the edge for coding in this comparison, averaging 63.2 versus 27. Ling 2.6 Flash stays close enough that the answer can still flip depending on your workload.
GLM-5 has the edge for instruction following in this comparison, averaging 92.6 versus 57. Ling 2.6 Flash stays close enough that the answer can still flip depending on your workload.
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