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Model comparison
Qwen's Qwen3.8 Max (0902) against Z.ai's GLM 5.3: vendor list prices, limits, abilities and the Artificial Analysis indexes side by side - and what OpenSmartRoute does when both are available.
GLM 5.3 lists at $0.409 per million tokens blended against $3.00 for Qwen3.8 Max (0902) - 7.3x less.
Intelligence index 45 against 45: within a point of each other.
Qwen3.8 Max (0902) scores 76 on the coding index against 75 - 1 points apart.
Qwen3.8 Max (0902) scores 56 on the agentic index against 53 - 3 points apart.
GLM 5.3 reads 1.0M tokens at once, Qwen3.8 Max (0902) 1M.
only Qwen3.8 Max (0902) offers image input; only GLM 5.3 offers open weights.
Vendor list prices per million tokens and published limits; indexes are Artificial Analysis composites (0-100).
| Specification | Qwen3.8 Max (0902) | GLM 5.3 |
|---|---|---|
| Input price / 1M tokens | $2.00 | $0.08 |
| Output price / 1M tokens | $6.00 | $1.40 |
| Blended (3 in : 1 out) | $3.00 | $0.41 |
| Cached input / 1M | $0.25 | $0.08 |
| Context window | 1M | 1.0M |
| Max output | 131K | 944K |
| Input | text, image, video | text |
| Output | text | text |
| Tool calls | ||
| Structured output | ||
| Reasoning | ||
| Open weights | ||
| Intelligence index | 45 | 45 |
| Coding index | 76 | 75 |
| Agentic index | 56 | 53 |
| Released | Sep 3, 2026 | Aug 18, 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 | Qwen3.8 Max (0902) | GLM 5.3 | Difference |
|---|---|---|---|
| Support chat20M tokens in, 5M out a month - a help desk answering a few thousand conversations. | $70.00 | $8.58 | $61.42 less on GLM 5.3 |
| Document summaries100M in, 5M out - long inputs, short answers. | $230 | $14.90 | $215 less on GLM 5.3 |
| Coding assistant60M in, 20M out - a team's editor assistant, output-heavy. | $240 | $32.74 | $207 less on GLM 5.3 |
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 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, kind: llm, metadata: { model: z-ai/glm-5.3 } }
- { id: qwen3.8-max-0902, kind: llm, metadata: { model: qwen/qwen3.8-max-0902 } }
# 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]
weight: 0.8
- name: hard-requests-escalate
when: { min_complexity: 0.45 }
prefer: [qwen3.8-max-0902]
weight: 0.8Every Qwen and Z.ai model, with prices, is on the vendor pages; the rankings show what the router actually picks.