Head-to-head showdown: GLM-5V-Turbo (Z.ai) ($1.20 in / $4.00 out per 1M) vs Databricks DBRX Instruct ($0.60 in / $1.80 out per 1M). Databricks DBRX Instruct is 2.2× cheaper across standard token mixes, with 200K vs 32.8K context windows.
glm-5v-turbo · zai
databricks/dbrx-instruct · databricks
Benchmark
| Workload Scenario | GLM-5V-Turbo (Z.ai) | Databricks DBRX Instruct | Price Delta |
|---|---|---|---|
| 1M input tokens (raw text) | $1.20 | $0.60 | +$0.60 |
| 1M output tokens (generation) | $4.00 | $1.80 | +$2.20 |
| 1M tokens · 70% input / 30% output mix | $2.04 | $0.96 | +$1.08 |
| Standard chat turn (4K in / 800 out, 50% cached) | $0.00608 | $0.00384 | +$0.00224 |
| Monthly scale (10K requests / day) | $1,824.00 | $1,152.00 | +$672.00 |
Negative difference = GLM-5V-Turbo (Z.ai) is cheaper. Positive = Databricks DBRX Instruct is cheaper.
Scaling Curve
Total cost of a token volume at 70/30 input/output split (uncached). Log-log scale.
Verdict
Databricks DBRX Instruct is cheaper on both input and output rates, so it costs less at every input/output mix. Price alone still isn't the whole decision: capability, latency and context limits (GLM-5V-Turbo (Z.ai): 200K, Databricks DBRX Instruct: 32.8K) may justify the premium for your task.
FAQ
On input, Databricks DBRX Instruct is cheaper ($0.60/M vs $1.20/M). On output, Databricks DBRX Instruct is cheaper ($1.80/M vs $4.00/M). The same model is cheaper on both sides, so it wins at every mix.
A chat-style request (4,000 input + 800 output tokens, 50% cached) costs $0.00608 on GLM-5V-Turbo (Z.ai) and $0.00384 on Databricks DBRX Instruct — Databricks DBRX Instruct is 1.6× more expensive for that workload.
GLM-5V-Turbo (Z.ai) supports 200,000 tokens (131.1K max output); Databricks DBRX Instruct supports 32,768 (4.1K max output). GLM-5V-Turbo (Z.ai) fits 6.1× more context, which matters for long documents and agents.