GLM 4.6 vs GLM 5

Pricing math

GLM 4.6 costs $0.43/M input and $1.74/M output from the provider; on OpenKey that's $0.43 × 1.03 = $0.4429/M input and $1.74 × 1.03 = $1.7922/M output. GLM 5 costs $0.60/M input and $1.92/M output from the provider, or $0.618/M input and $1.9776/M output on OpenKey. GLM 4.6's input price is 72% of GLM 5's (input_price_ratio: 0.72) — a real gap, but not a large absolute one at typical volumes.

For a 10M-input / 2M-output workload: GLM 4.6 runs $7.78 total, GLM 5 runs $9.84. That's a $2.06 difference for that workload — worth noting if you're running this at scale, but small next to the capability gap below.

Coding and agent benchmarks

Across the 11 Design Arena categories both models share, GLM 5 has a higher Elo in all 11. The gaps are largest in agentic categories: androidnative (1244 vs 1097, GLM 5 ranked #6 vs GLM 4.6's #20), mobileapps (1222 vs 1186, rank #10 vs #19), and fullstack (1190 vs 1098, rank #13 vs #22). In pure model-quality categories the gap is smaller but consistent: codecategories (1295 vs 1215), gamedev (1299 vs 1215), website (1290 vs 1217). GLM 4.6 has an artificial_analysis agentic_index of 17.7; no equivalent index is reported for GLM 5, so that comparison isn't available. GLM 5 also enables reasoning by default, which GLM 4.6 does not.

Context and parameters

Both models share the same 202,752-token context window — no advantage either way (context_ratio: 1.0). GLM 4.6 caps completions at 131,072 tokens; GLM 5 doesn't report a max_completion_tokens limit in this data, so don't assume it's unlimited, just undocumented here. GLM 5 supports two extra parameters GLM 4.6 doesn't: `logprobs` and `top_logprobs`, useful if you need token-level probability data for evaluation or guardrail pipelines. Otherwise the parameter sets are nearly identical (tool calling, structured outputs, reasoning toggles, penalties).

When to pick each

Pick GLM 5 by default for anything involving code generation, multi-step agent workflows, or app scaffolding — the Elo gap holds across every shared category, agentic and otherwise. Pick GLM 4.6 when you're running high-volume, less demanding tasks where the 28% lower input cost adds up, or when you don't need default-on reasoning and want a lighter-weight call. Both models are available on OpenKey through one API key, billed at the provider's list price plus a flat 3% fee.

Questions

Is GLM 5 worth the extra cost over GLM 4.6?

For coding and agent tasks, yes. GLM 5 wins all 11 shared Design Arena categories, with a 147-point Elo gap in androidnative (1244 vs 1097). The cost difference on a 10M-input/2M-output workload is $9.84 vs $7.78 — about $2.06 more, which is small relative to the capability gap.

Do GLM 4.6 and GLM 5 have the same context window?

Yes, both support 202,752 tokens of context (context_ratio: 1.0). GLM 4.6 caps output at 131,072 tokens; GLM 5's max output isn't documented in the catalog data, so check current provider docs before relying on a specific limit.

How much more expensive is GLM 5 per token?

GLM 5's input price is $0.618/M on OpenKey ($0.60 provider price × 1.03) versus GLM 4.6's $0.4429/M ($0.43 × 1.03) — GLM 4.6's input price is 72% of GLM 5's. Output is closer: $1.9776/M vs $1.7922/M.

Does GLM 5 use reasoning by default?

Yes — GLM 5 has default_enabled reasoning, while GLM 4.6's reasoning is optional and not mandatory. Both models support the `reasoning` parameter, but GLM 5 turns it on unless you explicitly disable it, which can affect latency and output token counts.