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Model comparison
OpenAI's GPT-5.6 Terra against Z.ai's GLM 5.3 Flash: vendor list prices, limits, abilities and the Artificial Analysis indexes side by side - and what OpenSmartRoute does when both are available.
GLM 5.3 Flash lists at $0.237 per million tokens blended against $4.50 for GPT-5.6 Terra - 19x less.
Intelligence index 42 against 42: within a point of each other.
GPT-5.6 Terra scores 77 on the coding index against 72 - 5 points apart.
GLM 5.3 Flash scores 51 on the agentic index against 43 - 8 points apart.
GPT-5.6 Terra reads 1.1M tokens at once, GLM 5.3 Flash 1.0M.
only GLM 5.3 Flash offers open weights.
GLM 5.3 Flash was released about 2 months later.
Vendor list prices per million tokens and published limits; indexes are Artificial Analysis composites (0-100).
| Specification | GPT-5.6 Terra | GLM 5.3 Flash |
|---|---|---|
| Input price / 1M tokens | $2.00 | $0.15 |
| Output price / 1M tokens | $12.0 | $0.50 |
| Blended (3 in : 1 out) | $4.50 | $0.24 |
| Cached input / 1M | $0.20 | $0.03 |
| Context window | 1.1M | 1.0M |
| Max output | 128K | 944K |
| Input | file, image, text | text, image, video |
| Output | text | text |
| Tool calls | ||
| Structured output | ||
| Reasoning | ||
| Open weights | ||
| Intelligence index | 42 | 42 |
| Coding index | 77 | 72 |
| Agentic index | 43 | 51 |
| Released | Jul 9, 2026 | Aug 26, 2026 |
| Available on this deployment |
List price times tokens for three typical workloads; your mix of input and output decides which model's pricing suits you.
| Workload | GPT-5.6 Terra | GLM 5.3 Flash | Difference |
|---|---|---|---|
| Support chat20M tokens in, 5M out a month - a help desk answering a few thousand conversations. | $100 | $5.50 | $94.50 less on GLM 5.3 Flash |
| Document summaries100M in, 5M out - long inputs, short answers. | $260 | $17.50 | $243 less on GLM 5.3 Flash |
| Coding assistant60M in, 20M out - a team's editor assistant, output-heavy. | $360 | $19.00 | $341 less on GLM 5.3 Flash |
The two measure the same on the intelligence index, so the router treats them as peers and lets price, observed latency and your outcomes decide: GLM 5.3 Flash wins on cost, and a few bad outcomes on either move the next requests to the other.
# targets.yaml - both models as routing targets (metadata.model links each to the catalogue entry)
targets:
- { id: glm-5.3-flash, kind: llm, metadata: { model: z-ai/glm-5.3-flash } }
- { id: gpt-5.6-terra, kind: llm, metadata: { model: openai/gpt-5.6-terra } }
# rules.yaml - the cheaper model by default, the stronger one when the request is hard
rules:
- name: simple-requests-stay-cheap
when: { max_complexity: 0.45 }
prefer: [glm-5.3-flash]
weight: 0.8
- name: hard-requests-escalate
when: { min_complexity: 0.45 }
prefer: [gpt-5.6-terra]
weight: 0.8Every OpenAI and Z.ai model, with prices, is on the vendor pages; the rankings show what the router actually picks.