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Model comparison
Moonshot AI's Kimi K3 against Qwen's Qwen3.8 2.4T A95B: vendor list prices, limits, abilities and the Artificial Analysis indexes side by side - and what OpenSmartRoute does when both are available.
Qwen3.8 2.4T A95B lists at $3.00 per million tokens blended against $5.17 for Kimi K3 - 1.7x less.
Kimi K3 scores 44 on the intelligence index against 40 - 4 points apart.
Kimi K3 scores 76 on the coding index against 72 - 4 points apart.
Agentic index 50 against 50: within a point of each other.
only Kimi K3 offers image input.
Vendor list prices per million tokens and published limits; indexes are Artificial Analysis composites (0-100).
| Specification | Kimi K3 | Qwen3.8 2.4T A95B |
|---|---|---|
| Input price / 1M tokens | $0.23 | $2.00 |
| Output price / 1M tokens | $20.0 | $6.00 |
| Blended (3 in : 1 out) | $5.17 | $3.00 |
| Cached input / 1M | $0.22 | $0.25 |
| Context window | 1.0M | 1.0M |
| Max output | 944K | 909K |
| Input | text, image, video | text |
| Output | text | text |
| Tool calls | ||
| Structured output | ||
| Reasoning | ||
| Open weights | ||
| Intelligence index | 44 | 40 |
| Coding index | 76 | 72 |
| Agentic index | 50 | 50 |
| Released | Jul 16, 2026 | Aug 12, 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 | Kimi K3 | Qwen3.8 2.4T A95B | Difference |
|---|---|---|---|
| Support chat20M tokens in, 5M out a month - a help desk answering a few thousand conversations. | $105 | $70.00 | $34.62 less on Qwen3.8 2.4T A95B |
| Document summaries100M in, 5M out - long inputs, short answers. | $123 | $230 | $107 less on Kimi K3 |
| Coding assistant60M in, 20M out - a team's editor assistant, output-heavy. | $414 | $240 | $174 less on Qwen3.8 2.4T A95B |
Qwen3.8 2.4T A95B takes the short and general requests - at $3.00 blended it is the default - and Kimi K3 the ones whose complexity or quality floor rules the cheaper model out. The split is per request, not per app, and every decision carries the scores for both.
# targets.yaml - both models as routing targets (metadata.model links each to the catalogue entry)
targets:
- { id: qwen3.8-2.4t-a95b, kind: llm, metadata: { model: qwen/qwen3.8-2.4t-a95b } }
- { id: kimi-k3, kind: llm, metadata: { model: moonshotai/kimi-k3 } }
# 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: [qwen3.8-2.4t-a95b]
weight: 0.8
- name: hard-requests-escalate
when: { min_complexity: 0.45 }
prefer: [kimi-k3]
weight: 0.8Every Moonshot AI and Qwen model, with prices, is on the vendor pages; the rankings show what the router actually picks.