Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GLM-5 has the cleaner overall profile here, landing at 64 versus 62. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
Composer 2 is also the more expensive model on tokens at $0.50 input / $2.50 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. Composer 2 is the reasoning model in the pair, while GLM-5 is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use.
Pick GLM-5 if you want the stronger benchmark profile. Composer 2 only becomes the better choice if agentic is the priority or you want the stronger reasoning-first profile.
Composer 2
61.7
GLM-5
58.4
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
Benchmark data for this category is coming soon.
Benchmark data for this category is coming soon.
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
Benchmark data for this category is coming soon.
Benchmark data for this category is coming soon.
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
GLM-5 is ahead overall, 64 to 62. The biggest single separator in this matchup is React Native Evals, where the scores are 97.2% and 74.8%.
Composer 2 has the edge for agentic tasks in this comparison, averaging 61.7 versus 58.4. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Get notified when new models drop, benchmark scores change, or the leaderboard shifts. One email per week.
Free. No spam. Unsubscribe anytime. We only store derived location metadata for consent routing.