Side-by-side pricing, features and compliance for any tools in the directory.
Side-by-side comparison of GLM-5.2, Qwen3.8-Max: pricing, capabilities, integrations and compliance — from verified HokAI records.
Z.ai
Pick it if: GLM-5.2 fits engineering teams running autonomous coding agents over large monorepos who want to avoid API vendor lock-in. It scores 80.3% on GPQA Diamond, the highest published result for an open-weights model, and its MIT license suits self-hosted or fine-tuned deployments. Skip it for voice or vision workloads, which need a separate model.
Its edge: Tops open-source SWE-bench Pro at 62.1% as of June 2026, 3.7 points ahead of predecessor GLM-5.1.
The catch: No native image, audio, or video input — vision tasks require the separate GLM-5V-Turbo model.
Alibaba Cloud
Pick it if: Qwen3.8-Max launched GA on August 3, 2026 with 2.4 trillion total parameters (about 95 billion active) and a 1-million-token context window across a single flat pricing tier. It targets teams building large-context, multimodal agentic workloads on Alibaba Cloud who can tolerate its still-unpublished safety and training model card.
Its edge: Leads PaperBench at 93.0, ahead of GPT-5.6 Sol (90.5), Fable 5 (88.8) and Opus 4.8 (80.3).
The catch: No published safety or training model card as of the 2026-08-03 GA launch.
| Input / 1M tokens | $1.40 | $2 |
|---|---|---|
| Output / 1M tokens | $4.40 | $6 |
| Cached input / 1M | $0.26 | -- |
| Blended cost (3:1) | $2.15 | $3 |
| Summarise a 20-page PDF | $0.046 | $0.066 |
| Support reply | $0.0041 | $0.0058 |
| One coding agent run | $0.368 | $0.520 |
| Free tier | false | true |
| Strengths | Tops open-source SWE-bench Pro at 62.1% as of June 2026, 3.7 points ahead of predecessor GLM-5.1.; 1-million-token context window at $1.40/1M input — the largest context available in any MIT-licensed model.; MIT license and OpenAI-compatibl | Leads PaperBench at 93.0, ahead of GPT-5.6 Sol (90.5), Fable 5 (88.8) and Opus 4.8 (80.3).; 1-million-token context window (991.8K input non-thinking / 983.6K thinking) with a single flat pricing tier covering the full window.; $2.00 / $6.0 |
|---|---|---|
| Limitations | No native image, audio, or video input — vision tasks require the separate GLM-5V-Turbo model.; No disclosed SOC 2, HIPAA, or ISO 27001 certifications, limiting adoption in regulated industries.; 1M-context recall quality at depth has no pu | No published safety or training model card as of the 2026-08-03 GA launch.; Trails Fable 5 on Humanity's Last Exam (43.6 vs 53.3) and SWE-bench Pro (67.7 vs 80.0).; Open weights promised 'next week' at launch but not yet shipped; API-only f |
| Context window | 1M | 1M |
|---|---|---|
| Max output tokens | 131.1K | 131.1K |
| Input modalities | text; tool-calls | text; image; video |
| Output modalities | text; tool-calls | text |
| Capabilities | Tool use; Function calling; Structured output | Vision; Tool use; Video input |
| Reasoning modes | standard; extended-thinking-high; extended-thinking-max | standard; thinking (preserved reasoning) |
| Long-context recall | medium | unverified |
| Openness | open-source | proprietary |
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